code
stringlengths 161
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| apis
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"""Base object types."""
import pickle
import warnings
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.core.objects.base_node_mapping import (
DEFAULT_PERSIST_FNAME,
BaseObjectNodeMapping,
SimpleObjectNodeMapping,
)
from llama_index.core.schema import QueryType
from llama_index.core.storage.storage_context import (
DEFAULT_PERSIST_DIR,
StorageContext,
)
OT = TypeVar("OT")
class ObjectRetriever(ChainableMixin, Generic[OT]):
"""Object retriever."""
def __init__(
self, retriever: BaseRetriever, object_node_mapping: BaseObjectNodeMapping[OT]
):
self._retriever = retriever
self._object_node_mapping = object_node_mapping
@property
def retriever(self) -> BaseRetriever:
"""Retriever."""
return self._retriever
def retrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = self._retriever.retrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
async def aretrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = await self._retriever.aretrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
return ObjectRetrieverComponent(retriever=self)
class ObjectRetrieverComponent(QueryComponent):
"""Object retriever component."""
retriever: ObjectRetriever = Field(..., description="Retriever.")
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
self.retriever.retriever.callback_manager = callback_manager
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
# make sure input is a string
input["input"] = validate_and_convert_stringable(input["input"])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
output = self.retriever.retrieve(kwargs["input"])
return {"output": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component (async)."""
output = await self.retriever.aretrieve(kwargs["input"])
return {"output": output}
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys({"input"})
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"output"})
class ObjectIndex(Generic[OT]):
"""Object index."""
def __init__(
self, index: BaseIndex, object_node_mapping: BaseObjectNodeMapping
) -> None:
self._index = index
self._object_node_mapping = object_node_mapping
@classmethod
def from_objects(
cls,
objects: Sequence[OT],
object_mapping: Optional[BaseObjectNodeMapping] = None,
index_cls: Type[BaseIndex] = VectorStoreIndex,
**index_kwargs: Any,
) -> "ObjectIndex":
if object_mapping is None:
object_mapping = SimpleObjectNodeMapping.from_objects(objects)
nodes = object_mapping.to_nodes(objects)
index = index_cls(nodes, **index_kwargs)
return cls(index, object_mapping)
def insert_object(self, obj: Any) -> None:
self._object_node_mapping.add_object(obj)
node = self._object_node_mapping.to_node(obj)
self._index.insert_nodes([node])
def as_retriever(self, **kwargs: Any) -> ObjectRetriever:
return ObjectRetriever(
retriever=self._index.as_retriever(**kwargs),
object_node_mapping=self._object_node_mapping,
)
def as_node_retriever(self, **kwargs: Any) -> BaseRetriever:
return self._index.as_retriever(**kwargs)
def persist(
self,
persist_dir: str = DEFAULT_PERSIST_DIR,
obj_node_mapping_fname: str = DEFAULT_PERSIST_FNAME,
) -> None:
# try to persist object node mapping
try:
self._object_node_mapping.persist(
persist_dir=persist_dir, obj_node_mapping_fname=obj_node_mapping_fname
)
except (NotImplementedError, pickle.PickleError) as err:
warnings.warn(
(
"Unable to persist ObjectNodeMapping. You will need to "
"reconstruct the same object node mapping to build this ObjectIndex"
),
stacklevel=2,
)
self._index._storage_context.persist(persist_dir=persist_dir)
@classmethod
def from_persist_dir(
cls,
persist_dir: str = DEFAULT_PERSIST_DIR,
object_node_mapping: Optional[BaseObjectNodeMapping] = None,
) -> "ObjectIndex":
from llama_index.core.indices import load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
index = load_index_from_storage(storage_context)
if object_node_mapping:
return cls(index=index, object_node_mapping=object_node_mapping)
else:
# try to load object_node_mapping
# assume SimpleObjectNodeMapping for simplicity as its only subclass
# that supports this method
try:
object_node_mapping = SimpleObjectNodeMapping.from_persist_dir(
persist_dir=persist_dir
)
except Exception as err:
raise Exception(
"Unable to load from persist dir. The object_node_mapping cannot be loaded."
) from err
else:
return cls(index=index, object_node_mapping=object_node_mapping)
| [
"llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir",
"llama_index.core.base.query_pipeline.query.InputKeys.from_keys",
"llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.base.query_pipeline.query.OutputKeys.from_keys",
"llama_index.core.storage.storage_context.StorageContext.from_defaults",
"llama_index.core.base.query_pipeline.query.validate_and_convert_stringable",
"llama_index.core.indices.load_index_from_storage"
] | [((897, 910), 'typing.TypeVar', 'TypeVar', (['"""OT"""'], {}), "('OT')\n", (904, 910), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar\n'), ((2032, 2068), 'llama_index.core.bridge.pydantic.Field', 'Field', (['...'], {'description': '"""Retriever."""'}), "(..., description='Retriever.')\n", (2037, 2068), False, 'from llama_index.core.bridge.pydantic import Field\n'), ((2521, 2568), 'llama_index.core.base.query_pipeline.query.validate_and_convert_stringable', 'validate_and_convert_stringable', (["input['input']"], {}), "(input['input'])\n", (2552, 2568), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3055, 3085), 'llama_index.core.base.query_pipeline.query.InputKeys.from_keys', 'InputKeys.from_keys', (["{'input'}"], {}), "({'input'})\n", (3074, 3085), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3184, 3216), 'llama_index.core.base.query_pipeline.query.OutputKeys.from_keys', 'OutputKeys.from_keys', (["{'output'}"], {}), "({'output'})\n", (3204, 3216), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((5570, 5623), 'llama_index.core.storage.storage_context.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (5598, 5623), False, 'from llama_index.core.storage.storage_context import DEFAULT_PERSIST_DIR, StorageContext\n'), ((5640, 5680), 'llama_index.core.indices.load_index_from_storage', 'load_index_from_storage', (['storage_context'], {}), '(storage_context)\n', (5663, 5680), False, 'from llama_index.core.indices import load_index_from_storage\n'), ((3788, 3833), 'llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects', 'SimpleObjectNodeMapping.from_objects', (['objects'], {}), '(objects)\n', (3824, 3833), False, 'from llama_index.core.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n'), ((4944, 5105), 'warnings.warn', 'warnings.warn', (['"""Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex"""'], {'stacklevel': '(2)'}), "(\n 'Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex'\n , stacklevel=2)\n", (4957, 5105), False, 'import warnings\n'), ((6026, 6091), 'llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir', 'SimpleObjectNodeMapping.from_persist_dir', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (6066, 6091), False, 'from llama_index.core.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n')] |
"""Base object types."""
import pickle
import warnings
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.core.objects.base_node_mapping import (
DEFAULT_PERSIST_FNAME,
BaseObjectNodeMapping,
SimpleObjectNodeMapping,
)
from llama_index.core.schema import QueryType
from llama_index.core.storage.storage_context import (
DEFAULT_PERSIST_DIR,
StorageContext,
)
OT = TypeVar("OT")
class ObjectRetriever(ChainableMixin, Generic[OT]):
"""Object retriever."""
def __init__(
self, retriever: BaseRetriever, object_node_mapping: BaseObjectNodeMapping[OT]
):
self._retriever = retriever
self._object_node_mapping = object_node_mapping
@property
def retriever(self) -> BaseRetriever:
"""Retriever."""
return self._retriever
def retrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = self._retriever.retrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
async def aretrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = await self._retriever.aretrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
return ObjectRetrieverComponent(retriever=self)
class ObjectRetrieverComponent(QueryComponent):
"""Object retriever component."""
retriever: ObjectRetriever = Field(..., description="Retriever.")
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
self.retriever.retriever.callback_manager = callback_manager
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
# make sure input is a string
input["input"] = validate_and_convert_stringable(input["input"])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
output = self.retriever.retrieve(kwargs["input"])
return {"output": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component (async)."""
output = await self.retriever.aretrieve(kwargs["input"])
return {"output": output}
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys({"input"})
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"output"})
class ObjectIndex(Generic[OT]):
"""Object index."""
def __init__(
self, index: BaseIndex, object_node_mapping: BaseObjectNodeMapping
) -> None:
self._index = index
self._object_node_mapping = object_node_mapping
@classmethod
def from_objects(
cls,
objects: Sequence[OT],
object_mapping: Optional[BaseObjectNodeMapping] = None,
index_cls: Type[BaseIndex] = VectorStoreIndex,
**index_kwargs: Any,
) -> "ObjectIndex":
if object_mapping is None:
object_mapping = SimpleObjectNodeMapping.from_objects(objects)
nodes = object_mapping.to_nodes(objects)
index = index_cls(nodes, **index_kwargs)
return cls(index, object_mapping)
def insert_object(self, obj: Any) -> None:
self._object_node_mapping.add_object(obj)
node = self._object_node_mapping.to_node(obj)
self._index.insert_nodes([node])
def as_retriever(self, **kwargs: Any) -> ObjectRetriever:
return ObjectRetriever(
retriever=self._index.as_retriever(**kwargs),
object_node_mapping=self._object_node_mapping,
)
def as_node_retriever(self, **kwargs: Any) -> BaseRetriever:
return self._index.as_retriever(**kwargs)
def persist(
self,
persist_dir: str = DEFAULT_PERSIST_DIR,
obj_node_mapping_fname: str = DEFAULT_PERSIST_FNAME,
) -> None:
# try to persist object node mapping
try:
self._object_node_mapping.persist(
persist_dir=persist_dir, obj_node_mapping_fname=obj_node_mapping_fname
)
except (NotImplementedError, pickle.PickleError) as err:
warnings.warn(
(
"Unable to persist ObjectNodeMapping. You will need to "
"reconstruct the same object node mapping to build this ObjectIndex"
),
stacklevel=2,
)
self._index._storage_context.persist(persist_dir=persist_dir)
@classmethod
def from_persist_dir(
cls,
persist_dir: str = DEFAULT_PERSIST_DIR,
object_node_mapping: Optional[BaseObjectNodeMapping] = None,
) -> "ObjectIndex":
from llama_index.core.indices import load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
index = load_index_from_storage(storage_context)
if object_node_mapping:
return cls(index=index, object_node_mapping=object_node_mapping)
else:
# try to load object_node_mapping
# assume SimpleObjectNodeMapping for simplicity as its only subclass
# that supports this method
try:
object_node_mapping = SimpleObjectNodeMapping.from_persist_dir(
persist_dir=persist_dir
)
except Exception as err:
raise Exception(
"Unable to load from persist dir. The object_node_mapping cannot be loaded."
) from err
else:
return cls(index=index, object_node_mapping=object_node_mapping)
| [
"llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir",
"llama_index.core.base.query_pipeline.query.InputKeys.from_keys",
"llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.base.query_pipeline.query.OutputKeys.from_keys",
"llama_index.core.storage.storage_context.StorageContext.from_defaults",
"llama_index.core.base.query_pipeline.query.validate_and_convert_stringable",
"llama_index.core.indices.load_index_from_storage"
] | [((897, 910), 'typing.TypeVar', 'TypeVar', (['"""OT"""'], {}), "('OT')\n", (904, 910), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar\n'), ((2032, 2068), 'llama_index.core.bridge.pydantic.Field', 'Field', (['...'], {'description': '"""Retriever."""'}), "(..., description='Retriever.')\n", (2037, 2068), False, 'from llama_index.core.bridge.pydantic import Field\n'), ((2521, 2568), 'llama_index.core.base.query_pipeline.query.validate_and_convert_stringable', 'validate_and_convert_stringable', (["input['input']"], {}), "(input['input'])\n", (2552, 2568), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3055, 3085), 'llama_index.core.base.query_pipeline.query.InputKeys.from_keys', 'InputKeys.from_keys', (["{'input'}"], {}), "({'input'})\n", (3074, 3085), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3184, 3216), 'llama_index.core.base.query_pipeline.query.OutputKeys.from_keys', 'OutputKeys.from_keys', (["{'output'}"], {}), "({'output'})\n", (3204, 3216), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((5570, 5623), 'llama_index.core.storage.storage_context.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (5598, 5623), False, 'from llama_index.core.storage.storage_context import DEFAULT_PERSIST_DIR, StorageContext\n'), ((5640, 5680), 'llama_index.core.indices.load_index_from_storage', 'load_index_from_storage', (['storage_context'], {}), '(storage_context)\n', (5663, 5680), False, 'from llama_index.core.indices import load_index_from_storage\n'), ((3788, 3833), 'llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects', 'SimpleObjectNodeMapping.from_objects', (['objects'], {}), '(objects)\n', (3824, 3833), False, 'from llama_index.core.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n'), ((4944, 5105), 'warnings.warn', 'warnings.warn', (['"""Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex"""'], {'stacklevel': '(2)'}), "(\n 'Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex'\n , stacklevel=2)\n", (4957, 5105), False, 'import warnings\n'), ((6026, 6091), 'llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir', 'SimpleObjectNodeMapping.from_persist_dir', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (6066, 6091), False, 'from llama_index.core.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n')] |
"""Base object types."""
import pickle
import warnings
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.core.objects.base_node_mapping import (
DEFAULT_PERSIST_FNAME,
BaseObjectNodeMapping,
SimpleObjectNodeMapping,
)
from llama_index.core.schema import QueryType
from llama_index.core.storage.storage_context import (
DEFAULT_PERSIST_DIR,
StorageContext,
)
OT = TypeVar("OT")
class ObjectRetriever(ChainableMixin, Generic[OT]):
"""Object retriever."""
def __init__(
self, retriever: BaseRetriever, object_node_mapping: BaseObjectNodeMapping[OT]
):
self._retriever = retriever
self._object_node_mapping = object_node_mapping
@property
def retriever(self) -> BaseRetriever:
"""Retriever."""
return self._retriever
def retrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = self._retriever.retrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
async def aretrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = await self._retriever.aretrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
return ObjectRetrieverComponent(retriever=self)
class ObjectRetrieverComponent(QueryComponent):
"""Object retriever component."""
retriever: ObjectRetriever = Field(..., description="Retriever.")
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
self.retriever.retriever.callback_manager = callback_manager
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
# make sure input is a string
input["input"] = validate_and_convert_stringable(input["input"])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
output = self.retriever.retrieve(kwargs["input"])
return {"output": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component (async)."""
output = await self.retriever.aretrieve(kwargs["input"])
return {"output": output}
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys({"input"})
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"output"})
class ObjectIndex(Generic[OT]):
"""Object index."""
def __init__(
self, index: BaseIndex, object_node_mapping: BaseObjectNodeMapping
) -> None:
self._index = index
self._object_node_mapping = object_node_mapping
@classmethod
def from_objects(
cls,
objects: Sequence[OT],
object_mapping: Optional[BaseObjectNodeMapping] = None,
index_cls: Type[BaseIndex] = VectorStoreIndex,
**index_kwargs: Any,
) -> "ObjectIndex":
if object_mapping is None:
object_mapping = SimpleObjectNodeMapping.from_objects(objects)
nodes = object_mapping.to_nodes(objects)
index = index_cls(nodes, **index_kwargs)
return cls(index, object_mapping)
def insert_object(self, obj: Any) -> None:
self._object_node_mapping.add_object(obj)
node = self._object_node_mapping.to_node(obj)
self._index.insert_nodes([node])
def as_retriever(self, **kwargs: Any) -> ObjectRetriever:
return ObjectRetriever(
retriever=self._index.as_retriever(**kwargs),
object_node_mapping=self._object_node_mapping,
)
def as_node_retriever(self, **kwargs: Any) -> BaseRetriever:
return self._index.as_retriever(**kwargs)
def persist(
self,
persist_dir: str = DEFAULT_PERSIST_DIR,
obj_node_mapping_fname: str = DEFAULT_PERSIST_FNAME,
) -> None:
# try to persist object node mapping
try:
self._object_node_mapping.persist(
persist_dir=persist_dir, obj_node_mapping_fname=obj_node_mapping_fname
)
except (NotImplementedError, pickle.PickleError) as err:
warnings.warn(
(
"Unable to persist ObjectNodeMapping. You will need to "
"reconstruct the same object node mapping to build this ObjectIndex"
),
stacklevel=2,
)
self._index._storage_context.persist(persist_dir=persist_dir)
@classmethod
def from_persist_dir(
cls,
persist_dir: str = DEFAULT_PERSIST_DIR,
object_node_mapping: Optional[BaseObjectNodeMapping] = None,
) -> "ObjectIndex":
from llama_index.core.indices import load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
index = load_index_from_storage(storage_context)
if object_node_mapping:
return cls(index=index, object_node_mapping=object_node_mapping)
else:
# try to load object_node_mapping
# assume SimpleObjectNodeMapping for simplicity as its only subclass
# that supports this method
try:
object_node_mapping = SimpleObjectNodeMapping.from_persist_dir(
persist_dir=persist_dir
)
except Exception as err:
raise Exception(
"Unable to load from persist dir. The object_node_mapping cannot be loaded."
) from err
else:
return cls(index=index, object_node_mapping=object_node_mapping)
| [
"llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir",
"llama_index.core.base.query_pipeline.query.InputKeys.from_keys",
"llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.base.query_pipeline.query.OutputKeys.from_keys",
"llama_index.core.storage.storage_context.StorageContext.from_defaults",
"llama_index.core.base.query_pipeline.query.validate_and_convert_stringable",
"llama_index.core.indices.load_index_from_storage"
] | [((897, 910), 'typing.TypeVar', 'TypeVar', (['"""OT"""'], {}), "('OT')\n", (904, 910), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar\n'), ((2032, 2068), 'llama_index.core.bridge.pydantic.Field', 'Field', (['...'], {'description': '"""Retriever."""'}), "(..., description='Retriever.')\n", (2037, 2068), False, 'from llama_index.core.bridge.pydantic import Field\n'), ((2521, 2568), 'llama_index.core.base.query_pipeline.query.validate_and_convert_stringable', 'validate_and_convert_stringable', (["input['input']"], {}), "(input['input'])\n", (2552, 2568), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3055, 3085), 'llama_index.core.base.query_pipeline.query.InputKeys.from_keys', 'InputKeys.from_keys', (["{'input'}"], {}), "({'input'})\n", (3074, 3085), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3184, 3216), 'llama_index.core.base.query_pipeline.query.OutputKeys.from_keys', 'OutputKeys.from_keys', (["{'output'}"], {}), "({'output'})\n", (3204, 3216), False, 'from llama_index.core.base.query_pipeline.query import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((5570, 5623), 'llama_index.core.storage.storage_context.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (5598, 5623), False, 'from llama_index.core.storage.storage_context import DEFAULT_PERSIST_DIR, StorageContext\n'), ((5640, 5680), 'llama_index.core.indices.load_index_from_storage', 'load_index_from_storage', (['storage_context'], {}), '(storage_context)\n', (5663, 5680), False, 'from llama_index.core.indices import load_index_from_storage\n'), ((3788, 3833), 'llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects', 'SimpleObjectNodeMapping.from_objects', (['objects'], {}), '(objects)\n', (3824, 3833), False, 'from llama_index.core.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n'), ((4944, 5105), 'warnings.warn', 'warnings.warn', (['"""Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex"""'], {'stacklevel': '(2)'}), "(\n 'Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex'\n , stacklevel=2)\n", (4957, 5105), False, 'import warnings\n'), ((6026, 6091), 'llama_index.core.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir', 'SimpleObjectNodeMapping.from_persist_dir', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (6066, 6091), False, 'from llama_index.core.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n')] |
from typing import Any, Callable, Optional, Sequence
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.types import PydanticProgramMode
class MockLLM(CustomLLM):
max_tokens: Optional[int]
def __init__(
self,
max_tokens: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
) -> None:
super().__init__(
max_tokens=max_tokens,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
)
@classmethod
def class_name(cls) -> str:
return "MockLLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(num_output=self.max_tokens or -1)
def _generate_text(self, length: int) -> str:
return " ".join(["text" for _ in range(length)])
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
response_text = (
self._generate_text(self.max_tokens) if self.max_tokens else prompt
)
return CompletionResponse(
text=response_text,
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
def gen_prompt() -> CompletionResponseGen:
for ch in prompt:
yield CompletionResponse(
text=prompt,
delta=ch,
)
def gen_response(max_tokens: int) -> CompletionResponseGen:
for i in range(max_tokens):
response_text = self._generate_text(i)
yield CompletionResponse(
text=response_text,
delta="text ",
)
return gen_response(self.max_tokens) if self.max_tokens else gen_prompt()
| [
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.core.llms.types.CompletionResponse"
] | [((1537, 1562), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (1560, 1562), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((1876, 1901), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (1899, 1901), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((1377, 1422), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'num_output': '(self.max_tokens or -1)'}), '(num_output=self.max_tokens or -1)\n', (1388, 1422), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((1808, 1846), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'response_text'}), '(text=response_text)\n', (1826, 1846), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2128, 2169), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'prompt', 'delta': 'ch'}), '(text=prompt, delta=ch)\n', (2146, 2169), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2415, 2468), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'response_text', 'delta': '"""text """'}), "(text=response_text, delta='text ')\n", (2433, 2468), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n')] |
from typing import Any, Callable, Optional, Sequence
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.types import PydanticProgramMode
class MockLLM(CustomLLM):
max_tokens: Optional[int]
def __init__(
self,
max_tokens: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
) -> None:
super().__init__(
max_tokens=max_tokens,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
)
@classmethod
def class_name(cls) -> str:
return "MockLLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(num_output=self.max_tokens or -1)
def _generate_text(self, length: int) -> str:
return " ".join(["text" for _ in range(length)])
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
response_text = (
self._generate_text(self.max_tokens) if self.max_tokens else prompt
)
return CompletionResponse(
text=response_text,
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
def gen_prompt() -> CompletionResponseGen:
for ch in prompt:
yield CompletionResponse(
text=prompt,
delta=ch,
)
def gen_response(max_tokens: int) -> CompletionResponseGen:
for i in range(max_tokens):
response_text = self._generate_text(i)
yield CompletionResponse(
text=response_text,
delta="text ",
)
return gen_response(self.max_tokens) if self.max_tokens else gen_prompt()
| [
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.core.llms.types.CompletionResponse"
] | [((1537, 1562), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (1560, 1562), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((1876, 1901), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (1899, 1901), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((1377, 1422), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'num_output': '(self.max_tokens or -1)'}), '(num_output=self.max_tokens or -1)\n', (1388, 1422), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((1808, 1846), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'response_text'}), '(text=response_text)\n', (1826, 1846), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2128, 2169), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'prompt', 'delta': 'ch'}), '(text=prompt, delta=ch)\n', (2146, 2169), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2415, 2468), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'response_text', 'delta': '"""text """'}), "(text=response_text, delta='text ')\n", (2433, 2468), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset("MiniCovidQaDataset", "./data")
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=40, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 319), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""MiniCovidQaDataset"""', '"""./data"""'], {}), "('MiniCovidQaDataset', './data')\n", (287, 319), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((364, 416), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (395, 416), False, 'from llama_index.core import VectorStoreIndex\n'), ((509, 558), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack"""'], {}), "('RagEvaluatorPack', './pack')\n", (528, 558), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1409, 1433), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1431, 1433), False, 'import asyncio\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset("MiniCovidQaDataset", "./data")
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=40, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 319), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""MiniCovidQaDataset"""', '"""./data"""'], {}), "('MiniCovidQaDataset', './data')\n", (287, 319), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((364, 416), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (395, 416), False, 'from llama_index.core import VectorStoreIndex\n'), ((509, 558), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack"""'], {}), "('RagEvaluatorPack', './pack')\n", (528, 558), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1409, 1433), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1431, 1433), False, 'import asyncio\n')] |
"""Palm API."""
import os
from typing import Any, Callable, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
DEFAULT_PALM_MODEL = "models/text-bison-001"
class PaLM(CustomLLM):
"""PaLM LLM."""
model_name: str = Field(
default=DEFAULT_PALM_MODEL, description="The PaLM model to use."
)
num_output: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The number of tokens to generate.",
gt=0,
)
generate_kwargs: dict = Field(
default_factory=dict, description="Kwargs for generation."
)
_model: Any = PrivateAttr()
def __init__(
self,
api_key: Optional[str] = None,
model_name: Optional[str] = DEFAULT_PALM_MODEL,
num_output: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**generate_kwargs: Any,
) -> None:
"""Initialize params."""
try:
import google.generativeai as palm
except ImportError:
raise ValueError(
"PaLM is not installed. "
"Please install it with `pip install google-generativeai`."
)
api_key = api_key or os.environ.get("PALM_API_KEY")
palm.configure(api_key=api_key)
models = palm.list_models()
models_dict = {m.name: m for m in models}
if model_name not in models_dict:
raise ValueError(
f"Model name {model_name} not found in {models_dict.keys()}"
)
model_name = model_name
self._model = models_dict[model_name]
# get num_output
num_output = num_output or self._model.output_token_limit
generate_kwargs = generate_kwargs or {}
super().__init__(
model_name=model_name,
num_output=num_output,
generate_kwargs=generate_kwargs,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "PaLM_llm"
@property
def metadata(self) -> LLMMetadata:
"""Get LLM metadata."""
# TODO: google palm actually separates input and output token limits
total_tokens = self._model.input_token_limit + self.num_output
return LLMMetadata(
context_window=total_tokens,
num_output=self.num_output,
model_name=self.model_name,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Predict the answer to a query.
Args:
prompt (str): Prompt to use for prediction.
Returns:
Tuple[str, str]: Tuple of the predicted answer and the formatted prompt.
"""
import google.generativeai as palm
completion = palm.generate_text(
model=self.model_name,
prompt=prompt,
**kwargs,
)
return CompletionResponse(text=completion.result, raw=completion.candidates[0])
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Stream the answer to a query.
NOTE: this is a beta feature. Will try to build or use
better abstractions about response handling.
Args:
prompt (str): Prompt to use for prediction.
Returns:
str: The predicted answer.
"""
raise NotImplementedError(
"PaLM does not support streaming completion in LlamaIndex currently."
)
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr"
] | [((708, 779), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': 'DEFAULT_PALM_MODEL', 'description': '"""The PaLM model to use."""'}), "(default=DEFAULT_PALM_MODEL, description='The PaLM model to use.')\n", (713, 779), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((816, 910), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': 'DEFAULT_NUM_OUTPUTS', 'description': '"""The number of tokens to generate."""', 'gt': '(0)'}), "(default=DEFAULT_NUM_OUTPUTS, description=\n 'The number of tokens to generate.', gt=0)\n", (821, 910), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((965, 1030), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'dict', 'description': '"""Kwargs for generation."""'}), "(default_factory=dict, description='Kwargs for generation.')\n", (970, 1030), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1064, 1077), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (1075, 1077), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((3465, 3490), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (3488, 3490), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((4106, 4131), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (4129, 4131), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((2045, 2076), 'google.generativeai.configure', 'palm.configure', ([], {'api_key': 'api_key'}), '(api_key=api_key)\n', (2059, 2076), True, 'import google.generativeai as palm\n'), ((2095, 2113), 'google.generativeai.list_models', 'palm.list_models', ([], {}), '()\n', (2111, 2113), True, 'import google.generativeai as palm\n'), ((3315, 3415), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'context_window': 'total_tokens', 'num_output': 'self.num_output', 'model_name': 'self.model_name'}), '(context_window=total_tokens, num_output=self.num_output,\n model_name=self.model_name)\n', (3326, 3415), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((3898, 3964), 'google.generativeai.generate_text', 'palm.generate_text', ([], {'model': 'self.model_name', 'prompt': 'prompt'}), '(model=self.model_name, prompt=prompt, **kwargs)\n', (3916, 3964), True, 'import google.generativeai as palm\n'), ((4027, 4099), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'completion.result', 'raw': 'completion.candidates[0]'}), '(text=completion.result, raw=completion.candidates[0])\n', (4045, 4099), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2006, 2036), 'os.environ.get', 'os.environ.get', (['"""PALM_API_KEY"""'], {}), "('PALM_API_KEY')\n", (2020, 2036), False, 'import os\n')] |
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.ai21_utils import ai21_model_to_context_size
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.llms.generic_utils import (
completion_to_chat_decorator,
get_from_param_or_env,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class AI21(CustomLLM):
"""AI21 Labs LLM."""
model: str = Field(description="The AI21 model to use.")
maxTokens: int = Field(description="The maximum number of tokens to generate.")
temperature: float = Field(description="The temperature to use for sampling.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the anthropic API."
)
_api_key = PrivateAttr()
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = "j2-mid",
maxTokens: Optional[int] = 512,
temperature: Optional[float] = 0.1,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""Initialize params."""
try:
import ai21 as _ # noqa
except ImportError as e:
raise ImportError(
"You must install the `ai21` package to use AI21."
"Please `pip install ai21`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
api_key = get_from_param_or_env("api_key", api_key, "AI21_API_KEY")
self._api_key = api_key
super().__init__(
model=model,
maxTokens=maxTokens,
temperature=temperature,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(self) -> str:
"""Get Class Name."""
return "AI21_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=ai21_model_to_context_size(self.model),
num_output=self.maxTokens,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"maxTokens": self.maxTokens,
"temperature": self.temperature,
}
return {**base_kwargs, **self.additional_kwargs}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
import ai21
ai21.api_key = self._api_key
response = ai21.Completion.execute(**all_kwargs, prompt=prompt)
return CompletionResponse(
text=response["completions"][0]["data"]["text"], raw=response.__dict__
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError(
"AI21 does not currently support streaming completion."
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
chat_fn = completion_to_chat_decorator(self.complete)
return chat_fn(messages, **all_kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError("AI21 does not Currently Support Streaming Chat.")
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"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.generic_utils.get_from_param_or_env",
"llama_index.legacy.llms.ai21_utils.ai21_model_to_context_size",
"llama_index.legacy.callbacks.CallbackManager",
"llama_index.legacy.llms.generic_utils.completion_to_chat_decorator"
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import json
from typing import Any, Callable, Dict, List, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.legacy.llms.generic_utils import (
messages_to_prompt as generic_messages_to_prompt,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.vllm_utils import get_response, post_http_request
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class Vllm(LLM):
model: Optional[str] = Field(description="The HuggingFace Model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
tensor_parallel_size: Optional[int] = Field(
default=1,
description="The number of GPUs to use for distributed execution with tensor parallelism.",
)
trust_remote_code: Optional[bool] = Field(
default=True,
description="Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer.",
)
n: int = Field(
default=1,
description="Number of output sequences to return for the given prompt.",
)
best_of: Optional[int] = Field(
default=None,
description="Number of output sequences that are generated from the prompt.",
)
presence_penalty: float = Field(
default=0.0,
description="Float that penalizes new tokens based on whether they appear in the generated text so far.",
)
frequency_penalty: float = Field(
default=0.0,
description="Float that penalizes new tokens based on their frequency in the generated text so far.",
)
top_p: float = Field(
default=1.0,
description="Float that controls the cumulative probability of the top tokens to consider.",
)
top_k: int = Field(
default=-1,
description="Integer that controls the number of top tokens to consider.",
)
use_beam_search: bool = Field(
default=False, description="Whether to use beam search instead of sampling."
)
stop: Optional[List[str]] = Field(
default=None,
description="List of strings that stop the generation when they are generated.",
)
ignore_eos: bool = Field(
default=False,
description="Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.",
)
max_new_tokens: int = Field(
default=512,
description="Maximum number of tokens to generate per output sequence.",
)
logprobs: Optional[int] = Field(
default=None,
description="Number of log probabilities to return per output token.",
)
dtype: str = Field(
default="auto",
description="The data type for the model weights and activations.",
)
download_dir: Optional[str] = Field(
default=None,
description="Directory to download and load the weights. (Default to the default cache dir of huggingface)",
)
vllm_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Holds any model parameters valid for `vllm.LLM` call not explicitly specified.",
)
api_url: str = Field(description="The api url for vllm server")
_client: Any = PrivateAttr()
def __init__(
self,
model: str = "facebook/opt-125m",
temperature: float = 1.0,
tensor_parallel_size: int = 1,
trust_remote_code: bool = True,
n: int = 1,
best_of: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
top_p: float = 1.0,
top_k: int = -1,
use_beam_search: bool = False,
stop: Optional[List[str]] = None,
ignore_eos: bool = False,
max_new_tokens: int = 512,
logprobs: Optional[int] = None,
dtype: str = "auto",
download_dir: Optional[str] = None,
vllm_kwargs: Dict[str, Any] = {},
api_url: Optional[str] = "",
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from vllm import LLM as VLLModel
except ImportError:
raise ImportError(
"Could not import vllm python package. "
"Please install it with `pip install vllm`."
)
if model != "":
self._client = VLLModel(
model=model,
tensor_parallel_size=tensor_parallel_size,
trust_remote_code=trust_remote_code,
dtype=dtype,
download_dir=download_dir,
**vllm_kwargs
)
else:
self._client = None
callback_manager = callback_manager or CallbackManager([])
super().__init__(
model=model,
temperature=temperature,
n=n,
best_of=best_of,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
top_p=top_p,
top_k=top_k,
use_beam_search=use_beam_search,
stop=stop,
ignore_eos=ignore_eos,
max_new_tokens=max_new_tokens,
logprobs=logprobs,
dtype=dtype,
download_dir=download_dir,
vllm_kwargs=vllm_kwargs,
api_url=api_url,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "Vllm"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(model_name=self.model)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"max_tokens": self.max_new_tokens,
"n": self.n,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"use_beam_search": self.use_beam_search,
"best_of": self.best_of,
"ignore_eos": self.ignore_eos,
"stop": self.stop,
"logprobs": self.logprobs,
"top_k": self.top_k,
"top_p": self.top_p,
"stop": self.stop,
}
return {**base_kwargs}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
kwargs = kwargs if kwargs else {}
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params)
outputs = self._client.generate([prompt], sampling_params)
return CompletionResponse(text=outputs[0].outputs[0].text)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise (ValueError("Not Implemented"))
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise (ValueError("Not Implemented"))
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
kwargs = kwargs if kwargs else {}
return self.chat(messages, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise (ValueError("Not Implemented"))
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise (ValueError("Not Implemented"))
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise (ValueError("Not Implemented"))
class VllmServer(Vllm):
def __init__(
self,
model: str = "facebook/opt-125m",
api_url: str = "http://localhost:8000",
temperature: float = 1.0,
tensor_parallel_size: Optional[int] = 1,
trust_remote_code: Optional[bool] = True,
n: int = 1,
best_of: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
top_p: float = 1.0,
top_k: int = -1,
use_beam_search: bool = False,
stop: Optional[List[str]] = None,
ignore_eos: bool = False,
max_new_tokens: int = 512,
logprobs: Optional[int] = None,
dtype: str = "auto",
download_dir: Optional[str] = None,
messages_to_prompt: Optional[Callable] = None,
completion_to_prompt: Optional[Callable] = None,
vllm_kwargs: Dict[str, Any] = {},
callback_manager: Optional[CallbackManager] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
self._client = None
messages_to_prompt = messages_to_prompt or generic_messages_to_prompt
completion_to_prompt = completion_to_prompt or (lambda x: x)
callback_manager = callback_manager or CallbackManager([])
model = ""
super().__init__(
model=model,
temperature=temperature,
n=n,
best_of=best_of,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
top_p=top_p,
top_k=top_k,
use_beam_search=use_beam_search,
stop=stop,
ignore_eos=ignore_eos,
max_new_tokens=max_new_tokens,
logprobs=logprobs,
dtype=dtype,
download_dir=download_dir,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
vllm_kwargs=vllm_kwargs,
api_url=api_url,
callback_manager=callback_manager,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "VllmServer"
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> List[CompletionResponse]:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
response = post_http_request(self.api_url, sampling_params, stream=False)
output = get_response(response)
return CompletionResponse(text=output[0])
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
response = post_http_request(self.api_url, sampling_params, stream=True)
def gen() -> CompletionResponseGen:
for chunk in response.iter_lines(
chunk_size=8192, decode_unicode=False, delimiter=b"\0"
):
if chunk:
data = json.loads(chunk.decode("utf-8"))
yield CompletionResponse(text=data["text"][0])
return gen()
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
return self.complete(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
async def gen() -> CompletionResponseAsyncGen:
for message in self.stream_complete(prompt, **kwargs):
yield message
return gen()
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
return self.stream_chat(messages, **kwargs)
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import json
from typing import Any, Callable, Dict, List, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.legacy.llms.generic_utils import (
messages_to_prompt as generic_messages_to_prompt,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.vllm_utils import get_response, post_http_request
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class Vllm(LLM):
model: Optional[str] = Field(description="The HuggingFace Model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
tensor_parallel_size: Optional[int] = Field(
default=1,
description="The number of GPUs to use for distributed execution with tensor parallelism.",
)
trust_remote_code: Optional[bool] = Field(
default=True,
description="Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer.",
)
n: int = Field(
default=1,
description="Number of output sequences to return for the given prompt.",
)
best_of: Optional[int] = Field(
default=None,
description="Number of output sequences that are generated from the prompt.",
)
presence_penalty: float = Field(
default=0.0,
description="Float that penalizes new tokens based on whether they appear in the generated text so far.",
)
frequency_penalty: float = Field(
default=0.0,
description="Float that penalizes new tokens based on their frequency in the generated text so far.",
)
top_p: float = Field(
default=1.0,
description="Float that controls the cumulative probability of the top tokens to consider.",
)
top_k: int = Field(
default=-1,
description="Integer that controls the number of top tokens to consider.",
)
use_beam_search: bool = Field(
default=False, description="Whether to use beam search instead of sampling."
)
stop: Optional[List[str]] = Field(
default=None,
description="List of strings that stop the generation when they are generated.",
)
ignore_eos: bool = Field(
default=False,
description="Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.",
)
max_new_tokens: int = Field(
default=512,
description="Maximum number of tokens to generate per output sequence.",
)
logprobs: Optional[int] = Field(
default=None,
description="Number of log probabilities to return per output token.",
)
dtype: str = Field(
default="auto",
description="The data type for the model weights and activations.",
)
download_dir: Optional[str] = Field(
default=None,
description="Directory to download and load the weights. (Default to the default cache dir of huggingface)",
)
vllm_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Holds any model parameters valid for `vllm.LLM` call not explicitly specified.",
)
api_url: str = Field(description="The api url for vllm server")
_client: Any = PrivateAttr()
def __init__(
self,
model: str = "facebook/opt-125m",
temperature: float = 1.0,
tensor_parallel_size: int = 1,
trust_remote_code: bool = True,
n: int = 1,
best_of: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
top_p: float = 1.0,
top_k: int = -1,
use_beam_search: bool = False,
stop: Optional[List[str]] = None,
ignore_eos: bool = False,
max_new_tokens: int = 512,
logprobs: Optional[int] = None,
dtype: str = "auto",
download_dir: Optional[str] = None,
vllm_kwargs: Dict[str, Any] = {},
api_url: Optional[str] = "",
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from vllm import LLM as VLLModel
except ImportError:
raise ImportError(
"Could not import vllm python package. "
"Please install it with `pip install vllm`."
)
if model != "":
self._client = VLLModel(
model=model,
tensor_parallel_size=tensor_parallel_size,
trust_remote_code=trust_remote_code,
dtype=dtype,
download_dir=download_dir,
**vllm_kwargs
)
else:
self._client = None
callback_manager = callback_manager or CallbackManager([])
super().__init__(
model=model,
temperature=temperature,
n=n,
best_of=best_of,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
top_p=top_p,
top_k=top_k,
use_beam_search=use_beam_search,
stop=stop,
ignore_eos=ignore_eos,
max_new_tokens=max_new_tokens,
logprobs=logprobs,
dtype=dtype,
download_dir=download_dir,
vllm_kwargs=vllm_kwargs,
api_url=api_url,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "Vllm"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(model_name=self.model)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"max_tokens": self.max_new_tokens,
"n": self.n,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"use_beam_search": self.use_beam_search,
"best_of": self.best_of,
"ignore_eos": self.ignore_eos,
"stop": self.stop,
"logprobs": self.logprobs,
"top_k": self.top_k,
"top_p": self.top_p,
"stop": self.stop,
}
return {**base_kwargs}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
kwargs = kwargs if kwargs else {}
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params)
outputs = self._client.generate([prompt], sampling_params)
return CompletionResponse(text=outputs[0].outputs[0].text)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise (ValueError("Not Implemented"))
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise (ValueError("Not Implemented"))
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
kwargs = kwargs if kwargs else {}
return self.chat(messages, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise (ValueError("Not Implemented"))
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise (ValueError("Not Implemented"))
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise (ValueError("Not Implemented"))
class VllmServer(Vllm):
def __init__(
self,
model: str = "facebook/opt-125m",
api_url: str = "http://localhost:8000",
temperature: float = 1.0,
tensor_parallel_size: Optional[int] = 1,
trust_remote_code: Optional[bool] = True,
n: int = 1,
best_of: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
top_p: float = 1.0,
top_k: int = -1,
use_beam_search: bool = False,
stop: Optional[List[str]] = None,
ignore_eos: bool = False,
max_new_tokens: int = 512,
logprobs: Optional[int] = None,
dtype: str = "auto",
download_dir: Optional[str] = None,
messages_to_prompt: Optional[Callable] = None,
completion_to_prompt: Optional[Callable] = None,
vllm_kwargs: Dict[str, Any] = {},
callback_manager: Optional[CallbackManager] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
self._client = None
messages_to_prompt = messages_to_prompt or generic_messages_to_prompt
completion_to_prompt = completion_to_prompt or (lambda x: x)
callback_manager = callback_manager or CallbackManager([])
model = ""
super().__init__(
model=model,
temperature=temperature,
n=n,
best_of=best_of,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
top_p=top_p,
top_k=top_k,
use_beam_search=use_beam_search,
stop=stop,
ignore_eos=ignore_eos,
max_new_tokens=max_new_tokens,
logprobs=logprobs,
dtype=dtype,
download_dir=download_dir,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
vllm_kwargs=vllm_kwargs,
api_url=api_url,
callback_manager=callback_manager,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "VllmServer"
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> List[CompletionResponse]:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
response = post_http_request(self.api_url, sampling_params, stream=False)
output = get_response(response)
return CompletionResponse(text=output[0])
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
response = post_http_request(self.api_url, sampling_params, stream=True)
def gen() -> CompletionResponseGen:
for chunk in response.iter_lines(
chunk_size=8192, decode_unicode=False, delimiter=b"\0"
):
if chunk:
data = json.loads(chunk.decode("utf-8"))
yield CompletionResponse(text=data["text"][0])
return gen()
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
return self.complete(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
async def gen() -> CompletionResponseAsyncGen:
for message in self.stream_complete(prompt, **kwargs):
yield message
return gen()
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
return self.stream_chat(messages, **kwargs)
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.llms.vllm_utils.post_http_request",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.llms.generic_utils.stream_completion_response_to_chat_response",
"llama_index.legacy.llms.generic_utils.completion_response_to_chat_response",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.vllm_utils.get_response",
"llama_index.legacy.callbacks.CallbackManager"
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"""Prompts."""
from abc import ABC, abstractmethod
from copy import deepcopy
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Union,
)
from llama_index.core.bridge.pydantic import Field
if TYPE_CHECKING:
from llama_index.core.bridge.langchain import (
BasePromptTemplate as LangchainTemplate,
)
# pants: no-infer-dep
from llama_index.core.bridge.langchain import (
ConditionalPromptSelector as LangchainSelector,
)
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.generic_utils import (
messages_to_prompt as default_messages_to_prompt,
)
from llama_index.core.base.llms.generic_utils import (
prompt_to_messages,
)
from llama_index.core.prompts.prompt_type import PromptType
from llama_index.core.prompts.utils import get_template_vars
from llama_index.core.types import BaseOutputParser
class BasePromptTemplate(ChainableMixin, BaseModel, ABC):
metadata: Dict[str, Any]
template_vars: List[str]
kwargs: Dict[str, str]
output_parser: Optional[BaseOutputParser]
template_var_mappings: Optional[Dict[str, Any]] = Field(
default_factory=dict, description="Template variable mappings (Optional)."
)
function_mappings: Optional[Dict[str, Callable]] = Field(
default_factory=dict,
description=(
"Function mappings (Optional). This is a mapping from template "
"variable names to functions that take in the current kwargs and "
"return a string."
),
)
def _map_template_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""For keys in template_var_mappings, swap in the right keys."""
template_var_mappings = self.template_var_mappings or {}
return {template_var_mappings.get(k, k): v for k, v in kwargs.items()}
def _map_function_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""For keys in function_mappings, compute values and combine w/ kwargs.
Users can pass in functions instead of fixed values as format variables.
For each function, we call the function with the current kwargs,
get back the value, and then use that value in the template
for the corresponding format variable.
"""
function_mappings = self.function_mappings or {}
# first generate the values for the functions
new_kwargs = {}
for k, v in function_mappings.items():
# TODO: figure out what variables to pass into each function
# is it the kwargs specified during query time? just the fixed kwargs?
# all kwargs?
new_kwargs[k] = v(**kwargs)
# then, add the fixed variables only if not in new_kwargs already
# (implying that function mapping will override fixed variables)
for k, v in kwargs.items():
if k not in new_kwargs:
new_kwargs[k] = v
return new_kwargs
def _map_all_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""Map both template and function variables.
We (1) first call function mappings to compute functions,
and then (2) call the template_var_mappings.
"""
# map function
new_kwargs = self._map_function_vars(kwargs)
# map template vars (to point to existing format vars in string template)
return self._map_template_vars(new_kwargs)
class Config:
arbitrary_types_allowed = True
@abstractmethod
def partial_format(self, **kwargs: Any) -> "BasePromptTemplate":
...
@abstractmethod
def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
...
@abstractmethod
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
...
@abstractmethod
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
...
def _as_query_component(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> QueryComponent:
"""As query component."""
return PromptComponent(prompt=self, format_messages=False, llm=llm)
class PromptTemplate(BasePromptTemplate):
template: str
def __init__(
self,
template: str,
prompt_type: str = PromptType.CUSTOM,
output_parser: Optional[BaseOutputParser] = None,
metadata: Optional[Dict[str, Any]] = None,
template_var_mappings: Optional[Dict[str, Any]] = None,
function_mappings: Optional[Dict[str, Callable]] = None,
**kwargs: Any,
) -> None:
if metadata is None:
metadata = {}
metadata["prompt_type"] = prompt_type
template_vars = get_template_vars(template)
super().__init__(
template=template,
template_vars=template_vars,
kwargs=kwargs,
metadata=metadata,
output_parser=output_parser,
template_var_mappings=template_var_mappings,
function_mappings=function_mappings,
)
def partial_format(self, **kwargs: Any) -> "PromptTemplate":
"""Partially format the prompt."""
# NOTE: this is a hack to get around deepcopy failing on output parser
output_parser = self.output_parser
self.output_parser = None
# get function and fixed kwargs, and add that to a copy
# of the current prompt object
prompt = deepcopy(self)
prompt.kwargs.update(kwargs)
# NOTE: put the output parser back
prompt.output_parser = output_parser
self.output_parser = output_parser
return prompt
def format(
self,
llm: Optional[BaseLLM] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
**kwargs: Any,
) -> str:
"""Format the prompt into a string."""
del llm # unused
all_kwargs = {
**self.kwargs,
**kwargs,
}
mapped_all_kwargs = self._map_all_vars(all_kwargs)
prompt = self.template.format(**mapped_all_kwargs)
if self.output_parser is not None:
prompt = self.output_parser.format(prompt)
if completion_to_prompt is not None:
prompt = completion_to_prompt(prompt)
return prompt
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Format the prompt into a list of chat messages."""
del llm # unused
prompt = self.format(**kwargs)
return prompt_to_messages(prompt)
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
return self.template
class ChatPromptTemplate(BasePromptTemplate):
message_templates: List[ChatMessage]
def __init__(
self,
message_templates: List[ChatMessage],
prompt_type: str = PromptType.CUSTOM,
output_parser: Optional[BaseOutputParser] = None,
metadata: Optional[Dict[str, Any]] = None,
template_var_mappings: Optional[Dict[str, Any]] = None,
function_mappings: Optional[Dict[str, Callable]] = None,
**kwargs: Any,
):
if metadata is None:
metadata = {}
metadata["prompt_type"] = prompt_type
template_vars = []
for message_template in message_templates:
template_vars.extend(get_template_vars(message_template.content or ""))
super().__init__(
message_templates=message_templates,
kwargs=kwargs,
metadata=metadata,
output_parser=output_parser,
template_vars=template_vars,
template_var_mappings=template_var_mappings,
function_mappings=function_mappings,
)
@classmethod
def from_messages(
cls,
message_templates: Union[List[Tuple[str, str]], List[ChatMessage]],
**kwargs: Any,
) -> "ChatPromptTemplate":
"""From messages."""
if isinstance(message_templates[0], tuple):
message_templates = [
ChatMessage.from_str(role=role, content=content)
for role, content in message_templates
]
return cls(message_templates=message_templates, **kwargs)
def partial_format(self, **kwargs: Any) -> "ChatPromptTemplate":
prompt = deepcopy(self)
prompt.kwargs.update(kwargs)
return prompt
def format(
self,
llm: Optional[BaseLLM] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
**kwargs: Any,
) -> str:
del llm # unused
messages = self.format_messages(**kwargs)
if messages_to_prompt is not None:
return messages_to_prompt(messages)
return default_messages_to_prompt(messages)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
del llm # unused
"""Format the prompt into a list of chat messages."""
all_kwargs = {
**self.kwargs,
**kwargs,
}
mapped_all_kwargs = self._map_all_vars(all_kwargs)
messages: List[ChatMessage] = []
for message_template in self.message_templates:
template_vars = get_template_vars(message_template.content or "")
relevant_kwargs = {
k: v for k, v in mapped_all_kwargs.items() if k in template_vars
}
content_template = message_template.content or ""
# if there's mappings specified, make sure those are used
content = content_template.format(**relevant_kwargs)
message: ChatMessage = message_template.copy()
message.content = content
messages.append(message)
if self.output_parser is not None:
messages = self.output_parser.format_messages(messages)
return messages
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
return default_messages_to_prompt(self.message_templates)
def _as_query_component(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> QueryComponent:
"""As query component."""
return PromptComponent(prompt=self, format_messages=True, llm=llm)
class SelectorPromptTemplate(BasePromptTemplate):
default_template: BasePromptTemplate
conditionals: Optional[
List[Tuple[Callable[[BaseLLM], bool], BasePromptTemplate]]
] = None
def __init__(
self,
default_template: BasePromptTemplate,
conditionals: Optional[
List[Tuple[Callable[[BaseLLM], bool], BasePromptTemplate]]
] = None,
):
metadata = default_template.metadata
kwargs = default_template.kwargs
template_vars = default_template.template_vars
output_parser = default_template.output_parser
super().__init__(
default_template=default_template,
conditionals=conditionals,
metadata=metadata,
kwargs=kwargs,
template_vars=template_vars,
output_parser=output_parser,
)
def select(self, llm: Optional[BaseLLM] = None) -> BasePromptTemplate:
# ensure output parser is up to date
self.default_template.output_parser = self.output_parser
if llm is None:
return self.default_template
if self.conditionals is not None:
for condition, prompt in self.conditionals:
if condition(llm):
# ensure output parser is up to date
prompt.output_parser = self.output_parser
return prompt
return self.default_template
def partial_format(self, **kwargs: Any) -> "SelectorPromptTemplate":
default_template = self.default_template.partial_format(**kwargs)
if self.conditionals is None:
conditionals = None
else:
conditionals = [
(condition, prompt.partial_format(**kwargs))
for condition, prompt in self.conditionals
]
return SelectorPromptTemplate(
default_template=default_template, conditionals=conditionals
)
def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
"""Format the prompt into a string."""
prompt = self.select(llm=llm)
return prompt.format(**kwargs)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Format the prompt into a list of chat messages."""
prompt = self.select(llm=llm)
return prompt.format_messages(**kwargs)
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
prompt = self.select(llm=llm)
return prompt.get_template(llm=llm)
class LangchainPromptTemplate(BasePromptTemplate):
selector: Any
requires_langchain_llm: bool = False
def __init__(
self,
template: Optional["LangchainTemplate"] = None,
selector: Optional["LangchainSelector"] = None,
output_parser: Optional[BaseOutputParser] = None,
prompt_type: str = PromptType.CUSTOM,
metadata: Optional[Dict[str, Any]] = None,
template_var_mappings: Optional[Dict[str, Any]] = None,
function_mappings: Optional[Dict[str, Callable]] = None,
requires_langchain_llm: bool = False,
) -> None:
try:
from llama_index.core.bridge.langchain import (
ConditionalPromptSelector as LangchainSelector,
)
except ImportError:
raise ImportError(
"Must install `llama_index[langchain]` to use LangchainPromptTemplate."
)
if selector is None:
if template is None:
raise ValueError("Must provide either template or selector.")
selector = LangchainSelector(default_prompt=template)
else:
if template is not None:
raise ValueError("Must provide either template or selector.")
selector = selector
kwargs = selector.default_prompt.partial_variables
template_vars = selector.default_prompt.input_variables
if metadata is None:
metadata = {}
metadata["prompt_type"] = prompt_type
super().__init__(
selector=selector,
metadata=metadata,
kwargs=kwargs,
template_vars=template_vars,
output_parser=output_parser,
template_var_mappings=template_var_mappings,
function_mappings=function_mappings,
requires_langchain_llm=requires_langchain_llm,
)
def partial_format(self, **kwargs: Any) -> "BasePromptTemplate":
"""Partially format the prompt."""
from llama_index.core.bridge.langchain import (
ConditionalPromptSelector as LangchainSelector,
)
mapped_kwargs = self._map_all_vars(kwargs)
default_prompt = self.selector.default_prompt.partial(**mapped_kwargs)
conditionals = [
(condition, prompt.partial(**mapped_kwargs))
for condition, prompt in self.selector.conditionals
]
lc_selector = LangchainSelector(
default_prompt=default_prompt, conditionals=conditionals
)
# copy full prompt object, replace selector
lc_prompt = deepcopy(self)
lc_prompt.selector = lc_selector
return lc_prompt
def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
"""Format the prompt into a string."""
from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep
if llm is not None:
# if llamaindex LLM is provided, and we require a langchain LLM,
# then error. but otherwise if `requires_langchain_llm` is False,
# then we can just use the default prompt
if not isinstance(llm, LangChainLLM) and self.requires_langchain_llm:
raise ValueError("Must provide a LangChainLLM.")
elif not isinstance(llm, LangChainLLM):
lc_template = self.selector.default_prompt
else:
lc_template = self.selector.get_prompt(llm=llm.llm)
else:
lc_template = self.selector.default_prompt
# if there's mappings specified, make sure those are used
mapped_kwargs = self._map_all_vars(kwargs)
return lc_template.format(**mapped_kwargs)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Format the prompt into a list of chat messages."""
from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep
from llama_index.llms.langchain.utils import (
from_lc_messages,
) # pants: no-infer-dep
if llm is not None:
# if llamaindex LLM is provided, and we require a langchain LLM,
# then error. but otherwise if `requires_langchain_llm` is False,
# then we can just use the default prompt
if not isinstance(llm, LangChainLLM) and self.requires_langchain_llm:
raise ValueError("Must provide a LangChainLLM.")
elif not isinstance(llm, LangChainLLM):
lc_template = self.selector.default_prompt
else:
lc_template = self.selector.get_prompt(llm=llm.llm)
else:
lc_template = self.selector.default_prompt
# if there's mappings specified, make sure those are used
mapped_kwargs = self._map_all_vars(kwargs)
lc_prompt_value = lc_template.format_prompt(**mapped_kwargs)
lc_messages = lc_prompt_value.to_messages()
return from_lc_messages(lc_messages)
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep
if llm is not None:
# if llamaindex LLM is provided, and we require a langchain LLM,
# then error. but otherwise if `requires_langchain_llm` is False,
# then we can just use the default prompt
if not isinstance(llm, LangChainLLM) and self.requires_langchain_llm:
raise ValueError("Must provide a LangChainLLM.")
elif not isinstance(llm, LangChainLLM):
lc_template = self.selector.default_prompt
else:
lc_template = self.selector.get_prompt(llm=llm.llm)
else:
lc_template = self.selector.default_prompt
try:
return str(lc_template.template) # type: ignore
except AttributeError:
return str(lc_template)
# NOTE: only for backwards compatibility
Prompt = PromptTemplate
class PromptComponent(QueryComponent):
"""Prompt component."""
prompt: BasePromptTemplate = Field(..., description="Prompt")
llm: Optional[BaseLLM] = Field(
default=None, description="LLM to use for formatting prompt."
)
format_messages: bool = Field(
default=False,
description="Whether to format the prompt into a list of chat messages.",
)
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: Any) -> None:
"""Set callback manager."""
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
keys = list(input.keys())
for k in keys:
input[k] = validate_and_convert_stringable(input[k])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
if self.format_messages:
output: Union[str, List[ChatMessage]] = self.prompt.format_messages(
llm=self.llm, **kwargs
)
else:
output = self.prompt.format(llm=self.llm, **kwargs)
return {"prompt": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component."""
# NOTE: no native async for prompt
return self._run_component(**kwargs)
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys(
set(self.prompt.template_vars) - set(self.prompt.kwargs)
)
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"prompt"})
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"""Prompts."""
from abc import ABC, abstractmethod
from copy import deepcopy
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Union,
)
from llama_index.core.bridge.pydantic import Field
if TYPE_CHECKING:
from llama_index.core.bridge.langchain import (
BasePromptTemplate as LangchainTemplate,
)
# pants: no-infer-dep
from llama_index.core.bridge.langchain import (
ConditionalPromptSelector as LangchainSelector,
)
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.generic_utils import (
messages_to_prompt as default_messages_to_prompt,
)
from llama_index.core.base.llms.generic_utils import (
prompt_to_messages,
)
from llama_index.core.prompts.prompt_type import PromptType
from llama_index.core.prompts.utils import get_template_vars
from llama_index.core.types import BaseOutputParser
class BasePromptTemplate(ChainableMixin, BaseModel, ABC):
metadata: Dict[str, Any]
template_vars: List[str]
kwargs: Dict[str, str]
output_parser: Optional[BaseOutputParser]
template_var_mappings: Optional[Dict[str, Any]] = Field(
default_factory=dict, description="Template variable mappings (Optional)."
)
function_mappings: Optional[Dict[str, Callable]] = Field(
default_factory=dict,
description=(
"Function mappings (Optional). This is a mapping from template "
"variable names to functions that take in the current kwargs and "
"return a string."
),
)
def _map_template_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""For keys in template_var_mappings, swap in the right keys."""
template_var_mappings = self.template_var_mappings or {}
return {template_var_mappings.get(k, k): v for k, v in kwargs.items()}
def _map_function_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""For keys in function_mappings, compute values and combine w/ kwargs.
Users can pass in functions instead of fixed values as format variables.
For each function, we call the function with the current kwargs,
get back the value, and then use that value in the template
for the corresponding format variable.
"""
function_mappings = self.function_mappings or {}
# first generate the values for the functions
new_kwargs = {}
for k, v in function_mappings.items():
# TODO: figure out what variables to pass into each function
# is it the kwargs specified during query time? just the fixed kwargs?
# all kwargs?
new_kwargs[k] = v(**kwargs)
# then, add the fixed variables only if not in new_kwargs already
# (implying that function mapping will override fixed variables)
for k, v in kwargs.items():
if k not in new_kwargs:
new_kwargs[k] = v
return new_kwargs
def _map_all_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""Map both template and function variables.
We (1) first call function mappings to compute functions,
and then (2) call the template_var_mappings.
"""
# map function
new_kwargs = self._map_function_vars(kwargs)
# map template vars (to point to existing format vars in string template)
return self._map_template_vars(new_kwargs)
class Config:
arbitrary_types_allowed = True
@abstractmethod
def partial_format(self, **kwargs: Any) -> "BasePromptTemplate":
...
@abstractmethod
def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
...
@abstractmethod
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
...
@abstractmethod
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
...
def _as_query_component(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> QueryComponent:
"""As query component."""
return PromptComponent(prompt=self, format_messages=False, llm=llm)
class PromptTemplate(BasePromptTemplate):
template: str
def __init__(
self,
template: str,
prompt_type: str = PromptType.CUSTOM,
output_parser: Optional[BaseOutputParser] = None,
metadata: Optional[Dict[str, Any]] = None,
template_var_mappings: Optional[Dict[str, Any]] = None,
function_mappings: Optional[Dict[str, Callable]] = None,
**kwargs: Any,
) -> None:
if metadata is None:
metadata = {}
metadata["prompt_type"] = prompt_type
template_vars = get_template_vars(template)
super().__init__(
template=template,
template_vars=template_vars,
kwargs=kwargs,
metadata=metadata,
output_parser=output_parser,
template_var_mappings=template_var_mappings,
function_mappings=function_mappings,
)
def partial_format(self, **kwargs: Any) -> "PromptTemplate":
"""Partially format the prompt."""
# NOTE: this is a hack to get around deepcopy failing on output parser
output_parser = self.output_parser
self.output_parser = None
# get function and fixed kwargs, and add that to a copy
# of the current prompt object
prompt = deepcopy(self)
prompt.kwargs.update(kwargs)
# NOTE: put the output parser back
prompt.output_parser = output_parser
self.output_parser = output_parser
return prompt
def format(
self,
llm: Optional[BaseLLM] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
**kwargs: Any,
) -> str:
"""Format the prompt into a string."""
del llm # unused
all_kwargs = {
**self.kwargs,
**kwargs,
}
mapped_all_kwargs = self._map_all_vars(all_kwargs)
prompt = self.template.format(**mapped_all_kwargs)
if self.output_parser is not None:
prompt = self.output_parser.format(prompt)
if completion_to_prompt is not None:
prompt = completion_to_prompt(prompt)
return prompt
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Format the prompt into a list of chat messages."""
del llm # unused
prompt = self.format(**kwargs)
return prompt_to_messages(prompt)
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
return self.template
class ChatPromptTemplate(BasePromptTemplate):
message_templates: List[ChatMessage]
def __init__(
self,
message_templates: List[ChatMessage],
prompt_type: str = PromptType.CUSTOM,
output_parser: Optional[BaseOutputParser] = None,
metadata: Optional[Dict[str, Any]] = None,
template_var_mappings: Optional[Dict[str, Any]] = None,
function_mappings: Optional[Dict[str, Callable]] = None,
**kwargs: Any,
):
if metadata is None:
metadata = {}
metadata["prompt_type"] = prompt_type
template_vars = []
for message_template in message_templates:
template_vars.extend(get_template_vars(message_template.content or ""))
super().__init__(
message_templates=message_templates,
kwargs=kwargs,
metadata=metadata,
output_parser=output_parser,
template_vars=template_vars,
template_var_mappings=template_var_mappings,
function_mappings=function_mappings,
)
@classmethod
def from_messages(
cls,
message_templates: Union[List[Tuple[str, str]], List[ChatMessage]],
**kwargs: Any,
) -> "ChatPromptTemplate":
"""From messages."""
if isinstance(message_templates[0], tuple):
message_templates = [
ChatMessage.from_str(role=role, content=content)
for role, content in message_templates
]
return cls(message_templates=message_templates, **kwargs)
def partial_format(self, **kwargs: Any) -> "ChatPromptTemplate":
prompt = deepcopy(self)
prompt.kwargs.update(kwargs)
return prompt
def format(
self,
llm: Optional[BaseLLM] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
**kwargs: Any,
) -> str:
del llm # unused
messages = self.format_messages(**kwargs)
if messages_to_prompt is not None:
return messages_to_prompt(messages)
return default_messages_to_prompt(messages)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
del llm # unused
"""Format the prompt into a list of chat messages."""
all_kwargs = {
**self.kwargs,
**kwargs,
}
mapped_all_kwargs = self._map_all_vars(all_kwargs)
messages: List[ChatMessage] = []
for message_template in self.message_templates:
template_vars = get_template_vars(message_template.content or "")
relevant_kwargs = {
k: v for k, v in mapped_all_kwargs.items() if k in template_vars
}
content_template = message_template.content or ""
# if there's mappings specified, make sure those are used
content = content_template.format(**relevant_kwargs)
message: ChatMessage = message_template.copy()
message.content = content
messages.append(message)
if self.output_parser is not None:
messages = self.output_parser.format_messages(messages)
return messages
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
return default_messages_to_prompt(self.message_templates)
def _as_query_component(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> QueryComponent:
"""As query component."""
return PromptComponent(prompt=self, format_messages=True, llm=llm)
class SelectorPromptTemplate(BasePromptTemplate):
default_template: BasePromptTemplate
conditionals: Optional[
List[Tuple[Callable[[BaseLLM], bool], BasePromptTemplate]]
] = None
def __init__(
self,
default_template: BasePromptTemplate,
conditionals: Optional[
List[Tuple[Callable[[BaseLLM], bool], BasePromptTemplate]]
] = None,
):
metadata = default_template.metadata
kwargs = default_template.kwargs
template_vars = default_template.template_vars
output_parser = default_template.output_parser
super().__init__(
default_template=default_template,
conditionals=conditionals,
metadata=metadata,
kwargs=kwargs,
template_vars=template_vars,
output_parser=output_parser,
)
def select(self, llm: Optional[BaseLLM] = None) -> BasePromptTemplate:
# ensure output parser is up to date
self.default_template.output_parser = self.output_parser
if llm is None:
return self.default_template
if self.conditionals is not None:
for condition, prompt in self.conditionals:
if condition(llm):
# ensure output parser is up to date
prompt.output_parser = self.output_parser
return prompt
return self.default_template
def partial_format(self, **kwargs: Any) -> "SelectorPromptTemplate":
default_template = self.default_template.partial_format(**kwargs)
if self.conditionals is None:
conditionals = None
else:
conditionals = [
(condition, prompt.partial_format(**kwargs))
for condition, prompt in self.conditionals
]
return SelectorPromptTemplate(
default_template=default_template, conditionals=conditionals
)
def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
"""Format the prompt into a string."""
prompt = self.select(llm=llm)
return prompt.format(**kwargs)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Format the prompt into a list of chat messages."""
prompt = self.select(llm=llm)
return prompt.format_messages(**kwargs)
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
prompt = self.select(llm=llm)
return prompt.get_template(llm=llm)
class LangchainPromptTemplate(BasePromptTemplate):
selector: Any
requires_langchain_llm: bool = False
def __init__(
self,
template: Optional["LangchainTemplate"] = None,
selector: Optional["LangchainSelector"] = None,
output_parser: Optional[BaseOutputParser] = None,
prompt_type: str = PromptType.CUSTOM,
metadata: Optional[Dict[str, Any]] = None,
template_var_mappings: Optional[Dict[str, Any]] = None,
function_mappings: Optional[Dict[str, Callable]] = None,
requires_langchain_llm: bool = False,
) -> None:
try:
from llama_index.core.bridge.langchain import (
ConditionalPromptSelector as LangchainSelector,
)
except ImportError:
raise ImportError(
"Must install `llama_index[langchain]` to use LangchainPromptTemplate."
)
if selector is None:
if template is None:
raise ValueError("Must provide either template or selector.")
selector = LangchainSelector(default_prompt=template)
else:
if template is not None:
raise ValueError("Must provide either template or selector.")
selector = selector
kwargs = selector.default_prompt.partial_variables
template_vars = selector.default_prompt.input_variables
if metadata is None:
metadata = {}
metadata["prompt_type"] = prompt_type
super().__init__(
selector=selector,
metadata=metadata,
kwargs=kwargs,
template_vars=template_vars,
output_parser=output_parser,
template_var_mappings=template_var_mappings,
function_mappings=function_mappings,
requires_langchain_llm=requires_langchain_llm,
)
def partial_format(self, **kwargs: Any) -> "BasePromptTemplate":
"""Partially format the prompt."""
from llama_index.core.bridge.langchain import (
ConditionalPromptSelector as LangchainSelector,
)
mapped_kwargs = self._map_all_vars(kwargs)
default_prompt = self.selector.default_prompt.partial(**mapped_kwargs)
conditionals = [
(condition, prompt.partial(**mapped_kwargs))
for condition, prompt in self.selector.conditionals
]
lc_selector = LangchainSelector(
default_prompt=default_prompt, conditionals=conditionals
)
# copy full prompt object, replace selector
lc_prompt = deepcopy(self)
lc_prompt.selector = lc_selector
return lc_prompt
def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str:
"""Format the prompt into a string."""
from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep
if llm is not None:
# if llamaindex LLM is provided, and we require a langchain LLM,
# then error. but otherwise if `requires_langchain_llm` is False,
# then we can just use the default prompt
if not isinstance(llm, LangChainLLM) and self.requires_langchain_llm:
raise ValueError("Must provide a LangChainLLM.")
elif not isinstance(llm, LangChainLLM):
lc_template = self.selector.default_prompt
else:
lc_template = self.selector.get_prompt(llm=llm.llm)
else:
lc_template = self.selector.default_prompt
# if there's mappings specified, make sure those are used
mapped_kwargs = self._map_all_vars(kwargs)
return lc_template.format(**mapped_kwargs)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Format the prompt into a list of chat messages."""
from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep
from llama_index.llms.langchain.utils import (
from_lc_messages,
) # pants: no-infer-dep
if llm is not None:
# if llamaindex LLM is provided, and we require a langchain LLM,
# then error. but otherwise if `requires_langchain_llm` is False,
# then we can just use the default prompt
if not isinstance(llm, LangChainLLM) and self.requires_langchain_llm:
raise ValueError("Must provide a LangChainLLM.")
elif not isinstance(llm, LangChainLLM):
lc_template = self.selector.default_prompt
else:
lc_template = self.selector.get_prompt(llm=llm.llm)
else:
lc_template = self.selector.default_prompt
# if there's mappings specified, make sure those are used
mapped_kwargs = self._map_all_vars(kwargs)
lc_prompt_value = lc_template.format_prompt(**mapped_kwargs)
lc_messages = lc_prompt_value.to_messages()
return from_lc_messages(lc_messages)
def get_template(self, llm: Optional[BaseLLM] = None) -> str:
from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep
if llm is not None:
# if llamaindex LLM is provided, and we require a langchain LLM,
# then error. but otherwise if `requires_langchain_llm` is False,
# then we can just use the default prompt
if not isinstance(llm, LangChainLLM) and self.requires_langchain_llm:
raise ValueError("Must provide a LangChainLLM.")
elif not isinstance(llm, LangChainLLM):
lc_template = self.selector.default_prompt
else:
lc_template = self.selector.get_prompt(llm=llm.llm)
else:
lc_template = self.selector.default_prompt
try:
return str(lc_template.template) # type: ignore
except AttributeError:
return str(lc_template)
# NOTE: only for backwards compatibility
Prompt = PromptTemplate
class PromptComponent(QueryComponent):
"""Prompt component."""
prompt: BasePromptTemplate = Field(..., description="Prompt")
llm: Optional[BaseLLM] = Field(
default=None, description="LLM to use for formatting prompt."
)
format_messages: bool = Field(
default=False,
description="Whether to format the prompt into a list of chat messages.",
)
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: Any) -> None:
"""Set callback manager."""
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
keys = list(input.keys())
for k in keys:
input[k] = validate_and_convert_stringable(input[k])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
if self.format_messages:
output: Union[str, List[ChatMessage]] = self.prompt.format_messages(
llm=self.llm, **kwargs
)
else:
output = self.prompt.format(llm=self.llm, **kwargs)
return {"prompt": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component."""
# NOTE: no native async for prompt
return self._run_component(**kwargs)
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys(
set(self.prompt.template_vars) - set(self.prompt.kwargs)
)
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"prompt"})
| [
"llama_index.core.base.llms.generic_utils.prompt_to_messages",
"llama_index.llms.langchain.utils.from_lc_messages",
"llama_index.core.base.llms.types.ChatMessage.from_str",
"llama_index.core.bridge.langchain.ConditionalPromptSelector",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.base.query_pipeline.query.OutputKeys.from_keys",
"llama_index.core.base.llms.generic_utils.messages_to_prompt",
"llama_index.core.base.query_pipeline.query.validate_and_convert_stringable",
"llama_index.core.prompts.utils.get_template_vars"
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import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
from llama_index.llms import OpenAI
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset(
"Uber10KDataset2021", "./uber10k_2021_dataset"
)
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff")
judge_llm = OpenAI(model="gpt-3.5-turbo")
rag_evaluator = RagEvaluatorPack(
query_engine=query_engine, rag_dataset=rag_dataset, judge_llm=judge_llm
)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.llms.OpenAI"
] | [((301, 371), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""Uber10KDataset2021"""', '"""./uber10k_2021_dataset"""'], {}), "('Uber10KDataset2021', './uber10k_2021_dataset')\n", (323, 371), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((430, 482), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (461, 482), False, 'from llama_index.core import VectorStoreIndex\n'), ((575, 630), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack_stuff"""'], {}), "('RagEvaluatorPack', './pack_stuff')\n", (594, 630), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((647, 676), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-3.5-turbo"""'}), "(model='gpt-3.5-turbo')\n", (653, 676), False, 'from llama_index.llms import OpenAI\n'), ((1562, 1586), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1584, 1586), False, 'import asyncio\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
from llama_index.llms import OpenAI
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset(
"Uber10KDataset2021", "./uber10k_2021_dataset"
)
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff")
judge_llm = OpenAI(model="gpt-3.5-turbo")
rag_evaluator = RagEvaluatorPack(
query_engine=query_engine, rag_dataset=rag_dataset, judge_llm=judge_llm
)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.llms.OpenAI"
] | [((301, 371), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""Uber10KDataset2021"""', '"""./uber10k_2021_dataset"""'], {}), "('Uber10KDataset2021', './uber10k_2021_dataset')\n", (323, 371), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((430, 482), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (461, 482), False, 'from llama_index.core import VectorStoreIndex\n'), ((575, 630), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack_stuff"""'], {}), "('RagEvaluatorPack', './pack_stuff')\n", (594, 630), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((647, 676), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-3.5-turbo"""'}), "(model='gpt-3.5-turbo')\n", (653, 676), False, 'from llama_index.llms import OpenAI\n'), ((1562, 1586), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1584, 1586), False, 'import asyncio\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.llms import OpenAI, Gemini
from llama_index.core import ServiceContext
import pandas as pd
async def main():
# DOWNLOAD LLAMADATASET
pairwise_evaluator_dataset, _ = download_llama_dataset(
"MtBenchHumanJudgementDataset", "./mt_bench_data"
)
# DEFINE EVALUATORS
gpt_4_context = ServiceContext.from_defaults(
llm=OpenAI(temperature=0, model="gpt-4"),
)
gpt_3p5_context = ServiceContext.from_defaults(
llm=OpenAI(temperature=0, model="gpt-3.5-turbo"),
)
gemini_pro_context = ServiceContext.from_defaults(
llm=Gemini(model="models/gemini-pro", temperature=0)
)
evaluators = {
"gpt-4": PairwiseComparisonEvaluator(service_context=gpt_4_context),
"gpt-3.5": PairwiseComparisonEvaluator(service_context=gpt_3p5_context),
"gemini-pro": PairwiseComparisonEvaluator(service_context=gemini_pro_context),
}
# EVALUATE WITH PACK
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
EvaluatorBenchmarkerPack = download_llama_pack("EvaluatorBenchmarkerPack", "./pack")
evaluator_benchmarker = EvaluatorBenchmarkerPack(
evaluator=evaluators["gpt-3.5"],
eval_dataset=pairwise_evaluator_dataset,
show_progress=True,
)
gpt_3p5_benchmark_df = await evaluator_benchmarker.arun(
batch_size=100, sleep_time_in_seconds=0
)
evaluator_benchmarker = EvaluatorBenchmarkerPack(
evaluator=evaluators["gpt-4"],
eval_dataset=pairwise_evaluator_dataset,
show_progress=True,
)
gpt_4_benchmark_df = await evaluator_benchmarker.arun(
batch_size=100, sleep_time_in_seconds=0
)
evaluator_benchmarker = EvaluatorBenchmarkerPack(
evaluator=evaluators["gemini-pro"],
eval_dataset=pairwise_evaluator_dataset,
show_progress=True,
)
gemini_pro_benchmark_df = await evaluator_benchmarker.arun(
batch_size=5, sleep_time_in_seconds=0.5
)
benchmark_df = pd.concat(
[
gpt_3p5_benchmark_df,
gpt_4_benchmark_df,
gemini_pro_benchmark_df,
],
axis=0,
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.llms.Gemini",
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.llms.OpenAI",
"llama_index.core.evaluation.PairwiseComparisonEvaluator"
] | [((402, 475), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""MtBenchHumanJudgementDataset"""', '"""./mt_bench_data"""'], {}), "('MtBenchHumanJudgementDataset', './mt_bench_data')\n", (424, 475), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((1675, 1732), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""EvaluatorBenchmarkerPack"""', '"""./pack"""'], {}), "('EvaluatorBenchmarkerPack', './pack')\n", (1694, 1732), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((2636, 2726), 'pandas.concat', 'pd.concat', (['[gpt_3p5_benchmark_df, gpt_4_benchmark_df, gemini_pro_benchmark_df]'], {'axis': '(0)'}), '([gpt_3p5_benchmark_df, gpt_4_benchmark_df,\n gemini_pro_benchmark_df], axis=0)\n', (2645, 2726), True, 'import pandas as pd\n'), ((2857, 2881), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (2879, 2881), False, 'import asyncio\n'), ((898, 956), 'llama_index.core.evaluation.PairwiseComparisonEvaluator', 'PairwiseComparisonEvaluator', ([], {'service_context': 'gpt_4_context'}), '(service_context=gpt_4_context)\n', (925, 956), False, 'from llama_index.core.evaluation import PairwiseComparisonEvaluator\n'), ((977, 1037), 'llama_index.core.evaluation.PairwiseComparisonEvaluator', 'PairwiseComparisonEvaluator', ([], {'service_context': 'gpt_3p5_context'}), '(service_context=gpt_3p5_context)\n', (1004, 1037), False, 'from llama_index.core.evaluation import PairwiseComparisonEvaluator\n'), ((1061, 1124), 'llama_index.core.evaluation.PairwiseComparisonEvaluator', 'PairwiseComparisonEvaluator', ([], {'service_context': 'gemini_pro_context'}), '(service_context=gemini_pro_context)\n', (1088, 1124), False, 'from llama_index.core.evaluation import PairwiseComparisonEvaluator\n'), ((577, 613), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model': '"""gpt-4"""'}), "(temperature=0, model='gpt-4')\n", (583, 613), False, 'from llama_index.llms import OpenAI, Gemini\n'), ((686, 730), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model': '"""gpt-3.5-turbo"""'}), "(temperature=0, model='gpt-3.5-turbo')\n", (692, 730), False, 'from llama_index.llms import OpenAI, Gemini\n'), ((806, 854), 'llama_index.llms.Gemini', 'Gemini', ([], {'model': '"""models/gemini-pro"""', 'temperature': '(0)'}), "(model='models/gemini-pro', temperature=0)\n", (812, 854), False, 'from llama_index.llms import OpenAI, Gemini\n')] |
from abc import abstractmethod
from typing import (
Any,
Sequence,
)
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
)
from llama_index.core.bridge.pydantic import Field, validator
from llama_index.core.callbacks import CallbackManager
from llama_index.core.schema import BaseComponent
class BaseLLM(ChainableMixin, BaseComponent):
"""LLM interface."""
callback_manager: CallbackManager = Field(
default_factory=CallbackManager, exclude=True
)
class Config:
arbitrary_types_allowed = True
@validator("callback_manager", pre=True)
def _validate_callback_manager(cls, v: CallbackManager) -> CallbackManager:
if v is None:
return CallbackManager([])
return v
@property
@abstractmethod
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
@abstractmethod
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat endpoint for LLM."""
@abstractmethod
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint for LLM."""
@abstractmethod
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
"""Streaming chat endpoint for LLM."""
@abstractmethod
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Streaming completion endpoint for LLM."""
# ===== Async Endpoints =====
@abstractmethod
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""Async chat endpoint for LLM."""
@abstractmethod
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Async completion endpoint for LLM."""
@abstractmethod
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
"""Async streaming chat endpoint for LLM."""
@abstractmethod
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
"""Async streaming completion endpoint for LLM."""
| [
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.bridge.pydantic.validator",
"llama_index.core.bridge.pydantic.Field"
] | [((669, 721), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'CallbackManager', 'exclude': '(True)'}), '(default_factory=CallbackManager, exclude=True)\n', (674, 721), False, 'from llama_index.core.bridge.pydantic import Field, validator\n'), ((800, 839), 'llama_index.core.bridge.pydantic.validator', 'validator', (['"""callback_manager"""'], {'pre': '(True)'}), "('callback_manager', pre=True)\n", (809, 839), False, 'from llama_index.core.bridge.pydantic import Field, validator\n'), ((961, 980), 'llama_index.core.callbacks.CallbackManager', 'CallbackManager', (['[]'], {}), '([])\n', (976, 980), False, 'from llama_index.core.callbacks import CallbackManager\n')] |
from abc import abstractmethod
from typing import (
Any,
Sequence,
)
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
)
from llama_index.core.bridge.pydantic import Field, validator
from llama_index.core.callbacks import CallbackManager
from llama_index.core.schema import BaseComponent
class BaseLLM(ChainableMixin, BaseComponent):
"""LLM interface."""
callback_manager: CallbackManager = Field(
default_factory=CallbackManager, exclude=True
)
class Config:
arbitrary_types_allowed = True
@validator("callback_manager", pre=True)
def _validate_callback_manager(cls, v: CallbackManager) -> CallbackManager:
if v is None:
return CallbackManager([])
return v
@property
@abstractmethod
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
@abstractmethod
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat endpoint for LLM."""
@abstractmethod
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint for LLM."""
@abstractmethod
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
"""Streaming chat endpoint for LLM."""
@abstractmethod
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Streaming completion endpoint for LLM."""
# ===== Async Endpoints =====
@abstractmethod
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""Async chat endpoint for LLM."""
@abstractmethod
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Async completion endpoint for LLM."""
@abstractmethod
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
"""Async streaming chat endpoint for LLM."""
@abstractmethod
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
"""Async streaming completion endpoint for LLM."""
| [
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.bridge.pydantic.validator",
"llama_index.core.bridge.pydantic.Field"
] | [((669, 721), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'CallbackManager', 'exclude': '(True)'}), '(default_factory=CallbackManager, exclude=True)\n', (674, 721), False, 'from llama_index.core.bridge.pydantic import Field, validator\n'), ((800, 839), 'llama_index.core.bridge.pydantic.validator', 'validator', (['"""callback_manager"""'], {'pre': '(True)'}), "('callback_manager', pre=True)\n", (809, 839), False, 'from llama_index.core.bridge.pydantic import Field, validator\n'), ((961, 980), 'llama_index.core.callbacks.CallbackManager', 'CallbackManager', (['[]'], {}), '([])\n', (976, 980), False, 'from llama_index.core.callbacks import CallbackManager\n')] |
from abc import abstractmethod
from typing import (
Any,
Sequence,
)
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
)
from llama_index.core.bridge.pydantic import Field, validator
from llama_index.core.callbacks import CallbackManager
from llama_index.core.schema import BaseComponent
class BaseLLM(ChainableMixin, BaseComponent):
"""LLM interface."""
callback_manager: CallbackManager = Field(
default_factory=CallbackManager, exclude=True
)
class Config:
arbitrary_types_allowed = True
@validator("callback_manager", pre=True)
def _validate_callback_manager(cls, v: CallbackManager) -> CallbackManager:
if v is None:
return CallbackManager([])
return v
@property
@abstractmethod
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
@abstractmethod
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat endpoint for LLM."""
@abstractmethod
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint for LLM."""
@abstractmethod
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
"""Streaming chat endpoint for LLM."""
@abstractmethod
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Streaming completion endpoint for LLM."""
# ===== Async Endpoints =====
@abstractmethod
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""Async chat endpoint for LLM."""
@abstractmethod
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Async completion endpoint for LLM."""
@abstractmethod
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
"""Async streaming chat endpoint for LLM."""
@abstractmethod
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
"""Async streaming completion endpoint for LLM."""
| [
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.bridge.pydantic.validator",
"llama_index.core.bridge.pydantic.Field"
] | [((669, 721), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'CallbackManager', 'exclude': '(True)'}), '(default_factory=CallbackManager, exclude=True)\n', (674, 721), False, 'from llama_index.core.bridge.pydantic import Field, validator\n'), ((800, 839), 'llama_index.core.bridge.pydantic.validator', 'validator', (['"""callback_manager"""'], {'pre': '(True)'}), "('callback_manager', pre=True)\n", (809, 839), False, 'from llama_index.core.bridge.pydantic import Field, validator\n'), ((961, 980), 'llama_index.core.callbacks.CallbackManager', 'CallbackManager', (['[]'], {}), '([])\n', (976, 980), False, 'from llama_index.core.callbacks import CallbackManager\n')] |
from abc import abstractmethod
from typing import Any, List, Sequence, Union
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
QueryComponent,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.prompts.mixin import PromptMixin, PromptMixinType
from llama_index.core.schema import QueryBundle, QueryType
from llama_index.core.tools.types import ToolMetadata
MetadataType = Union[str, ToolMetadata]
class SingleSelection(BaseModel):
"""A single selection of a choice."""
index: int
reason: str
class MultiSelection(BaseModel):
"""A multi-selection of choices."""
selections: List[SingleSelection]
@property
def ind(self) -> int:
if len(self.selections) != 1:
raise ValueError(
f"There are {len(self.selections)} selections, " "please use .inds."
)
return self.selections[0].index
@property
def reason(self) -> str:
if len(self.reasons) != 1:
raise ValueError(
f"There are {len(self.reasons)} selections, " "please use .reasons."
)
return self.selections[0].reason
@property
def inds(self) -> List[int]:
return [x.index for x in self.selections]
@property
def reasons(self) -> List[str]:
return [x.reason for x in self.selections]
# separate name for clarity and to not confuse function calling model
SelectorResult = MultiSelection
def _wrap_choice(choice: MetadataType) -> ToolMetadata:
if isinstance(choice, ToolMetadata):
return choice
elif isinstance(choice, str):
return ToolMetadata(description=choice)
else:
raise ValueError(f"Unexpected type: {type(choice)}")
def _wrap_query(query: QueryType) -> QueryBundle:
if isinstance(query, QueryBundle):
return query
elif isinstance(query, str):
return QueryBundle(query_str=query)
else:
raise ValueError(f"Unexpected type: {type(query)}")
class BaseSelector(PromptMixin, ChainableMixin):
"""Base selector."""
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt sub-modules."""
return {}
def select(
self, choices: Sequence[MetadataType], query: QueryType
) -> SelectorResult:
metadatas = [_wrap_choice(choice) for choice in choices]
query_bundle = _wrap_query(query)
return self._select(choices=metadatas, query=query_bundle)
async def aselect(
self, choices: Sequence[MetadataType], query: QueryType
) -> SelectorResult:
metadatas = [_wrap_choice(choice) for choice in choices]
query_bundle = _wrap_query(query)
return await self._aselect(choices=metadatas, query=query_bundle)
@abstractmethod
def _select(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> SelectorResult:
pass
@abstractmethod
async def _aselect(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> SelectorResult:
pass
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
from llama_index.core.query_pipeline.components.router import (
SelectorComponent,
)
return SelectorComponent(selector=self)
| [
"llama_index.core.query_pipeline.components.router.SelectorComponent",
"llama_index.core.schema.QueryBundle",
"llama_index.core.tools.types.ToolMetadata"
] | [((3300, 3332), 'llama_index.core.query_pipeline.components.router.SelectorComponent', 'SelectorComponent', ([], {'selector': 'self'}), '(selector=self)\n', (3317, 3332), False, 'from llama_index.core.query_pipeline.components.router import SelectorComponent\n'), ((1653, 1685), 'llama_index.core.tools.types.ToolMetadata', 'ToolMetadata', ([], {'description': 'choice'}), '(description=choice)\n', (1665, 1685), False, 'from llama_index.core.tools.types import ToolMetadata\n'), ((1917, 1945), 'llama_index.core.schema.QueryBundle', 'QueryBundle', ([], {'query_str': 'query'}), '(query_str=query)\n', (1928, 1945), False, 'from llama_index.core.schema import QueryBundle, QueryType\n')] |
from abc import abstractmethod
from typing import Any, List, Sequence, Union
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
QueryComponent,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.prompts.mixin import PromptMixin, PromptMixinType
from llama_index.core.schema import QueryBundle, QueryType
from llama_index.core.tools.types import ToolMetadata
MetadataType = Union[str, ToolMetadata]
class SingleSelection(BaseModel):
"""A single selection of a choice."""
index: int
reason: str
class MultiSelection(BaseModel):
"""A multi-selection of choices."""
selections: List[SingleSelection]
@property
def ind(self) -> int:
if len(self.selections) != 1:
raise ValueError(
f"There are {len(self.selections)} selections, " "please use .inds."
)
return self.selections[0].index
@property
def reason(self) -> str:
if len(self.reasons) != 1:
raise ValueError(
f"There are {len(self.reasons)} selections, " "please use .reasons."
)
return self.selections[0].reason
@property
def inds(self) -> List[int]:
return [x.index for x in self.selections]
@property
def reasons(self) -> List[str]:
return [x.reason for x in self.selections]
# separate name for clarity and to not confuse function calling model
SelectorResult = MultiSelection
def _wrap_choice(choice: MetadataType) -> ToolMetadata:
if isinstance(choice, ToolMetadata):
return choice
elif isinstance(choice, str):
return ToolMetadata(description=choice)
else:
raise ValueError(f"Unexpected type: {type(choice)}")
def _wrap_query(query: QueryType) -> QueryBundle:
if isinstance(query, QueryBundle):
return query
elif isinstance(query, str):
return QueryBundle(query_str=query)
else:
raise ValueError(f"Unexpected type: {type(query)}")
class BaseSelector(PromptMixin, ChainableMixin):
"""Base selector."""
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt sub-modules."""
return {}
def select(
self, choices: Sequence[MetadataType], query: QueryType
) -> SelectorResult:
metadatas = [_wrap_choice(choice) for choice in choices]
query_bundle = _wrap_query(query)
return self._select(choices=metadatas, query=query_bundle)
async def aselect(
self, choices: Sequence[MetadataType], query: QueryType
) -> SelectorResult:
metadatas = [_wrap_choice(choice) for choice in choices]
query_bundle = _wrap_query(query)
return await self._aselect(choices=metadatas, query=query_bundle)
@abstractmethod
def _select(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> SelectorResult:
pass
@abstractmethod
async def _aselect(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> SelectorResult:
pass
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
from llama_index.core.query_pipeline.components.router import (
SelectorComponent,
)
return SelectorComponent(selector=self)
| [
"llama_index.core.query_pipeline.components.router.SelectorComponent",
"llama_index.core.schema.QueryBundle",
"llama_index.core.tools.types.ToolMetadata"
] | [((3300, 3332), 'llama_index.core.query_pipeline.components.router.SelectorComponent', 'SelectorComponent', ([], {'selector': 'self'}), '(selector=self)\n', (3317, 3332), False, 'from llama_index.core.query_pipeline.components.router import SelectorComponent\n'), ((1653, 1685), 'llama_index.core.tools.types.ToolMetadata', 'ToolMetadata', ([], {'description': 'choice'}), '(description=choice)\n', (1665, 1685), False, 'from llama_index.core.tools.types import ToolMetadata\n'), ((1917, 1945), 'llama_index.core.schema.QueryBundle', 'QueryBundle', ([], {'query_str': 'query'}), '(query_str=query)\n', (1928, 1945), False, 'from llama_index.core.schema import QueryBundle, QueryType\n')] |
from abc import abstractmethod
from typing import Any, List, Sequence, Union
from llama_index.core.base.query_pipeline.query import (
ChainableMixin,
QueryComponent,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.prompts.mixin import PromptMixin, PromptMixinType
from llama_index.core.schema import QueryBundle, QueryType
from llama_index.core.tools.types import ToolMetadata
MetadataType = Union[str, ToolMetadata]
class SingleSelection(BaseModel):
"""A single selection of a choice."""
index: int
reason: str
class MultiSelection(BaseModel):
"""A multi-selection of choices."""
selections: List[SingleSelection]
@property
def ind(self) -> int:
if len(self.selections) != 1:
raise ValueError(
f"There are {len(self.selections)} selections, " "please use .inds."
)
return self.selections[0].index
@property
def reason(self) -> str:
if len(self.reasons) != 1:
raise ValueError(
f"There are {len(self.reasons)} selections, " "please use .reasons."
)
return self.selections[0].reason
@property
def inds(self) -> List[int]:
return [x.index for x in self.selections]
@property
def reasons(self) -> List[str]:
return [x.reason for x in self.selections]
# separate name for clarity and to not confuse function calling model
SelectorResult = MultiSelection
def _wrap_choice(choice: MetadataType) -> ToolMetadata:
if isinstance(choice, ToolMetadata):
return choice
elif isinstance(choice, str):
return ToolMetadata(description=choice)
else:
raise ValueError(f"Unexpected type: {type(choice)}")
def _wrap_query(query: QueryType) -> QueryBundle:
if isinstance(query, QueryBundle):
return query
elif isinstance(query, str):
return QueryBundle(query_str=query)
else:
raise ValueError(f"Unexpected type: {type(query)}")
class BaseSelector(PromptMixin, ChainableMixin):
"""Base selector."""
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt sub-modules."""
return {}
def select(
self, choices: Sequence[MetadataType], query: QueryType
) -> SelectorResult:
metadatas = [_wrap_choice(choice) for choice in choices]
query_bundle = _wrap_query(query)
return self._select(choices=metadatas, query=query_bundle)
async def aselect(
self, choices: Sequence[MetadataType], query: QueryType
) -> SelectorResult:
metadatas = [_wrap_choice(choice) for choice in choices]
query_bundle = _wrap_query(query)
return await self._aselect(choices=metadatas, query=query_bundle)
@abstractmethod
def _select(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> SelectorResult:
pass
@abstractmethod
async def _aselect(
self, choices: Sequence[ToolMetadata], query: QueryBundle
) -> SelectorResult:
pass
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
from llama_index.core.query_pipeline.components.router import (
SelectorComponent,
)
return SelectorComponent(selector=self)
| [
"llama_index.core.query_pipeline.components.router.SelectorComponent",
"llama_index.core.schema.QueryBundle",
"llama_index.core.tools.types.ToolMetadata"
] | [((3300, 3332), 'llama_index.core.query_pipeline.components.router.SelectorComponent', 'SelectorComponent', ([], {'selector': 'self'}), '(selector=self)\n', (3317, 3332), False, 'from llama_index.core.query_pipeline.components.router import SelectorComponent\n'), ((1653, 1685), 'llama_index.core.tools.types.ToolMetadata', 'ToolMetadata', ([], {'description': 'choice'}), '(description=choice)\n', (1665, 1685), False, 'from llama_index.core.tools.types import ToolMetadata\n'), ((1917, 1945), 'llama_index.core.schema.QueryBundle', 'QueryBundle', ([], {'query_str': 'query'}), '(query_str=query)\n', (1928, 1945), False, 'from llama_index.core.schema import QueryBundle, QueryType\n')] |
"""Base agent type."""
import uuid
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from llama_index.legacy.bridge.pydantic import BaseModel, Field
from llama_index.legacy.callbacks import CallbackManager, trace_method
from llama_index.legacy.chat_engine.types import (
BaseChatEngine,
StreamingAgentChatResponse,
)
from llama_index.legacy.core.base_query_engine import BaseQueryEngine
from llama_index.legacy.core.llms.types import ChatMessage
from llama_index.legacy.core.response.schema import RESPONSE_TYPE, Response
from llama_index.legacy.memory.types import BaseMemory
from llama_index.legacy.prompts.mixin import (
PromptDictType,
PromptMixin,
PromptMixinType,
)
from llama_index.legacy.schema import QueryBundle
class BaseAgent(BaseChatEngine, BaseQueryEngine):
"""Base Agent."""
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
# TODO: the ReAct agent does not explicitly specify prompts, would need a
# refactor to expose those prompts
return {}
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt modules."""
return {}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
# ===== Query Engine Interface =====
@trace_method("query")
def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
agent_response = self.chat(
query_bundle.query_str,
chat_history=[],
)
return Response(
response=str(agent_response), source_nodes=agent_response.source_nodes
)
@trace_method("query")
async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
agent_response = await self.achat(
query_bundle.query_str,
chat_history=[],
)
return Response(
response=str(agent_response), source_nodes=agent_response.source_nodes
)
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
raise NotImplementedError("stream_chat not implemented")
async def astream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
raise NotImplementedError("astream_chat not implemented")
class TaskStep(BaseModel):
"""Agent task step.
Represents a single input step within the execution run ("Task") of an agent
given a user input.
The output is returned as a `TaskStepOutput`.
"""
task_id: str = Field(..., diescription="Task ID")
step_id: str = Field(..., description="Step ID")
input: Optional[str] = Field(default=None, description="User input")
# memory: BaseMemory = Field(
# ..., type=BaseMemory, description="Conversational Memory"
# )
step_state: Dict[str, Any] = Field(
default_factory=dict, description="Additional state for a given step."
)
# NOTE: the state below may change throughout the course of execution
# this tracks the relationships to other steps
next_steps: Dict[str, "TaskStep"] = Field(
default_factory=dict, description="Next steps to be executed."
)
prev_steps: Dict[str, "TaskStep"] = Field(
default_factory=dict,
description="Previous steps that were dependencies for this step.",
)
is_ready: bool = Field(
default=True, description="Is this step ready to be executed?"
)
def get_next_step(
self,
step_id: str,
input: Optional[str] = None,
step_state: Optional[Dict[str, Any]] = None,
) -> "TaskStep":
"""Convenience function to get next step.
Preserve task_id, memory, step_state.
"""
return TaskStep(
task_id=self.task_id,
step_id=step_id,
input=input,
# memory=self.memory,
step_state=step_state or self.step_state,
)
def link_step(
self,
next_step: "TaskStep",
) -> None:
"""Link to next step.
Add link from this step to next, and from next step to current.
"""
self.next_steps[next_step.step_id] = next_step
next_step.prev_steps[self.step_id] = self
class TaskStepOutput(BaseModel):
"""Agent task step output."""
output: Any = Field(..., description="Task step output")
task_step: TaskStep = Field(..., description="Task step input")
next_steps: List[TaskStep] = Field(..., description="Next steps to be executed.")
is_last: bool = Field(default=False, description="Is this the last step?")
def __str__(self) -> str:
"""String representation."""
return str(self.output)
class Task(BaseModel):
"""Agent Task.
Represents a "run" of an agent given a user input.
"""
class Config:
arbitrary_types_allowed = True
task_id: str = Field(
default_factory=lambda: str(uuid.uuid4()), type=str, description="Task ID"
)
input: str = Field(..., type=str, description="User input")
# NOTE: this is state that may be modified throughout the course of execution of the task
memory: BaseMemory = Field(
...,
type=BaseMemory,
description=(
"Conversational Memory. Maintains state before execution of this task."
),
)
callback_manager: CallbackManager = Field(
default_factory=CallbackManager,
exclude=True,
description="Callback manager for the task.",
)
extra_state: Dict[str, Any] = Field(
default_factory=dict,
description=(
"Additional user-specified state for a given task. "
"Can be modified throughout the execution of a task."
),
)
class BaseAgentWorker(PromptMixin):
"""Base agent worker."""
class Config:
arbitrary_types_allowed = True
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
# TODO: the ReAct agent does not explicitly specify prompts, would need a
# refactor to expose those prompts
return {}
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt modules."""
return {}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
@abstractmethod
def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
"""Initialize step from task."""
@abstractmethod
def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
"""Run step."""
@abstractmethod
async def arun_step(
self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
"""Run step (async)."""
raise NotImplementedError
@abstractmethod
def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
"""Run step (stream)."""
# TODO: figure out if we need a different type for TaskStepOutput
raise NotImplementedError
@abstractmethod
async def astream_step(
self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
"""Run step (async stream)."""
raise NotImplementedError
@abstractmethod
def finalize_task(self, task: Task, **kwargs: Any) -> None:
"""Finalize task, after all the steps are completed."""
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
# TODO: make this abstractmethod (right now will break some agent impls)
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"llama_index.legacy.callbacks.trace_method",
"llama_index.legacy.bridge.pydantic.Field"
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Can be modified throughout the execution of a task.'\n )\n", (5626, 5775), False, 'from llama_index.legacy.bridge.pydantic import BaseModel, Field\n'), ((5010, 5022), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (5020, 5022), False, 'import uuid\n')] |
import json
from typing import Any, Dict, Sequence, Tuple
import httpx
from httpx import Timeout
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
DEFAULT_REQUEST_TIMEOUT = 30.0
def get_addtional_kwargs(
response: Dict[str, Any], exclude: Tuple[str, ...]
) -> Dict[str, Any]:
return {k: v for k, v in response.items() if k not in exclude}
class Ollama(CustomLLM):
base_url: str = Field(
default="http://localhost:11434",
description="Base url the model is hosted under.",
)
model: str = Field(description="The Ollama model to use.")
temperature: float = Field(
default=0.75,
description="The temperature to use for sampling.",
gte=0.0,
lte=1.0,
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
request_timeout: float = Field(
default=DEFAULT_REQUEST_TIMEOUT,
description="The timeout for making http request to Ollama API server",
)
prompt_key: str = Field(
default="prompt", description="The key to use for the prompt in API calls."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional model parameters for the Ollama API.",
)
@classmethod
def class_name(cls) -> str:
return "Ollama_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=DEFAULT_NUM_OUTPUTS,
model_name=self.model,
is_chat_model=True, # Ollama supports chat API for all models
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"num_ctx": self.context_window,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
payload = {
"model": self.model,
"messages": [
{
"role": message.role.value,
"content": message.content,
**message.additional_kwargs,
}
for message in messages
],
"options": self._model_kwargs,
"stream": False,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/api/chat",
json=payload,
)
response.raise_for_status()
raw = response.json()
message = raw["message"]
return ChatResponse(
message=ChatMessage(
content=message.get("content"),
role=MessageRole(message.get("role")),
additional_kwargs=get_addtional_kwargs(
message, ("content", "role")
),
),
raw=raw,
additional_kwargs=get_addtional_kwargs(raw, ("message",)),
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
payload = {
"model": self.model,
"messages": [
{
"role": message.role.value,
"content": message.content,
**message.additional_kwargs,
}
for message in messages
],
"options": self._model_kwargs,
"stream": True,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
with client.stream(
method="POST",
url=f"{self.base_url}/api/chat",
json=payload,
) as response:
response.raise_for_status()
text = ""
for line in response.iter_lines():
if line:
chunk = json.loads(line)
if "done" in chunk and chunk["done"]:
break
message = chunk["message"]
delta = message.get("content")
text += delta
yield ChatResponse(
message=ChatMessage(
content=text,
role=MessageRole(message.get("role")),
additional_kwargs=get_addtional_kwargs(
message, ("content", "role")
),
),
delta=delta,
raw=chunk,
additional_kwargs=get_addtional_kwargs(chunk, ("message",)),
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
payload = {
self.prompt_key: prompt,
"model": self.model,
"options": self._model_kwargs,
"stream": False,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/api/generate",
json=payload,
)
response.raise_for_status()
raw = response.json()
text = raw.get("response")
return CompletionResponse(
text=text,
raw=raw,
additional_kwargs=get_addtional_kwargs(raw, ("response",)),
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
payload = {
self.prompt_key: prompt,
"model": self.model,
"options": self._model_kwargs,
"stream": True,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
with client.stream(
method="POST",
url=f"{self.base_url}/api/generate",
json=payload,
) as response:
response.raise_for_status()
text = ""
for line in response.iter_lines():
if line:
chunk = json.loads(line)
delta = chunk.get("response")
text += delta
yield CompletionResponse(
delta=delta,
text=text,
raw=chunk,
additional_kwargs=get_addtional_kwargs(
chunk, ("response",)
),
)
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"llama_index.legacy.llms.base.llm_chat_callback",
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import json
from typing import Any, Dict, Sequence, Tuple
import httpx
from httpx import Timeout
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
DEFAULT_REQUEST_TIMEOUT = 30.0
def get_addtional_kwargs(
response: Dict[str, Any], exclude: Tuple[str, ...]
) -> Dict[str, Any]:
return {k: v for k, v in response.items() if k not in exclude}
class Ollama(CustomLLM):
base_url: str = Field(
default="http://localhost:11434",
description="Base url the model is hosted under.",
)
model: str = Field(description="The Ollama model to use.")
temperature: float = Field(
default=0.75,
description="The temperature to use for sampling.",
gte=0.0,
lte=1.0,
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
request_timeout: float = Field(
default=DEFAULT_REQUEST_TIMEOUT,
description="The timeout for making http request to Ollama API server",
)
prompt_key: str = Field(
default="prompt", description="The key to use for the prompt in API calls."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional model parameters for the Ollama API.",
)
@classmethod
def class_name(cls) -> str:
return "Ollama_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=DEFAULT_NUM_OUTPUTS,
model_name=self.model,
is_chat_model=True, # Ollama supports chat API for all models
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"num_ctx": self.context_window,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
payload = {
"model": self.model,
"messages": [
{
"role": message.role.value,
"content": message.content,
**message.additional_kwargs,
}
for message in messages
],
"options": self._model_kwargs,
"stream": False,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/api/chat",
json=payload,
)
response.raise_for_status()
raw = response.json()
message = raw["message"]
return ChatResponse(
message=ChatMessage(
content=message.get("content"),
role=MessageRole(message.get("role")),
additional_kwargs=get_addtional_kwargs(
message, ("content", "role")
),
),
raw=raw,
additional_kwargs=get_addtional_kwargs(raw, ("message",)),
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
payload = {
"model": self.model,
"messages": [
{
"role": message.role.value,
"content": message.content,
**message.additional_kwargs,
}
for message in messages
],
"options": self._model_kwargs,
"stream": True,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
with client.stream(
method="POST",
url=f"{self.base_url}/api/chat",
json=payload,
) as response:
response.raise_for_status()
text = ""
for line in response.iter_lines():
if line:
chunk = json.loads(line)
if "done" in chunk and chunk["done"]:
break
message = chunk["message"]
delta = message.get("content")
text += delta
yield ChatResponse(
message=ChatMessage(
content=text,
role=MessageRole(message.get("role")),
additional_kwargs=get_addtional_kwargs(
message, ("content", "role")
),
),
delta=delta,
raw=chunk,
additional_kwargs=get_addtional_kwargs(chunk, ("message",)),
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
payload = {
self.prompt_key: prompt,
"model": self.model,
"options": self._model_kwargs,
"stream": False,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
response = client.post(
url=f"{self.base_url}/api/generate",
json=payload,
)
response.raise_for_status()
raw = response.json()
text = raw.get("response")
return CompletionResponse(
text=text,
raw=raw,
additional_kwargs=get_addtional_kwargs(raw, ("response",)),
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
payload = {
self.prompt_key: prompt,
"model": self.model,
"options": self._model_kwargs,
"stream": True,
**kwargs,
}
with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
with client.stream(
method="POST",
url=f"{self.base_url}/api/generate",
json=payload,
) as response:
response.raise_for_status()
text = ""
for line in response.iter_lines():
if line:
chunk = json.loads(line)
delta = chunk.get("response")
text += delta
yield CompletionResponse(
delta=delta,
text=text,
raw=chunk,
additional_kwargs=get_addtional_kwargs(
chunk, ("response",)
),
)
| [
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field"
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import warnings
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.cohere_utils import (
CHAT_MODELS,
acompletion_with_retry,
cohere_modelname_to_contextsize,
completion_with_retry,
messages_to_cohere_history,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class Cohere(LLM):
model: str = Field(description="The cohere model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_retries: int = Field(
default=10, description="The maximum number of API retries."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Cohere API."
)
max_tokens: int = Field(description="The maximum number of tokens to generate.")
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = "command",
temperature: float = 0.5,
max_tokens: int = 512,
timeout: Optional[float] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
import cohere
except ImportError as e:
raise ImportError(
"You must install the `cohere` package to use Cohere."
"Please `pip install cohere`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
self._client = cohere.Client(api_key, client_name="llama_index")
self._aclient = cohere.AsyncClient(api_key, client_name="llama_index")
super().__init__(
temperature=temperature,
additional_kwargs=additional_kwargs,
timeout=timeout,
max_retries=max_retries,
model=model,
callback_manager=callback_manager,
max_tokens=max_tokens,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "Cohere_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=cohere_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
system_role=MessageRole.CHATBOT,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
raw=response.__dict__,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
return CompletionResponse(
text=response.generations[0].text,
raw=response.__dict__,
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for r in response:
if "text" in r.__dict__:
content_delta = r.text
else:
content_delta = ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=r.__dict__,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
def gen() -> CompletionResponseGen:
content = ""
for r in response:
content_delta = r.text
content += content_delta
yield CompletionResponse(
text=content, delta=content_delta, raw=r._asdict()
)
return gen()
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
raw=response.__dict__,
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
return CompletionResponse(
text=response.generations[0].text,
raw=response.__dict__,
)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
async def gen() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for r in response:
if "text" in r.__dict__:
content_delta = r.text
else:
content_delta = ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=r.__dict__,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
async def gen() -> CompletionResponseAsyncGen:
content = ""
async for r in response:
content_delta = r.text
content += content_delta
yield CompletionResponse(
text=content, delta=content_delta, raw=r._asdict()
)
return gen()
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import warnings
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.cohere_utils import (
CHAT_MODELS,
acompletion_with_retry,
cohere_modelname_to_contextsize,
completion_with_retry,
messages_to_cohere_history,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class Cohere(LLM):
model: str = Field(description="The cohere model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_retries: int = Field(
default=10, description="The maximum number of API retries."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Cohere API."
)
max_tokens: int = Field(description="The maximum number of tokens to generate.")
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = "command",
temperature: float = 0.5,
max_tokens: int = 512,
timeout: Optional[float] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
import cohere
except ImportError as e:
raise ImportError(
"You must install the `cohere` package to use Cohere."
"Please `pip install cohere`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
self._client = cohere.Client(api_key, client_name="llama_index")
self._aclient = cohere.AsyncClient(api_key, client_name="llama_index")
super().__init__(
temperature=temperature,
additional_kwargs=additional_kwargs,
timeout=timeout,
max_retries=max_retries,
model=model,
callback_manager=callback_manager,
max_tokens=max_tokens,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "Cohere_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=cohere_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
system_role=MessageRole.CHATBOT,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
raw=response.__dict__,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
return CompletionResponse(
text=response.generations[0].text,
raw=response.__dict__,
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for r in response:
if "text" in r.__dict__:
content_delta = r.text
else:
content_delta = ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=r.__dict__,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
def gen() -> CompletionResponseGen:
content = ""
for r in response:
content_delta = r.text
content += content_delta
yield CompletionResponse(
text=content, delta=content_delta, raw=r._asdict()
)
return gen()
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
raw=response.__dict__,
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
return CompletionResponse(
text=response.generations[0].text,
raw=response.__dict__,
)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
async def gen() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for r in response:
if "text" in r.__dict__:
content_delta = r.text
else:
content_delta = ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=r.__dict__,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
async def gen() -> CompletionResponseAsyncGen:
content = ""
async for r in response:
content_delta = r.text
content += content_delta
yield CompletionResponse(
text=content, delta=content_delta, raw=r._asdict()
)
return gen()
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from abc import abstractmethod
from typing import List
from llama_index.core.indices.query.schema import QueryBundle, QueryType
from llama_index.core.prompts.mixin import PromptMixin
from llama_index.core.schema import NodeWithScore
class BaseImageRetriever(PromptMixin):
"""Base Image Retriever Abstraction."""
def text_to_image_retrieve(
self, str_or_query_bundle: QueryType
) -> List[NodeWithScore]:
"""Retrieve image nodes given query or single image input.
Args:
str_or_query_bundle (QueryType): a query text
string or a QueryBundle object.
"""
if isinstance(str_or_query_bundle, str):
str_or_query_bundle = QueryBundle(query_str=str_or_query_bundle)
return self._text_to_image_retrieve(str_or_query_bundle)
@abstractmethod
def _text_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Retrieve image nodes or documents given query text.
Implemented by the user.
"""
def image_to_image_retrieve(
self, str_or_query_bundle: QueryType
) -> List[NodeWithScore]:
"""Retrieve image nodes given single image input.
Args:
str_or_query_bundle (QueryType): a image path
string or a QueryBundle object.
"""
if isinstance(str_or_query_bundle, str):
# leave query_str as empty since we are using image_path for image retrieval
str_or_query_bundle = QueryBundle(
query_str="", image_path=str_or_query_bundle
)
return self._image_to_image_retrieve(str_or_query_bundle)
@abstractmethod
def _image_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Retrieve image nodes or documents given image.
Implemented by the user.
"""
# Async Methods
async def atext_to_image_retrieve(
self,
str_or_query_bundle: QueryType,
) -> List[NodeWithScore]:
if isinstance(str_or_query_bundle, str):
str_or_query_bundle = QueryBundle(query_str=str_or_query_bundle)
return await self._atext_to_image_retrieve(str_or_query_bundle)
@abstractmethod
async def _atext_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Async retrieve image nodes or documents given query text.
Implemented by the user.
"""
async def aimage_to_image_retrieve(
self,
str_or_query_bundle: QueryType,
) -> List[NodeWithScore]:
if isinstance(str_or_query_bundle, str):
# leave query_str as empty since we are using image_path for image retrieval
str_or_query_bundle = QueryBundle(
query_str="", image_path=str_or_query_bundle
)
return await self._aimage_to_image_retrieve(str_or_query_bundle)
@abstractmethod
async def _aimage_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Async retrieve image nodes or documents given image.
Implemented by the user.
"""
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from abc import abstractmethod
from typing import List
from llama_index.core.indices.query.schema import QueryBundle, QueryType
from llama_index.core.prompts.mixin import PromptMixin
from llama_index.core.schema import NodeWithScore
class BaseImageRetriever(PromptMixin):
"""Base Image Retriever Abstraction."""
def text_to_image_retrieve(
self, str_or_query_bundle: QueryType
) -> List[NodeWithScore]:
"""Retrieve image nodes given query or single image input.
Args:
str_or_query_bundle (QueryType): a query text
string or a QueryBundle object.
"""
if isinstance(str_or_query_bundle, str):
str_or_query_bundle = QueryBundle(query_str=str_or_query_bundle)
return self._text_to_image_retrieve(str_or_query_bundle)
@abstractmethod
def _text_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Retrieve image nodes or documents given query text.
Implemented by the user.
"""
def image_to_image_retrieve(
self, str_or_query_bundle: QueryType
) -> List[NodeWithScore]:
"""Retrieve image nodes given single image input.
Args:
str_or_query_bundle (QueryType): a image path
string or a QueryBundle object.
"""
if isinstance(str_or_query_bundle, str):
# leave query_str as empty since we are using image_path for image retrieval
str_or_query_bundle = QueryBundle(
query_str="", image_path=str_or_query_bundle
)
return self._image_to_image_retrieve(str_or_query_bundle)
@abstractmethod
def _image_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Retrieve image nodes or documents given image.
Implemented by the user.
"""
# Async Methods
async def atext_to_image_retrieve(
self,
str_or_query_bundle: QueryType,
) -> List[NodeWithScore]:
if isinstance(str_or_query_bundle, str):
str_or_query_bundle = QueryBundle(query_str=str_or_query_bundle)
return await self._atext_to_image_retrieve(str_or_query_bundle)
@abstractmethod
async def _atext_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Async retrieve image nodes or documents given query text.
Implemented by the user.
"""
async def aimage_to_image_retrieve(
self,
str_or_query_bundle: QueryType,
) -> List[NodeWithScore]:
if isinstance(str_or_query_bundle, str):
# leave query_str as empty since we are using image_path for image retrieval
str_or_query_bundle = QueryBundle(
query_str="", image_path=str_or_query_bundle
)
return await self._aimage_to_image_retrieve(str_or_query_bundle)
@abstractmethod
async def _aimage_to_image_retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Async retrieve image nodes or documents given image.
Implemented by the user.
"""
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import json
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, Optional, Type
if TYPE_CHECKING:
from llama_index.legacy.bridge.langchain import StructuredTool, Tool
from deprecated import deprecated
from llama_index.legacy.bridge.pydantic import BaseModel
class DefaultToolFnSchema(BaseModel):
"""Default tool function Schema."""
input: str
@dataclass
class ToolMetadata:
description: str
name: Optional[str] = None
fn_schema: Optional[Type[BaseModel]] = DefaultToolFnSchema
def get_parameters_dict(self) -> dict:
if self.fn_schema is None:
parameters = {
"type": "object",
"properties": {
"input": {"title": "input query string", "type": "string"},
},
"required": ["input"],
}
else:
parameters = self.fn_schema.schema()
parameters = {
k: v
for k, v in parameters.items()
if k in ["type", "properties", "required", "definitions"]
}
return parameters
@property
def fn_schema_str(self) -> str:
"""Get fn schema as string."""
if self.fn_schema is None:
raise ValueError("fn_schema is None.")
parameters = self.get_parameters_dict()
return json.dumps(parameters)
def get_name(self) -> str:
"""Get name."""
if self.name is None:
raise ValueError("name is None.")
return self.name
@deprecated(
"Deprecated in favor of `to_openai_tool`, which should be used instead."
)
def to_openai_function(self) -> Dict[str, Any]:
"""Deprecated and replaced by `to_openai_tool`.
The name and arguments of a function that should be called, as generated by the
model.
"""
return {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters_dict(),
}
def to_openai_tool(self) -> Dict[str, Any]:
"""To OpenAI tool."""
return {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters_dict(),
},
}
class ToolOutput(BaseModel):
"""Tool output."""
content: str
tool_name: str
raw_input: Dict[str, Any]
raw_output: Any
def __str__(self) -> str:
"""String."""
return str(self.content)
class BaseTool:
@property
@abstractmethod
def metadata(self) -> ToolMetadata:
pass
@abstractmethod
def __call__(self, input: Any) -> ToolOutput:
pass
def _process_langchain_tool_kwargs(
self,
langchain_tool_kwargs: Any,
) -> Dict[str, Any]:
"""Process langchain tool kwargs."""
if "name" not in langchain_tool_kwargs:
langchain_tool_kwargs["name"] = self.metadata.name or ""
if "description" not in langchain_tool_kwargs:
langchain_tool_kwargs["description"] = self.metadata.description
if "fn_schema" not in langchain_tool_kwargs:
langchain_tool_kwargs["args_schema"] = self.metadata.fn_schema
return langchain_tool_kwargs
def to_langchain_tool(
self,
**langchain_tool_kwargs: Any,
) -> "Tool":
"""To langchain tool."""
from llama_index.legacy.bridge.langchain import Tool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return Tool.from_function(
func=self.__call__,
**langchain_tool_kwargs,
)
def to_langchain_structured_tool(
self,
**langchain_tool_kwargs: Any,
) -> "StructuredTool":
"""To langchain structured tool."""
from llama_index.legacy.bridge.langchain import StructuredTool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return StructuredTool.from_function(
func=self.__call__,
**langchain_tool_kwargs,
)
class AsyncBaseTool(BaseTool):
"""
Base-level tool class that is backwards compatible with the old tool spec but also
supports async.
"""
def __call__(self, *args: Any, **kwargs: Any) -> ToolOutput:
return self.call(*args, **kwargs)
@abstractmethod
def call(self, input: Any) -> ToolOutput:
"""
This is the method that should be implemented by the tool developer.
"""
@abstractmethod
async def acall(self, input: Any) -> ToolOutput:
"""
This is the async version of the call method.
Should also be implemented by the tool developer as an
async-compatible implementation.
"""
class BaseToolAsyncAdapter(AsyncBaseTool):
"""
Adapter class that allows a synchronous tool to be used as an async tool.
"""
def __init__(self, tool: BaseTool):
self.base_tool = tool
@property
def metadata(self) -> ToolMetadata:
return self.base_tool.metadata
def call(self, input: Any) -> ToolOutput:
return self.base_tool(input)
async def acall(self, input: Any) -> ToolOutput:
return self.call(input)
def adapt_to_async_tool(tool: BaseTool) -> AsyncBaseTool:
"""
Converts a synchronous tool to an async tool.
"""
if isinstance(tool, AsyncBaseTool):
return tool
else:
return BaseToolAsyncAdapter(tool)
| [
"llama_index.legacy.bridge.langchain.Tool.from_function",
"llama_index.legacy.bridge.langchain.StructuredTool.from_function"
] | [((1586, 1675), 'deprecated.deprecated', 'deprecated', (['"""Deprecated in favor of `to_openai_tool`, which should be used instead."""'], {}), "(\n 'Deprecated in favor of `to_openai_tool`, which should be used instead.')\n", (1596, 1675), False, 'from deprecated import deprecated\n'), ((1400, 1422), 'json.dumps', 'json.dumps', (['parameters'], {}), '(parameters)\n', (1410, 1422), False, 'import json\n'), ((3697, 3760), 'llama_index.legacy.bridge.langchain.Tool.from_function', 'Tool.from_function', ([], {'func': 'self.__call__'}), '(func=self.__call__, **langchain_tool_kwargs)\n', (3715, 3760), False, 'from llama_index.legacy.bridge.langchain import Tool\n'), ((4158, 4231), 'llama_index.legacy.bridge.langchain.StructuredTool.from_function', 'StructuredTool.from_function', ([], {'func': 'self.__call__'}), '(func=self.__call__, **langchain_tool_kwargs)\n', (4186, 4231), False, 'from llama_index.legacy.bridge.langchain import StructuredTool\n')] |
import json
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, Optional, Type
if TYPE_CHECKING:
from llama_index.legacy.bridge.langchain import StructuredTool, Tool
from deprecated import deprecated
from llama_index.legacy.bridge.pydantic import BaseModel
class DefaultToolFnSchema(BaseModel):
"""Default tool function Schema."""
input: str
@dataclass
class ToolMetadata:
description: str
name: Optional[str] = None
fn_schema: Optional[Type[BaseModel]] = DefaultToolFnSchema
def get_parameters_dict(self) -> dict:
if self.fn_schema is None:
parameters = {
"type": "object",
"properties": {
"input": {"title": "input query string", "type": "string"},
},
"required": ["input"],
}
else:
parameters = self.fn_schema.schema()
parameters = {
k: v
for k, v in parameters.items()
if k in ["type", "properties", "required", "definitions"]
}
return parameters
@property
def fn_schema_str(self) -> str:
"""Get fn schema as string."""
if self.fn_schema is None:
raise ValueError("fn_schema is None.")
parameters = self.get_parameters_dict()
return json.dumps(parameters)
def get_name(self) -> str:
"""Get name."""
if self.name is None:
raise ValueError("name is None.")
return self.name
@deprecated(
"Deprecated in favor of `to_openai_tool`, which should be used instead."
)
def to_openai_function(self) -> Dict[str, Any]:
"""Deprecated and replaced by `to_openai_tool`.
The name and arguments of a function that should be called, as generated by the
model.
"""
return {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters_dict(),
}
def to_openai_tool(self) -> Dict[str, Any]:
"""To OpenAI tool."""
return {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters_dict(),
},
}
class ToolOutput(BaseModel):
"""Tool output."""
content: str
tool_name: str
raw_input: Dict[str, Any]
raw_output: Any
def __str__(self) -> str:
"""String."""
return str(self.content)
class BaseTool:
@property
@abstractmethod
def metadata(self) -> ToolMetadata:
pass
@abstractmethod
def __call__(self, input: Any) -> ToolOutput:
pass
def _process_langchain_tool_kwargs(
self,
langchain_tool_kwargs: Any,
) -> Dict[str, Any]:
"""Process langchain tool kwargs."""
if "name" not in langchain_tool_kwargs:
langchain_tool_kwargs["name"] = self.metadata.name or ""
if "description" not in langchain_tool_kwargs:
langchain_tool_kwargs["description"] = self.metadata.description
if "fn_schema" not in langchain_tool_kwargs:
langchain_tool_kwargs["args_schema"] = self.metadata.fn_schema
return langchain_tool_kwargs
def to_langchain_tool(
self,
**langchain_tool_kwargs: Any,
) -> "Tool":
"""To langchain tool."""
from llama_index.legacy.bridge.langchain import Tool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return Tool.from_function(
func=self.__call__,
**langchain_tool_kwargs,
)
def to_langchain_structured_tool(
self,
**langchain_tool_kwargs: Any,
) -> "StructuredTool":
"""To langchain structured tool."""
from llama_index.legacy.bridge.langchain import StructuredTool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return StructuredTool.from_function(
func=self.__call__,
**langchain_tool_kwargs,
)
class AsyncBaseTool(BaseTool):
"""
Base-level tool class that is backwards compatible with the old tool spec but also
supports async.
"""
def __call__(self, *args: Any, **kwargs: Any) -> ToolOutput:
return self.call(*args, **kwargs)
@abstractmethod
def call(self, input: Any) -> ToolOutput:
"""
This is the method that should be implemented by the tool developer.
"""
@abstractmethod
async def acall(self, input: Any) -> ToolOutput:
"""
This is the async version of the call method.
Should also be implemented by the tool developer as an
async-compatible implementation.
"""
class BaseToolAsyncAdapter(AsyncBaseTool):
"""
Adapter class that allows a synchronous tool to be used as an async tool.
"""
def __init__(self, tool: BaseTool):
self.base_tool = tool
@property
def metadata(self) -> ToolMetadata:
return self.base_tool.metadata
def call(self, input: Any) -> ToolOutput:
return self.base_tool(input)
async def acall(self, input: Any) -> ToolOutput:
return self.call(input)
def adapt_to_async_tool(tool: BaseTool) -> AsyncBaseTool:
"""
Converts a synchronous tool to an async tool.
"""
if isinstance(tool, AsyncBaseTool):
return tool
else:
return BaseToolAsyncAdapter(tool)
| [
"llama_index.legacy.bridge.langchain.Tool.from_function",
"llama_index.legacy.bridge.langchain.StructuredTool.from_function"
] | [((1586, 1675), 'deprecated.deprecated', 'deprecated', (['"""Deprecated in favor of `to_openai_tool`, which should be used instead."""'], {}), "(\n 'Deprecated in favor of `to_openai_tool`, which should be used instead.')\n", (1596, 1675), False, 'from deprecated import deprecated\n'), ((1400, 1422), 'json.dumps', 'json.dumps', (['parameters'], {}), '(parameters)\n', (1410, 1422), False, 'import json\n'), ((3697, 3760), 'llama_index.legacy.bridge.langchain.Tool.from_function', 'Tool.from_function', ([], {'func': 'self.__call__'}), '(func=self.__call__, **langchain_tool_kwargs)\n', (3715, 3760), False, 'from llama_index.legacy.bridge.langchain import Tool\n'), ((4158, 4231), 'llama_index.legacy.bridge.langchain.StructuredTool.from_function', 'StructuredTool.from_function', ([], {'func': 'self.__call__'}), '(func=self.__call__, **langchain_tool_kwargs)\n', (4186, 4231), False, 'from llama_index.legacy.bridge.langchain import StructuredTool\n')] |
import logging
from dataclasses import dataclass
from typing import Any, List, Optional, cast
from deprecated import deprecated
import llama_index.core
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.indices.prompt_helper import PromptHelper
from llama_index.core.service_context_elements.llm_predictor import (
LLMPredictor,
BaseLLMPredictor,
)
from llama_index.core.base.llms.types import LLMMetadata
from llama_index.core.llms.llm import LLM
from llama_index.core.llms.utils import LLMType, resolve_llm
from llama_index.core.service_context_elements.llama_logger import LlamaLogger
from llama_index.core.node_parser.interface import NodeParser, TextSplitter
from llama_index.core.node_parser.text.sentence import (
DEFAULT_CHUNK_SIZE,
SENTENCE_CHUNK_OVERLAP,
SentenceSplitter,
)
from llama_index.core.prompts.base import BasePromptTemplate
from llama_index.core.schema import TransformComponent
from llama_index.core.types import PydanticProgramMode
logger = logging.getLogger(__name__)
def _get_default_node_parser(
chunk_size: int = DEFAULT_CHUNK_SIZE,
chunk_overlap: int = SENTENCE_CHUNK_OVERLAP,
callback_manager: Optional[CallbackManager] = None,
) -> NodeParser:
"""Get default node parser."""
return SentenceSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
callback_manager=callback_manager or CallbackManager(),
)
def _get_default_prompt_helper(
llm_metadata: LLMMetadata,
context_window: Optional[int] = None,
num_output: Optional[int] = None,
) -> PromptHelper:
"""Get default prompt helper."""
if context_window is not None:
llm_metadata.context_window = context_window
if num_output is not None:
llm_metadata.num_output = num_output
return PromptHelper.from_llm_metadata(llm_metadata=llm_metadata)
class ServiceContextData(BaseModel):
llm: dict
llm_predictor: dict
prompt_helper: dict
embed_model: dict
transformations: List[dict]
@dataclass
class ServiceContext:
"""Service Context container.
The service context container is a utility container for LlamaIndex
index and query classes. It contains the following:
- llm_predictor: BaseLLMPredictor
- prompt_helper: PromptHelper
- embed_model: BaseEmbedding
- node_parser: NodeParser
- llama_logger: LlamaLogger (deprecated)
- callback_manager: CallbackManager
"""
llm_predictor: BaseLLMPredictor
prompt_helper: PromptHelper
embed_model: BaseEmbedding
transformations: List[TransformComponent]
llama_logger: LlamaLogger
callback_manager: CallbackManager
@classmethod
@deprecated(
version="0.10.0",
reason="ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.",
)
def from_defaults(
cls,
llm_predictor: Optional[BaseLLMPredictor] = None,
llm: Optional[LLMType] = "default",
prompt_helper: Optional[PromptHelper] = None,
embed_model: Optional[Any] = "default",
node_parser: Optional[NodeParser] = None,
text_splitter: Optional[TextSplitter] = None,
transformations: Optional[List[TransformComponent]] = None,
llama_logger: Optional[LlamaLogger] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
query_wrapper_prompt: Optional[BasePromptTemplate] = None,
# pydantic program mode (used if output_cls is specified)
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
# node parser kwargs
chunk_size: Optional[int] = None,
chunk_overlap: Optional[int] = None,
# prompt helper kwargs
context_window: Optional[int] = None,
num_output: Optional[int] = None,
# deprecated kwargs
chunk_size_limit: Optional[int] = None,
) -> "ServiceContext":
"""Create a ServiceContext from defaults.
If an argument is specified, then use the argument value provided for that
parameter. If an argument is not specified, then use the default value.
You can change the base defaults by setting llama_index.global_service_context
to a ServiceContext object with your desired settings.
Args:
llm_predictor (Optional[BaseLLMPredictor]): LLMPredictor
prompt_helper (Optional[PromptHelper]): PromptHelper
embed_model (Optional[BaseEmbedding]): BaseEmbedding
or "local" (use local model)
node_parser (Optional[NodeParser]): NodeParser
llama_logger (Optional[LlamaLogger]): LlamaLogger (deprecated)
chunk_size (Optional[int]): chunk_size
callback_manager (Optional[CallbackManager]): CallbackManager
system_prompt (Optional[str]): System-wide prompt to be prepended
to all input prompts, used to guide system "decision making"
query_wrapper_prompt (Optional[BasePromptTemplate]): A format to wrap
passed-in input queries.
Deprecated Args:
chunk_size_limit (Optional[int]): renamed to chunk_size
"""
from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model
embed_model = cast(EmbedType, embed_model)
if chunk_size_limit is not None and chunk_size is None:
logger.warning(
"chunk_size_limit is deprecated, please specify chunk_size instead"
)
chunk_size = chunk_size_limit
if llama_index.core.global_service_context is not None:
return cls.from_service_context(
llama_index.core.global_service_context,
llm=llm,
llm_predictor=llm_predictor,
prompt_helper=prompt_helper,
embed_model=embed_model,
node_parser=node_parser,
text_splitter=text_splitter,
llama_logger=llama_logger,
callback_manager=callback_manager,
context_window=context_window,
chunk_size=chunk_size,
chunk_size_limit=chunk_size_limit,
chunk_overlap=chunk_overlap,
num_output=num_output,
system_prompt=system_prompt,
query_wrapper_prompt=query_wrapper_prompt,
transformations=transformations,
)
callback_manager = callback_manager or CallbackManager([])
if llm != "default":
if llm_predictor is not None:
raise ValueError("Cannot specify both llm and llm_predictor")
llm = resolve_llm(llm)
llm.system_prompt = llm.system_prompt or system_prompt
llm.query_wrapper_prompt = llm.query_wrapper_prompt or query_wrapper_prompt
llm.pydantic_program_mode = (
llm.pydantic_program_mode or pydantic_program_mode
)
if llm_predictor is not None:
print("LLMPredictor is deprecated, please use LLM instead.")
llm_predictor = llm_predictor or LLMPredictor(
llm=llm, pydantic_program_mode=pydantic_program_mode
)
if isinstance(llm_predictor, LLMPredictor):
llm_predictor.llm.callback_manager = callback_manager
if system_prompt:
llm_predictor.system_prompt = system_prompt
if query_wrapper_prompt:
llm_predictor.query_wrapper_prompt = query_wrapper_prompt
# NOTE: the embed_model isn't used in all indices
# NOTE: embed model should be a transformation, but the way the service
# context works, we can't put in there yet.
embed_model = resolve_embed_model(embed_model)
embed_model.callback_manager = callback_manager
prompt_helper = prompt_helper or _get_default_prompt_helper(
llm_metadata=llm_predictor.metadata,
context_window=context_window,
num_output=num_output,
)
if text_splitter is not None and node_parser is not None:
raise ValueError("Cannot specify both text_splitter and node_parser")
node_parser = (
text_splitter # text splitter extends node parser
or node_parser
or _get_default_node_parser(
chunk_size=chunk_size or DEFAULT_CHUNK_SIZE,
chunk_overlap=chunk_overlap or SENTENCE_CHUNK_OVERLAP,
callback_manager=callback_manager,
)
)
transformations = transformations or [node_parser]
llama_logger = llama_logger or LlamaLogger()
return cls(
llm_predictor=llm_predictor,
embed_model=embed_model,
prompt_helper=prompt_helper,
transformations=transformations,
llama_logger=llama_logger, # deprecated
callback_manager=callback_manager,
)
@classmethod
def from_service_context(
cls,
service_context: "ServiceContext",
llm_predictor: Optional[BaseLLMPredictor] = None,
llm: Optional[LLMType] = "default",
prompt_helper: Optional[PromptHelper] = None,
embed_model: Optional[Any] = "default",
node_parser: Optional[NodeParser] = None,
text_splitter: Optional[TextSplitter] = None,
transformations: Optional[List[TransformComponent]] = None,
llama_logger: Optional[LlamaLogger] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
query_wrapper_prompt: Optional[BasePromptTemplate] = None,
# node parser kwargs
chunk_size: Optional[int] = None,
chunk_overlap: Optional[int] = None,
# prompt helper kwargs
context_window: Optional[int] = None,
num_output: Optional[int] = None,
# deprecated kwargs
chunk_size_limit: Optional[int] = None,
) -> "ServiceContext":
"""Instantiate a new service context using a previous as the defaults."""
from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model
embed_model = cast(EmbedType, embed_model)
if chunk_size_limit is not None and chunk_size is None:
logger.warning(
"chunk_size_limit is deprecated, please specify chunk_size",
DeprecationWarning,
)
chunk_size = chunk_size_limit
callback_manager = callback_manager or service_context.callback_manager
if llm != "default":
if llm_predictor is not None:
raise ValueError("Cannot specify both llm and llm_predictor")
llm = resolve_llm(llm)
llm_predictor = LLMPredictor(llm=llm)
llm_predictor = llm_predictor or service_context.llm_predictor
if isinstance(llm_predictor, LLMPredictor):
llm_predictor.llm.callback_manager = callback_manager
if system_prompt:
llm_predictor.system_prompt = system_prompt
if query_wrapper_prompt:
llm_predictor.query_wrapper_prompt = query_wrapper_prompt
# NOTE: the embed_model isn't used in all indices
# default to using the embed model passed from the service context
if embed_model == "default":
embed_model = service_context.embed_model
embed_model = resolve_embed_model(embed_model)
embed_model.callback_manager = callback_manager
prompt_helper = prompt_helper or service_context.prompt_helper
if context_window is not None or num_output is not None:
prompt_helper = _get_default_prompt_helper(
llm_metadata=llm_predictor.metadata,
context_window=context_window,
num_output=num_output,
)
transformations = transformations or []
node_parser_found = False
for transform in service_context.transformations:
if isinstance(transform, NodeParser):
node_parser_found = True
node_parser = transform
break
if text_splitter is not None and node_parser is not None:
raise ValueError("Cannot specify both text_splitter and node_parser")
if not node_parser_found:
node_parser = (
text_splitter # text splitter extends node parser
or node_parser
or _get_default_node_parser(
chunk_size=chunk_size or DEFAULT_CHUNK_SIZE,
chunk_overlap=chunk_overlap or SENTENCE_CHUNK_OVERLAP,
callback_manager=callback_manager,
)
)
transformations = transformations or service_context.transformations
llama_logger = llama_logger or service_context.llama_logger
return cls(
llm_predictor=llm_predictor,
embed_model=embed_model,
prompt_helper=prompt_helper,
transformations=transformations,
llama_logger=llama_logger, # deprecated
callback_manager=callback_manager,
)
@property
def llm(self) -> LLM:
return self.llm_predictor.llm
@property
def node_parser(self) -> NodeParser:
"""Get the node parser."""
for transform in self.transformations:
if isinstance(transform, NodeParser):
return transform
raise ValueError("No node parser found.")
def to_dict(self) -> dict:
"""Convert service context to dict."""
llm_dict = self.llm_predictor.llm.to_dict()
llm_predictor_dict = self.llm_predictor.to_dict()
embed_model_dict = self.embed_model.to_dict()
prompt_helper_dict = self.prompt_helper.to_dict()
tranform_list_dict = [x.to_dict() for x in self.transformations]
return ServiceContextData(
llm=llm_dict,
llm_predictor=llm_predictor_dict,
prompt_helper=prompt_helper_dict,
embed_model=embed_model_dict,
transformations=tranform_list_dict,
).dict()
@classmethod
def from_dict(cls, data: dict) -> "ServiceContext":
from llama_index.core.embeddings.loading import load_embed_model
from llama_index.core.extractors.loading import load_extractor
from llama_index.core.node_parser.loading import load_parser
from llama_index.core.service_context_elements.llm_predictor import (
load_predictor,
)
service_context_data = ServiceContextData.parse_obj(data)
llm_predictor = load_predictor(service_context_data.llm_predictor)
embed_model = load_embed_model(service_context_data.embed_model)
prompt_helper = PromptHelper.from_dict(service_context_data.prompt_helper)
transformations: List[TransformComponent] = []
for transform in service_context_data.transformations:
try:
transformations.append(load_parser(transform))
except ValueError:
transformations.append(load_extractor(transform))
return cls.from_defaults(
llm_predictor=llm_predictor,
prompt_helper=prompt_helper,
embed_model=embed_model,
transformations=transformations,
)
def set_global_service_context(service_context: Optional[ServiceContext]) -> None:
"""Helper function to set the global service context."""
llama_index.core.global_service_context = service_context
if service_context is not None:
from llama_index.core.settings import Settings
Settings.llm = service_context.llm
Settings.embed_model = service_context.embed_model
Settings.prompt_helper = service_context.prompt_helper
Settings.transformations = service_context.transformations
Settings.node_parser = service_context.node_parser
Settings.callback_manager = service_context.callback_manager
| [
"llama_index.core.embeddings.utils.resolve_embed_model",
"llama_index.core.node_parser.loading.load_parser",
"llama_index.core.extractors.loading.load_extractor",
"llama_index.core.callbacks.base.CallbackManager",
"llama_index.core.indices.prompt_helper.PromptHelper.from_llm_metadata",
"llama_index.core.service_context_elements.llm_predictor.load_predictor",
"llama_index.core.service_context_elements.llama_logger.LlamaLogger",
"llama_index.core.llms.utils.resolve_llm",
"llama_index.core.service_context_elements.llm_predictor.LLMPredictor",
"llama_index.core.embeddings.loading.load_embed_model",
"llama_index.core.indices.prompt_helper.PromptHelper.from_dict"
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import logging
from dataclasses import dataclass
from typing import Any, List, Optional, cast
from deprecated import deprecated
import llama_index.core
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.indices.prompt_helper import PromptHelper
from llama_index.core.service_context_elements.llm_predictor import (
LLMPredictor,
BaseLLMPredictor,
)
from llama_index.core.base.llms.types import LLMMetadata
from llama_index.core.llms.llm import LLM
from llama_index.core.llms.utils import LLMType, resolve_llm
from llama_index.core.service_context_elements.llama_logger import LlamaLogger
from llama_index.core.node_parser.interface import NodeParser, TextSplitter
from llama_index.core.node_parser.text.sentence import (
DEFAULT_CHUNK_SIZE,
SENTENCE_CHUNK_OVERLAP,
SentenceSplitter,
)
from llama_index.core.prompts.base import BasePromptTemplate
from llama_index.core.schema import TransformComponent
from llama_index.core.types import PydanticProgramMode
logger = logging.getLogger(__name__)
def _get_default_node_parser(
chunk_size: int = DEFAULT_CHUNK_SIZE,
chunk_overlap: int = SENTENCE_CHUNK_OVERLAP,
callback_manager: Optional[CallbackManager] = None,
) -> NodeParser:
"""Get default node parser."""
return SentenceSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
callback_manager=callback_manager or CallbackManager(),
)
def _get_default_prompt_helper(
llm_metadata: LLMMetadata,
context_window: Optional[int] = None,
num_output: Optional[int] = None,
) -> PromptHelper:
"""Get default prompt helper."""
if context_window is not None:
llm_metadata.context_window = context_window
if num_output is not None:
llm_metadata.num_output = num_output
return PromptHelper.from_llm_metadata(llm_metadata=llm_metadata)
class ServiceContextData(BaseModel):
llm: dict
llm_predictor: dict
prompt_helper: dict
embed_model: dict
transformations: List[dict]
@dataclass
class ServiceContext:
"""Service Context container.
The service context container is a utility container for LlamaIndex
index and query classes. It contains the following:
- llm_predictor: BaseLLMPredictor
- prompt_helper: PromptHelper
- embed_model: BaseEmbedding
- node_parser: NodeParser
- llama_logger: LlamaLogger (deprecated)
- callback_manager: CallbackManager
"""
llm_predictor: BaseLLMPredictor
prompt_helper: PromptHelper
embed_model: BaseEmbedding
transformations: List[TransformComponent]
llama_logger: LlamaLogger
callback_manager: CallbackManager
@classmethod
@deprecated(
version="0.10.0",
reason="ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.",
)
def from_defaults(
cls,
llm_predictor: Optional[BaseLLMPredictor] = None,
llm: Optional[LLMType] = "default",
prompt_helper: Optional[PromptHelper] = None,
embed_model: Optional[Any] = "default",
node_parser: Optional[NodeParser] = None,
text_splitter: Optional[TextSplitter] = None,
transformations: Optional[List[TransformComponent]] = None,
llama_logger: Optional[LlamaLogger] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
query_wrapper_prompt: Optional[BasePromptTemplate] = None,
# pydantic program mode (used if output_cls is specified)
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
# node parser kwargs
chunk_size: Optional[int] = None,
chunk_overlap: Optional[int] = None,
# prompt helper kwargs
context_window: Optional[int] = None,
num_output: Optional[int] = None,
# deprecated kwargs
chunk_size_limit: Optional[int] = None,
) -> "ServiceContext":
"""Create a ServiceContext from defaults.
If an argument is specified, then use the argument value provided for that
parameter. If an argument is not specified, then use the default value.
You can change the base defaults by setting llama_index.global_service_context
to a ServiceContext object with your desired settings.
Args:
llm_predictor (Optional[BaseLLMPredictor]): LLMPredictor
prompt_helper (Optional[PromptHelper]): PromptHelper
embed_model (Optional[BaseEmbedding]): BaseEmbedding
or "local" (use local model)
node_parser (Optional[NodeParser]): NodeParser
llama_logger (Optional[LlamaLogger]): LlamaLogger (deprecated)
chunk_size (Optional[int]): chunk_size
callback_manager (Optional[CallbackManager]): CallbackManager
system_prompt (Optional[str]): System-wide prompt to be prepended
to all input prompts, used to guide system "decision making"
query_wrapper_prompt (Optional[BasePromptTemplate]): A format to wrap
passed-in input queries.
Deprecated Args:
chunk_size_limit (Optional[int]): renamed to chunk_size
"""
from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model
embed_model = cast(EmbedType, embed_model)
if chunk_size_limit is not None and chunk_size is None:
logger.warning(
"chunk_size_limit is deprecated, please specify chunk_size instead"
)
chunk_size = chunk_size_limit
if llama_index.core.global_service_context is not None:
return cls.from_service_context(
llama_index.core.global_service_context,
llm=llm,
llm_predictor=llm_predictor,
prompt_helper=prompt_helper,
embed_model=embed_model,
node_parser=node_parser,
text_splitter=text_splitter,
llama_logger=llama_logger,
callback_manager=callback_manager,
context_window=context_window,
chunk_size=chunk_size,
chunk_size_limit=chunk_size_limit,
chunk_overlap=chunk_overlap,
num_output=num_output,
system_prompt=system_prompt,
query_wrapper_prompt=query_wrapper_prompt,
transformations=transformations,
)
callback_manager = callback_manager or CallbackManager([])
if llm != "default":
if llm_predictor is not None:
raise ValueError("Cannot specify both llm and llm_predictor")
llm = resolve_llm(llm)
llm.system_prompt = llm.system_prompt or system_prompt
llm.query_wrapper_prompt = llm.query_wrapper_prompt or query_wrapper_prompt
llm.pydantic_program_mode = (
llm.pydantic_program_mode or pydantic_program_mode
)
if llm_predictor is not None:
print("LLMPredictor is deprecated, please use LLM instead.")
llm_predictor = llm_predictor or LLMPredictor(
llm=llm, pydantic_program_mode=pydantic_program_mode
)
if isinstance(llm_predictor, LLMPredictor):
llm_predictor.llm.callback_manager = callback_manager
if system_prompt:
llm_predictor.system_prompt = system_prompt
if query_wrapper_prompt:
llm_predictor.query_wrapper_prompt = query_wrapper_prompt
# NOTE: the embed_model isn't used in all indices
# NOTE: embed model should be a transformation, but the way the service
# context works, we can't put in there yet.
embed_model = resolve_embed_model(embed_model)
embed_model.callback_manager = callback_manager
prompt_helper = prompt_helper or _get_default_prompt_helper(
llm_metadata=llm_predictor.metadata,
context_window=context_window,
num_output=num_output,
)
if text_splitter is not None and node_parser is not None:
raise ValueError("Cannot specify both text_splitter and node_parser")
node_parser = (
text_splitter # text splitter extends node parser
or node_parser
or _get_default_node_parser(
chunk_size=chunk_size or DEFAULT_CHUNK_SIZE,
chunk_overlap=chunk_overlap or SENTENCE_CHUNK_OVERLAP,
callback_manager=callback_manager,
)
)
transformations = transformations or [node_parser]
llama_logger = llama_logger or LlamaLogger()
return cls(
llm_predictor=llm_predictor,
embed_model=embed_model,
prompt_helper=prompt_helper,
transformations=transformations,
llama_logger=llama_logger, # deprecated
callback_manager=callback_manager,
)
@classmethod
def from_service_context(
cls,
service_context: "ServiceContext",
llm_predictor: Optional[BaseLLMPredictor] = None,
llm: Optional[LLMType] = "default",
prompt_helper: Optional[PromptHelper] = None,
embed_model: Optional[Any] = "default",
node_parser: Optional[NodeParser] = None,
text_splitter: Optional[TextSplitter] = None,
transformations: Optional[List[TransformComponent]] = None,
llama_logger: Optional[LlamaLogger] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
query_wrapper_prompt: Optional[BasePromptTemplate] = None,
# node parser kwargs
chunk_size: Optional[int] = None,
chunk_overlap: Optional[int] = None,
# prompt helper kwargs
context_window: Optional[int] = None,
num_output: Optional[int] = None,
# deprecated kwargs
chunk_size_limit: Optional[int] = None,
) -> "ServiceContext":
"""Instantiate a new service context using a previous as the defaults."""
from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model
embed_model = cast(EmbedType, embed_model)
if chunk_size_limit is not None and chunk_size is None:
logger.warning(
"chunk_size_limit is deprecated, please specify chunk_size",
DeprecationWarning,
)
chunk_size = chunk_size_limit
callback_manager = callback_manager or service_context.callback_manager
if llm != "default":
if llm_predictor is not None:
raise ValueError("Cannot specify both llm and llm_predictor")
llm = resolve_llm(llm)
llm_predictor = LLMPredictor(llm=llm)
llm_predictor = llm_predictor or service_context.llm_predictor
if isinstance(llm_predictor, LLMPredictor):
llm_predictor.llm.callback_manager = callback_manager
if system_prompt:
llm_predictor.system_prompt = system_prompt
if query_wrapper_prompt:
llm_predictor.query_wrapper_prompt = query_wrapper_prompt
# NOTE: the embed_model isn't used in all indices
# default to using the embed model passed from the service context
if embed_model == "default":
embed_model = service_context.embed_model
embed_model = resolve_embed_model(embed_model)
embed_model.callback_manager = callback_manager
prompt_helper = prompt_helper or service_context.prompt_helper
if context_window is not None or num_output is not None:
prompt_helper = _get_default_prompt_helper(
llm_metadata=llm_predictor.metadata,
context_window=context_window,
num_output=num_output,
)
transformations = transformations or []
node_parser_found = False
for transform in service_context.transformations:
if isinstance(transform, NodeParser):
node_parser_found = True
node_parser = transform
break
if text_splitter is not None and node_parser is not None:
raise ValueError("Cannot specify both text_splitter and node_parser")
if not node_parser_found:
node_parser = (
text_splitter # text splitter extends node parser
or node_parser
or _get_default_node_parser(
chunk_size=chunk_size or DEFAULT_CHUNK_SIZE,
chunk_overlap=chunk_overlap or SENTENCE_CHUNK_OVERLAP,
callback_manager=callback_manager,
)
)
transformations = transformations or service_context.transformations
llama_logger = llama_logger or service_context.llama_logger
return cls(
llm_predictor=llm_predictor,
embed_model=embed_model,
prompt_helper=prompt_helper,
transformations=transformations,
llama_logger=llama_logger, # deprecated
callback_manager=callback_manager,
)
@property
def llm(self) -> LLM:
return self.llm_predictor.llm
@property
def node_parser(self) -> NodeParser:
"""Get the node parser."""
for transform in self.transformations:
if isinstance(transform, NodeParser):
return transform
raise ValueError("No node parser found.")
def to_dict(self) -> dict:
"""Convert service context to dict."""
llm_dict = self.llm_predictor.llm.to_dict()
llm_predictor_dict = self.llm_predictor.to_dict()
embed_model_dict = self.embed_model.to_dict()
prompt_helper_dict = self.prompt_helper.to_dict()
tranform_list_dict = [x.to_dict() for x in self.transformations]
return ServiceContextData(
llm=llm_dict,
llm_predictor=llm_predictor_dict,
prompt_helper=prompt_helper_dict,
embed_model=embed_model_dict,
transformations=tranform_list_dict,
).dict()
@classmethod
def from_dict(cls, data: dict) -> "ServiceContext":
from llama_index.core.embeddings.loading import load_embed_model
from llama_index.core.extractors.loading import load_extractor
from llama_index.core.node_parser.loading import load_parser
from llama_index.core.service_context_elements.llm_predictor import (
load_predictor,
)
service_context_data = ServiceContextData.parse_obj(data)
llm_predictor = load_predictor(service_context_data.llm_predictor)
embed_model = load_embed_model(service_context_data.embed_model)
prompt_helper = PromptHelper.from_dict(service_context_data.prompt_helper)
transformations: List[TransformComponent] = []
for transform in service_context_data.transformations:
try:
transformations.append(load_parser(transform))
except ValueError:
transformations.append(load_extractor(transform))
return cls.from_defaults(
llm_predictor=llm_predictor,
prompt_helper=prompt_helper,
embed_model=embed_model,
transformations=transformations,
)
def set_global_service_context(service_context: Optional[ServiceContext]) -> None:
"""Helper function to set the global service context."""
llama_index.core.global_service_context = service_context
if service_context is not None:
from llama_index.core.settings import Settings
Settings.llm = service_context.llm
Settings.embed_model = service_context.embed_model
Settings.prompt_helper = service_context.prompt_helper
Settings.transformations = service_context.transformations
Settings.node_parser = service_context.node_parser
Settings.callback_manager = service_context.callback_manager
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"llama_index.core.service_context_elements.llama_logger.LlamaLogger",
"llama_index.core.llms.utils.resolve_llm",
"llama_index.core.service_context_elements.llm_predictor.LLMPredictor",
"llama_index.core.embeddings.loading.load_embed_model",
"llama_index.core.indices.prompt_helper.PromptHelper.from_dict"
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from typing import List, Optional
import fsspec
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.schema import BaseNode
from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc
from llama_index.core.storage.kvstore import (
SimpleKVStore as SimpleCache,
)
from llama_index.core.storage.kvstore.types import (
BaseKVStore as BaseCache,
)
DEFAULT_CACHE_NAME = "llama_cache"
class IngestionCache(BaseModel):
class Config:
arbitrary_types_allowed = True
nodes_key = "nodes"
collection: str = Field(
default=DEFAULT_CACHE_NAME, description="Collection name of the cache."
)
cache: BaseCache = Field(default_factory=SimpleCache, description="Cache to use.")
# TODO: add async get/put methods?
def put(
self, key: str, nodes: List[BaseNode], collection: Optional[str] = None
) -> None:
"""Put a value into the cache."""
collection = collection or self.collection
val = {self.nodes_key: [doc_to_json(node) for node in nodes]}
self.cache.put(key, val, collection=collection)
def get(
self, key: str, collection: Optional[str] = None
) -> Optional[List[BaseNode]]:
"""Get a value from the cache."""
collection = collection or self.collection
node_dicts = self.cache.get(key, collection=collection)
if node_dicts is None:
return None
return [json_to_doc(node_dict) for node_dict in node_dicts[self.nodes_key]]
def clear(self, collection: Optional[str] = None) -> None:
"""Clear the cache."""
collection = collection or self.collection
data = self.cache.get_all(collection=collection)
for key in data:
self.cache.delete(key, collection=collection)
def persist(
self, persist_path: str, fs: Optional[fsspec.AbstractFileSystem] = None
) -> None:
"""Persist the cache to a directory, if possible."""
if isinstance(self.cache, SimpleCache):
self.cache.persist(persist_path, fs=fs)
else:
print("Warning: skipping persist, only needed for SimpleCache.")
@classmethod
def from_persist_path(
cls,
persist_path: str,
collection: str = DEFAULT_CACHE_NAME,
fs: Optional[fsspec.AbstractFileSystem] = None,
) -> "IngestionCache":
"""Create a IngestionCache from a persist directory."""
return cls(
collection=collection,
cache=SimpleCache.from_persist_path(persist_path, fs=fs),
)
__all__ = ["SimpleCache", "BaseCache"]
| [
"llama_index.core.storage.docstore.utils.json_to_doc",
"llama_index.core.storage.docstore.utils.doc_to_json",
"llama_index.core.storage.kvstore.SimpleKVStore.from_persist_path",
"llama_index.core.bridge.pydantic.Field"
] | [((577, 655), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default': 'DEFAULT_CACHE_NAME', 'description': '"""Collection name of the cache."""'}), "(default=DEFAULT_CACHE_NAME, description='Collection name of the cache.')\n", (582, 655), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field\n'), ((693, 756), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'SimpleCache', 'description': '"""Cache to use."""'}), "(default_factory=SimpleCache, description='Cache to use.')\n", (698, 756), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field\n'), ((1461, 1483), 'llama_index.core.storage.docstore.utils.json_to_doc', 'json_to_doc', (['node_dict'], {}), '(node_dict)\n', (1472, 1483), False, 'from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc\n'), ((1031, 1048), 'llama_index.core.storage.docstore.utils.doc_to_json', 'doc_to_json', (['node'], {}), '(node)\n', (1042, 1048), False, 'from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc\n'), ((2531, 2581), 'llama_index.core.storage.kvstore.SimpleKVStore.from_persist_path', 'SimpleCache.from_persist_path', (['persist_path'], {'fs': 'fs'}), '(persist_path, fs=fs)\n', (2560, 2581), True, 'from llama_index.core.storage.kvstore import SimpleKVStore as SimpleCache\n')] |
from typing import List, Optional
import fsspec
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.schema import BaseNode
from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc
from llama_index.core.storage.kvstore import (
SimpleKVStore as SimpleCache,
)
from llama_index.core.storage.kvstore.types import (
BaseKVStore as BaseCache,
)
DEFAULT_CACHE_NAME = "llama_cache"
class IngestionCache(BaseModel):
class Config:
arbitrary_types_allowed = True
nodes_key = "nodes"
collection: str = Field(
default=DEFAULT_CACHE_NAME, description="Collection name of the cache."
)
cache: BaseCache = Field(default_factory=SimpleCache, description="Cache to use.")
# TODO: add async get/put methods?
def put(
self, key: str, nodes: List[BaseNode], collection: Optional[str] = None
) -> None:
"""Put a value into the cache."""
collection = collection or self.collection
val = {self.nodes_key: [doc_to_json(node) for node in nodes]}
self.cache.put(key, val, collection=collection)
def get(
self, key: str, collection: Optional[str] = None
) -> Optional[List[BaseNode]]:
"""Get a value from the cache."""
collection = collection or self.collection
node_dicts = self.cache.get(key, collection=collection)
if node_dicts is None:
return None
return [json_to_doc(node_dict) for node_dict in node_dicts[self.nodes_key]]
def clear(self, collection: Optional[str] = None) -> None:
"""Clear the cache."""
collection = collection or self.collection
data = self.cache.get_all(collection=collection)
for key in data:
self.cache.delete(key, collection=collection)
def persist(
self, persist_path: str, fs: Optional[fsspec.AbstractFileSystem] = None
) -> None:
"""Persist the cache to a directory, if possible."""
if isinstance(self.cache, SimpleCache):
self.cache.persist(persist_path, fs=fs)
else:
print("Warning: skipping persist, only needed for SimpleCache.")
@classmethod
def from_persist_path(
cls,
persist_path: str,
collection: str = DEFAULT_CACHE_NAME,
fs: Optional[fsspec.AbstractFileSystem] = None,
) -> "IngestionCache":
"""Create a IngestionCache from a persist directory."""
return cls(
collection=collection,
cache=SimpleCache.from_persist_path(persist_path, fs=fs),
)
__all__ = ["SimpleCache", "BaseCache"]
| [
"llama_index.core.storage.docstore.utils.json_to_doc",
"llama_index.core.storage.docstore.utils.doc_to_json",
"llama_index.core.storage.kvstore.SimpleKVStore.from_persist_path",
"llama_index.core.bridge.pydantic.Field"
] | [((577, 655), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default': 'DEFAULT_CACHE_NAME', 'description': '"""Collection name of the cache."""'}), "(default=DEFAULT_CACHE_NAME, description='Collection name of the cache.')\n", (582, 655), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field\n'), ((693, 756), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'SimpleCache', 'description': '"""Cache to use."""'}), "(default_factory=SimpleCache, description='Cache to use.')\n", (698, 756), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field\n'), ((1461, 1483), 'llama_index.core.storage.docstore.utils.json_to_doc', 'json_to_doc', (['node_dict'], {}), '(node_dict)\n', (1472, 1483), False, 'from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc\n'), ((1031, 1048), 'llama_index.core.storage.docstore.utils.doc_to_json', 'doc_to_json', (['node'], {}), '(node)\n', (1042, 1048), False, 'from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc\n'), ((2531, 2581), 'llama_index.core.storage.kvstore.SimpleKVStore.from_persist_path', 'SimpleCache.from_persist_path', (['persist_path'], {'fs': 'fs'}), '(persist_path, fs=fs)\n', (2560, 2581), True, 'from llama_index.core.storage.kvstore import SimpleKVStore as SimpleCache\n')] |
"""Base object types."""
import pickle
import warnings
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.callbacks.base import CallbackManager
from llama_index.legacy.core.base_retriever import BaseRetriever
from llama_index.legacy.core.query_pipeline.query_component import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.legacy.indices.base import BaseIndex
from llama_index.legacy.indices.vector_store.base import VectorStoreIndex
from llama_index.legacy.objects.base_node_mapping import (
DEFAULT_PERSIST_FNAME,
BaseObjectNodeMapping,
SimpleObjectNodeMapping,
)
from llama_index.legacy.schema import QueryType
from llama_index.legacy.storage.storage_context import (
DEFAULT_PERSIST_DIR,
StorageContext,
)
OT = TypeVar("OT")
class ObjectRetriever(ChainableMixin, Generic[OT]):
"""Object retriever."""
def __init__(
self, retriever: BaseRetriever, object_node_mapping: BaseObjectNodeMapping[OT]
):
self._retriever = retriever
self._object_node_mapping = object_node_mapping
@property
def retriever(self) -> BaseRetriever:
"""Retriever."""
return self._retriever
def retrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = self._retriever.retrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
async def aretrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = await self._retriever.aretrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
return ObjectRetrieverComponent(retriever=self)
class ObjectRetrieverComponent(QueryComponent):
"""Object retriever component."""
retriever: ObjectRetriever = Field(..., description="Retriever.")
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
self.retriever.retriever.callback_manager = callback_manager
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
# make sure input is a string
input["input"] = validate_and_convert_stringable(input["input"])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
output = self.retriever.retrieve(kwargs["input"])
return {"output": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component (async)."""
output = await self.retriever.aretrieve(kwargs["input"])
return {"output": output}
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys({"input"})
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"output"})
class ObjectIndex(Generic[OT]):
"""Object index."""
def __init__(
self, index: BaseIndex, object_node_mapping: BaseObjectNodeMapping
) -> None:
self._index = index
self._object_node_mapping = object_node_mapping
@classmethod
def from_objects(
cls,
objects: Sequence[OT],
object_mapping: Optional[BaseObjectNodeMapping] = None,
index_cls: Type[BaseIndex] = VectorStoreIndex,
**index_kwargs: Any,
) -> "ObjectIndex":
if object_mapping is None:
object_mapping = SimpleObjectNodeMapping.from_objects(objects)
nodes = object_mapping.to_nodes(objects)
index = index_cls(nodes, **index_kwargs)
return cls(index, object_mapping)
def insert_object(self, obj: Any) -> None:
self._object_node_mapping.add_object(obj)
node = self._object_node_mapping.to_node(obj)
self._index.insert_nodes([node])
def as_retriever(self, **kwargs: Any) -> ObjectRetriever:
return ObjectRetriever(
retriever=self._index.as_retriever(**kwargs),
object_node_mapping=self._object_node_mapping,
)
def as_node_retriever(self, **kwargs: Any) -> BaseRetriever:
return self._index.as_retriever(**kwargs)
def persist(
self,
persist_dir: str = DEFAULT_PERSIST_DIR,
obj_node_mapping_fname: str = DEFAULT_PERSIST_FNAME,
) -> None:
# try to persist object node mapping
try:
self._object_node_mapping.persist(
persist_dir=persist_dir, obj_node_mapping_fname=obj_node_mapping_fname
)
except (NotImplementedError, pickle.PickleError) as err:
warnings.warn(
(
"Unable to persist ObjectNodeMapping. You will need to "
"reconstruct the same object node mapping to build this ObjectIndex"
),
stacklevel=2,
)
self._index._storage_context.persist(persist_dir=persist_dir)
@classmethod
def from_persist_dir(
cls,
persist_dir: str = DEFAULT_PERSIST_DIR,
object_node_mapping: Optional[BaseObjectNodeMapping] = None,
) -> "ObjectIndex":
from llama_index.legacy.indices import load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
index = load_index_from_storage(storage_context)
if object_node_mapping:
return cls(index=index, object_node_mapping=object_node_mapping)
else:
# try to load object_node_mapping
# assume SimpleObjectNodeMapping for simplicity as its only subclass
# that supports this method
try:
object_node_mapping = SimpleObjectNodeMapping.from_persist_dir(
persist_dir=persist_dir
)
except Exception as err:
raise Exception(
"Unable to load from persist dir. The object_node_mapping cannot be loaded."
) from err
else:
return cls(index=index, object_node_mapping=object_node_mapping)
| [
"llama_index.legacy.core.query_pipeline.query_component.OutputKeys.from_keys",
"llama_index.legacy.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects",
"llama_index.legacy.core.query_pipeline.query_component.InputKeys.from_keys",
"llama_index.legacy.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir",
"llama_index.legacy.indices.load_index_from_storage",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.core.query_pipeline.query_component.validate_and_convert_stringable",
"llama_index.legacy.storage.storage_context.StorageContext.from_defaults"
] | [((925, 938), 'typing.TypeVar', 'TypeVar', (['"""OT"""'], {}), "('OT')\n", (932, 938), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar\n'), ((2060, 2096), 'llama_index.legacy.bridge.pydantic.Field', 'Field', (['...'], {'description': '"""Retriever."""'}), "(..., description='Retriever.')\n", (2065, 2096), False, 'from llama_index.legacy.bridge.pydantic import Field\n'), ((2549, 2596), 'llama_index.legacy.core.query_pipeline.query_component.validate_and_convert_stringable', 'validate_and_convert_stringable', (["input['input']"], {}), "(input['input'])\n", (2580, 2596), False, 'from llama_index.legacy.core.query_pipeline.query_component import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3083, 3113), 'llama_index.legacy.core.query_pipeline.query_component.InputKeys.from_keys', 'InputKeys.from_keys', (["{'input'}"], {}), "({'input'})\n", (3102, 3113), False, 'from llama_index.legacy.core.query_pipeline.query_component import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((3212, 3244), 'llama_index.legacy.core.query_pipeline.query_component.OutputKeys.from_keys', 'OutputKeys.from_keys', (["{'output'}"], {}), "({'output'})\n", (3232, 3244), False, 'from llama_index.legacy.core.query_pipeline.query_component import ChainableMixin, InputKeys, OutputKeys, QueryComponent, validate_and_convert_stringable\n'), ((5600, 5653), 'llama_index.legacy.storage.storage_context.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (5628, 5653), False, 'from llama_index.legacy.storage.storage_context import DEFAULT_PERSIST_DIR, StorageContext\n'), ((5670, 5710), 'llama_index.legacy.indices.load_index_from_storage', 'load_index_from_storage', (['storage_context'], {}), '(storage_context)\n', (5693, 5710), False, 'from llama_index.legacy.indices import load_index_from_storage\n'), ((3816, 3861), 'llama_index.legacy.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects', 'SimpleObjectNodeMapping.from_objects', (['objects'], {}), '(objects)\n', (3852, 3861), False, 'from llama_index.legacy.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n'), ((4972, 5133), 'warnings.warn', 'warnings.warn', (['"""Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex"""'], {'stacklevel': '(2)'}), "(\n 'Unable to persist ObjectNodeMapping. You will need to reconstruct the same object node mapping to build this ObjectIndex'\n , stacklevel=2)\n", (4985, 5133), False, 'import warnings\n'), ((6056, 6121), 'llama_index.legacy.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir', 'SimpleObjectNodeMapping.from_persist_dir', ([], {'persist_dir': 'persist_dir'}), '(persist_dir=persist_dir)\n', (6096, 6121), False, 'from llama_index.legacy.objects.base_node_mapping import DEFAULT_PERSIST_FNAME, BaseObjectNodeMapping, SimpleObjectNodeMapping\n')] |
"""Base object types."""
import pickle
import warnings
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.callbacks.base import CallbackManager
from llama_index.legacy.core.base_retriever import BaseRetriever
from llama_index.legacy.core.query_pipeline.query_component import (
ChainableMixin,
InputKeys,
OutputKeys,
QueryComponent,
validate_and_convert_stringable,
)
from llama_index.legacy.indices.base import BaseIndex
from llama_index.legacy.indices.vector_store.base import VectorStoreIndex
from llama_index.legacy.objects.base_node_mapping import (
DEFAULT_PERSIST_FNAME,
BaseObjectNodeMapping,
SimpleObjectNodeMapping,
)
from llama_index.legacy.schema import QueryType
from llama_index.legacy.storage.storage_context import (
DEFAULT_PERSIST_DIR,
StorageContext,
)
OT = TypeVar("OT")
class ObjectRetriever(ChainableMixin, Generic[OT]):
"""Object retriever."""
def __init__(
self, retriever: BaseRetriever, object_node_mapping: BaseObjectNodeMapping[OT]
):
self._retriever = retriever
self._object_node_mapping = object_node_mapping
@property
def retriever(self) -> BaseRetriever:
"""Retriever."""
return self._retriever
def retrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = self._retriever.retrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
async def aretrieve(self, str_or_query_bundle: QueryType) -> List[OT]:
nodes = await self._retriever.aretrieve(str_or_query_bundle)
return [self._object_node_mapping.from_node(node.node) for node in nodes]
def _as_query_component(self, **kwargs: Any) -> QueryComponent:
"""As query component."""
return ObjectRetrieverComponent(retriever=self)
class ObjectRetrieverComponent(QueryComponent):
"""Object retriever component."""
retriever: ObjectRetriever = Field(..., description="Retriever.")
class Config:
arbitrary_types_allowed = True
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
self.retriever.retriever.callback_manager = callback_manager
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
# make sure input is a string
input["input"] = validate_and_convert_stringable(input["input"])
return input
def _run_component(self, **kwargs: Any) -> Any:
"""Run component."""
output = self.retriever.retrieve(kwargs["input"])
return {"output": output}
async def _arun_component(self, **kwargs: Any) -> Any:
"""Run component (async)."""
output = await self.retriever.aretrieve(kwargs["input"])
return {"output": output}
@property
def input_keys(self) -> InputKeys:
"""Input keys."""
return InputKeys.from_keys({"input"})
@property
def output_keys(self) -> OutputKeys:
"""Output keys."""
return OutputKeys.from_keys({"output"})
class ObjectIndex(Generic[OT]):
"""Object index."""
def __init__(
self, index: BaseIndex, object_node_mapping: BaseObjectNodeMapping
) -> None:
self._index = index
self._object_node_mapping = object_node_mapping
@classmethod
def from_objects(
cls,
objects: Sequence[OT],
object_mapping: Optional[BaseObjectNodeMapping] = None,
index_cls: Type[BaseIndex] = VectorStoreIndex,
**index_kwargs: Any,
) -> "ObjectIndex":
if object_mapping is None:
object_mapping = SimpleObjectNodeMapping.from_objects(objects)
nodes = object_mapping.to_nodes(objects)
index = index_cls(nodes, **index_kwargs)
return cls(index, object_mapping)
def insert_object(self, obj: Any) -> None:
self._object_node_mapping.add_object(obj)
node = self._object_node_mapping.to_node(obj)
self._index.insert_nodes([node])
def as_retriever(self, **kwargs: Any) -> ObjectRetriever:
return ObjectRetriever(
retriever=self._index.as_retriever(**kwargs),
object_node_mapping=self._object_node_mapping,
)
def as_node_retriever(self, **kwargs: Any) -> BaseRetriever:
return self._index.as_retriever(**kwargs)
def persist(
self,
persist_dir: str = DEFAULT_PERSIST_DIR,
obj_node_mapping_fname: str = DEFAULT_PERSIST_FNAME,
) -> None:
# try to persist object node mapping
try:
self._object_node_mapping.persist(
persist_dir=persist_dir, obj_node_mapping_fname=obj_node_mapping_fname
)
except (NotImplementedError, pickle.PickleError) as err:
warnings.warn(
(
"Unable to persist ObjectNodeMapping. You will need to "
"reconstruct the same object node mapping to build this ObjectIndex"
),
stacklevel=2,
)
self._index._storage_context.persist(persist_dir=persist_dir)
@classmethod
def from_persist_dir(
cls,
persist_dir: str = DEFAULT_PERSIST_DIR,
object_node_mapping: Optional[BaseObjectNodeMapping] = None,
) -> "ObjectIndex":
from llama_index.legacy.indices import load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
index = load_index_from_storage(storage_context)
if object_node_mapping:
return cls(index=index, object_node_mapping=object_node_mapping)
else:
# try to load object_node_mapping
# assume SimpleObjectNodeMapping for simplicity as its only subclass
# that supports this method
try:
object_node_mapping = SimpleObjectNodeMapping.from_persist_dir(
persist_dir=persist_dir
)
except Exception as err:
raise Exception(
"Unable to load from persist dir. The object_node_mapping cannot be loaded."
) from err
else:
return cls(index=index, object_node_mapping=object_node_mapping)
| [
"llama_index.legacy.core.query_pipeline.query_component.OutputKeys.from_keys",
"llama_index.legacy.objects.base_node_mapping.SimpleObjectNodeMapping.from_objects",
"llama_index.legacy.core.query_pipeline.query_component.InputKeys.from_keys",
"llama_index.legacy.objects.base_node_mapping.SimpleObjectNodeMapping.from_persist_dir",
"llama_index.legacy.indices.load_index_from_storage",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.core.query_pipeline.query_component.validate_and_convert_stringable",
"llama_index.legacy.storage.storage_context.StorageContext.from_defaults"
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"""Base reader class."""
from abc import ABC
from typing import TYPE_CHECKING, Any, Dict, Iterable, List
if TYPE_CHECKING:
from llama_index.legacy.bridge.langchain import Document as LCDocument
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.schema import BaseComponent, Document
class BaseReader(ABC):
"""Utilities for loading data from a directory."""
def lazy_load_data(self, *args: Any, **load_kwargs: Any) -> Iterable[Document]:
"""Load data from the input directory lazily."""
raise NotImplementedError(
f"{self.__class__.__name__} does not provide lazy_load_data method currently"
)
def load_data(self, *args: Any, **load_kwargs: Any) -> List[Document]:
"""Load data from the input directory."""
return list(self.lazy_load_data(*args, **load_kwargs))
def load_langchain_documents(self, **load_kwargs: Any) -> List["LCDocument"]:
"""Load data in LangChain document format."""
docs = self.load_data(**load_kwargs)
return [d.to_langchain_format() for d in docs]
class BasePydanticReader(BaseReader, BaseComponent):
"""Serialiable Data Loader with Pydatnic."""
is_remote: bool = Field(
default=False,
description="Whether the data is loaded from a remote API or a local file.",
)
class Config:
arbitrary_types_allowed = True
class ReaderConfig(BaseComponent):
"""Represents a reader and it's input arguments."""
reader: BasePydanticReader = Field(..., description="Reader to use.")
reader_args: List[Any] = Field(default_factory=list, description="Reader args.")
reader_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Reader kwargs."
)
class Config:
arbitrary_types_allowed = True
@classmethod
def class_name(cls) -> str:
"""Get the name identifier of the class."""
return "ReaderConfig"
def to_dict(self, **kwargs: Any) -> Dict[str, Any]:
"""Convert the class to a dictionary."""
return {
"loader": self.reader.to_dict(**kwargs),
"reader_args": self.reader_args,
"reader_kwargs": self.reader_kwargs,
"class_name": self.class_name(),
}
def read(self) -> List[Document]:
"""Call the loader with the given arguments."""
return self.reader.load_data(*self.reader_args, **self.reader_kwargs)
| [
"llama_index.legacy.bridge.pydantic.Field"
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"""Base reader class."""
from abc import ABC
from typing import TYPE_CHECKING, Any, Dict, Iterable, List
if TYPE_CHECKING:
from llama_index.legacy.bridge.langchain import Document as LCDocument
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.schema import BaseComponent, Document
class BaseReader(ABC):
"""Utilities for loading data from a directory."""
def lazy_load_data(self, *args: Any, **load_kwargs: Any) -> Iterable[Document]:
"""Load data from the input directory lazily."""
raise NotImplementedError(
f"{self.__class__.__name__} does not provide lazy_load_data method currently"
)
def load_data(self, *args: Any, **load_kwargs: Any) -> List[Document]:
"""Load data from the input directory."""
return list(self.lazy_load_data(*args, **load_kwargs))
def load_langchain_documents(self, **load_kwargs: Any) -> List["LCDocument"]:
"""Load data in LangChain document format."""
docs = self.load_data(**load_kwargs)
return [d.to_langchain_format() for d in docs]
class BasePydanticReader(BaseReader, BaseComponent):
"""Serialiable Data Loader with Pydatnic."""
is_remote: bool = Field(
default=False,
description="Whether the data is loaded from a remote API or a local file.",
)
class Config:
arbitrary_types_allowed = True
class ReaderConfig(BaseComponent):
"""Represents a reader and it's input arguments."""
reader: BasePydanticReader = Field(..., description="Reader to use.")
reader_args: List[Any] = Field(default_factory=list, description="Reader args.")
reader_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Reader kwargs."
)
class Config:
arbitrary_types_allowed = True
@classmethod
def class_name(cls) -> str:
"""Get the name identifier of the class."""
return "ReaderConfig"
def to_dict(self, **kwargs: Any) -> Dict[str, Any]:
"""Convert the class to a dictionary."""
return {
"loader": self.reader.to_dict(**kwargs),
"reader_args": self.reader_args,
"reader_kwargs": self.reader_kwargs,
"class_name": self.class_name(),
}
def read(self) -> List[Document]:
"""Call the loader with the given arguments."""
return self.reader.load_data(*self.reader_args, **self.reader_kwargs)
| [
"llama_index.legacy.bridge.pydantic.Field"
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"""
Portkey integration with Llama_index for enhanced monitoring.
"""
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence, Union, cast
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.llms.generic_utils import (
chat_to_completion_decorator,
completion_to_chat_decorator,
stream_chat_to_completion_decorator,
stream_completion_to_chat_decorator,
)
from llama_index.legacy.llms.portkey_utils import (
IMPORT_ERROR_MESSAGE,
generate_llm_metadata,
get_llm,
is_chat_model,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
if TYPE_CHECKING:
from portkey import (
LLMOptions,
Modes,
ModesLiteral,
PortkeyResponse,
)
DEFAULT_PORTKEY_MODEL = "gpt-3.5-turbo"
class Portkey(CustomLLM):
"""_summary_.
Args:
LLM (_type_): _description_
"""
mode: Optional[Union["Modes", "ModesLiteral"]] = Field(
description="The mode for using the Portkey integration"
)
model: Optional[str] = Field(default=DEFAULT_PORTKEY_MODEL)
llm: "LLMOptions" = Field(description="LLM parameter", default_factory=dict)
llms: List["LLMOptions"] = Field(description="LLM parameters", default_factory=list)
_client: Any = PrivateAttr()
def __init__(
self,
*,
mode: Union["Modes", "ModesLiteral"],
api_key: Optional[str] = None,
base_url: Optional[str] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""
Initialize a Portkey instance.
Args:
mode (Optional[Modes]): The mode for using the Portkey integration
(default: Modes.SINGLE).
api_key (Optional[str]): The API key to authenticate with Portkey.
base_url (Optional[str]): The Base url to the self hosted rubeus \
(the opensource version of portkey) or any other self hosted server.
"""
try:
import portkey
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
super().__init__(
base_url=base_url,
api_key=api_key,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
if api_key is not None:
portkey.api_key = api_key
if base_url is not None:
portkey.base_url = base_url
portkey.mode = mode
self._client = portkey
self.model = None
self.mode = mode
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return generate_llm_metadata(self.llms[0])
def add_llms(
self, llm_params: Union["LLMOptions", List["LLMOptions"]]
) -> "Portkey":
"""
Adds the specified LLM parameters to the list of LLMs. This may be used for
fallbacks or load-balancing as specified in the mode.
Args:
llm_params (Union[LLMOptions, List[LLMOptions]]): A single LLM parameter \
set or a list of LLM parameter sets. Each set should be an instance of \
LLMOptions with
the specified attributes.
> provider: Optional[ProviderTypes]
> model: str
> temperature: float
> max_tokens: Optional[int]
> max_retries: int
> trace_id: Optional[str]
> cache_status: Optional[CacheType]
> cache: Optional[bool]
> metadata: Dict[str, Any]
> weight: Optional[float]
> **kwargs : Other additional parameters that are supported by \
LLMOptions in portkey-ai
NOTE: User may choose to pass additional params as well.
Returns:
self
"""
try:
from portkey import LLMOptions
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
if isinstance(llm_params, LLMOptions):
llm_params = [llm_params]
self.llms.extend(llm_params)
if self.model is None:
self.model = self.llms[0].model
return self
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint for LLM."""
if self._is_chat_model:
complete_fn = chat_to_completion_decorator(self._chat)
else:
complete_fn = self._complete
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if self._is_chat_model:
chat_fn = self._chat
else:
chat_fn = completion_to_chat_decorator(self._complete)
return chat_fn(messages, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Completion endpoint for LLM."""
if self._is_chat_model:
complete_fn = stream_chat_to_completion_decorator(self._stream_chat)
else:
complete_fn = self._stream_complete
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self._is_chat_model:
stream_chat_fn = self._stream_chat
else:
stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete)
return stream_chat_fn(messages, **kwargs)
def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
try:
from portkey import Config, Message
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
_messages = cast(
List[Message],
[{"role": i.role.value, "content": i.content} for i in messages],
)
config = Config(llms=self.llms)
response = self._client.ChatCompletions.create(
messages=_messages, config=config
)
self.llm = self._get_llm(response)
message = response.choices[0].message
return ChatResponse(message=message, raw=response)
def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
try:
from portkey import Config
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
config = Config(llms=self.llms)
response = self._client.Completions.create(prompt=prompt, config=config)
text = response.choices[0].text
return CompletionResponse(text=text, raw=response)
def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
try:
from portkey import Config, Message
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
_messages = cast(
List[Message],
[{"role": i.role.value, "content": i.content} for i in messages],
)
config = Config(llms=self.llms)
response = self._client.ChatCompletions.create(
messages=_messages, config=config, stream=True, **kwargs
)
def gen() -> ChatResponseGen:
content = ""
function_call: Optional[dict] = {}
for resp in response:
if resp.choices is None:
continue
delta = resp.choices[0].delta
role = delta.get("role", "assistant")
content_delta = delta.get("content", "") or ""
content += content_delta
function_call_delta = delta.get("function_call", None)
if function_call_delta is not None:
if function_call is None:
function_call = function_call_delta
# ensure we do not add a blank function call
if (
function_call
and function_call.get("function_name", "") is None
):
del function_call["function_name"]
else:
function_call["arguments"] += function_call_delta["arguments"]
additional_kwargs = {}
if function_call is not None:
additional_kwargs["function_call"] = function_call
yield ChatResponse(
message=ChatMessage(
role=role,
content=content,
additional_kwargs=additional_kwargs,
),
delta=content_delta,
raw=resp,
)
return gen()
def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
try:
from portkey import Config
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
config = Config(llms=self.llms)
response = self._client.Completions.create(
prompt=prompt, config=config, stream=True, **kwargs
)
def gen() -> CompletionResponseGen:
text = ""
for resp in response:
delta = resp.choices[0].text or ""
text += delta
yield CompletionResponse(
delta=delta,
text=text,
raw=resp,
)
return gen()
@property
def _is_chat_model(self) -> bool:
"""Check if a given model is a chat-based language model.
Returns:
bool: True if the provided model is a chat-based language model,
False otherwise.
"""
return is_chat_model(self.model or "")
def _get_llm(self, response: "PortkeyResponse") -> "LLMOptions":
return get_llm(response, self.llms)
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"""
Portkey integration with Llama_index for enhanced monitoring.
"""
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence, Union, cast
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.llms.generic_utils import (
chat_to_completion_decorator,
completion_to_chat_decorator,
stream_chat_to_completion_decorator,
stream_completion_to_chat_decorator,
)
from llama_index.legacy.llms.portkey_utils import (
IMPORT_ERROR_MESSAGE,
generate_llm_metadata,
get_llm,
is_chat_model,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
if TYPE_CHECKING:
from portkey import (
LLMOptions,
Modes,
ModesLiteral,
PortkeyResponse,
)
DEFAULT_PORTKEY_MODEL = "gpt-3.5-turbo"
class Portkey(CustomLLM):
"""_summary_.
Args:
LLM (_type_): _description_
"""
mode: Optional[Union["Modes", "ModesLiteral"]] = Field(
description="The mode for using the Portkey integration"
)
model: Optional[str] = Field(default=DEFAULT_PORTKEY_MODEL)
llm: "LLMOptions" = Field(description="LLM parameter", default_factory=dict)
llms: List["LLMOptions"] = Field(description="LLM parameters", default_factory=list)
_client: Any = PrivateAttr()
def __init__(
self,
*,
mode: Union["Modes", "ModesLiteral"],
api_key: Optional[str] = None,
base_url: Optional[str] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""
Initialize a Portkey instance.
Args:
mode (Optional[Modes]): The mode for using the Portkey integration
(default: Modes.SINGLE).
api_key (Optional[str]): The API key to authenticate with Portkey.
base_url (Optional[str]): The Base url to the self hosted rubeus \
(the opensource version of portkey) or any other self hosted server.
"""
try:
import portkey
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
super().__init__(
base_url=base_url,
api_key=api_key,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
if api_key is not None:
portkey.api_key = api_key
if base_url is not None:
portkey.base_url = base_url
portkey.mode = mode
self._client = portkey
self.model = None
self.mode = mode
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return generate_llm_metadata(self.llms[0])
def add_llms(
self, llm_params: Union["LLMOptions", List["LLMOptions"]]
) -> "Portkey":
"""
Adds the specified LLM parameters to the list of LLMs. This may be used for
fallbacks or load-balancing as specified in the mode.
Args:
llm_params (Union[LLMOptions, List[LLMOptions]]): A single LLM parameter \
set or a list of LLM parameter sets. Each set should be an instance of \
LLMOptions with
the specified attributes.
> provider: Optional[ProviderTypes]
> model: str
> temperature: float
> max_tokens: Optional[int]
> max_retries: int
> trace_id: Optional[str]
> cache_status: Optional[CacheType]
> cache: Optional[bool]
> metadata: Dict[str, Any]
> weight: Optional[float]
> **kwargs : Other additional parameters that are supported by \
LLMOptions in portkey-ai
NOTE: User may choose to pass additional params as well.
Returns:
self
"""
try:
from portkey import LLMOptions
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
if isinstance(llm_params, LLMOptions):
llm_params = [llm_params]
self.llms.extend(llm_params)
if self.model is None:
self.model = self.llms[0].model
return self
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint for LLM."""
if self._is_chat_model:
complete_fn = chat_to_completion_decorator(self._chat)
else:
complete_fn = self._complete
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if self._is_chat_model:
chat_fn = self._chat
else:
chat_fn = completion_to_chat_decorator(self._complete)
return chat_fn(messages, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Completion endpoint for LLM."""
if self._is_chat_model:
complete_fn = stream_chat_to_completion_decorator(self._stream_chat)
else:
complete_fn = self._stream_complete
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self._is_chat_model:
stream_chat_fn = self._stream_chat
else:
stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete)
return stream_chat_fn(messages, **kwargs)
def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
try:
from portkey import Config, Message
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
_messages = cast(
List[Message],
[{"role": i.role.value, "content": i.content} for i in messages],
)
config = Config(llms=self.llms)
response = self._client.ChatCompletions.create(
messages=_messages, config=config
)
self.llm = self._get_llm(response)
message = response.choices[0].message
return ChatResponse(message=message, raw=response)
def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
try:
from portkey import Config
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
config = Config(llms=self.llms)
response = self._client.Completions.create(prompt=prompt, config=config)
text = response.choices[0].text
return CompletionResponse(text=text, raw=response)
def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
try:
from portkey import Config, Message
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
_messages = cast(
List[Message],
[{"role": i.role.value, "content": i.content} for i in messages],
)
config = Config(llms=self.llms)
response = self._client.ChatCompletions.create(
messages=_messages, config=config, stream=True, **kwargs
)
def gen() -> ChatResponseGen:
content = ""
function_call: Optional[dict] = {}
for resp in response:
if resp.choices is None:
continue
delta = resp.choices[0].delta
role = delta.get("role", "assistant")
content_delta = delta.get("content", "") or ""
content += content_delta
function_call_delta = delta.get("function_call", None)
if function_call_delta is not None:
if function_call is None:
function_call = function_call_delta
# ensure we do not add a blank function call
if (
function_call
and function_call.get("function_name", "") is None
):
del function_call["function_name"]
else:
function_call["arguments"] += function_call_delta["arguments"]
additional_kwargs = {}
if function_call is not None:
additional_kwargs["function_call"] = function_call
yield ChatResponse(
message=ChatMessage(
role=role,
content=content,
additional_kwargs=additional_kwargs,
),
delta=content_delta,
raw=resp,
)
return gen()
def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
try:
from portkey import Config
except ImportError as exc:
raise ImportError(IMPORT_ERROR_MESSAGE) from exc
config = Config(llms=self.llms)
response = self._client.Completions.create(
prompt=prompt, config=config, stream=True, **kwargs
)
def gen() -> CompletionResponseGen:
text = ""
for resp in response:
delta = resp.choices[0].text or ""
text += delta
yield CompletionResponse(
delta=delta,
text=text,
raw=resp,
)
return gen()
@property
def _is_chat_model(self) -> bool:
"""Check if a given model is a chat-based language model.
Returns:
bool: True if the provided model is a chat-based language model,
False otherwise.
"""
return is_chat_model(self.model or "")
def _get_llm(self, response: "PortkeyResponse") -> "LLMOptions":
return get_llm(response, self.llms)
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"""Query plan tool."""
from typing import Any, Dict, List, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.response_synthesizers import (
BaseSynthesizer,
get_response_synthesizer,
)
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core.tools.types import BaseTool, ToolMetadata, ToolOutput
from llama_index.core.utils import print_text
DEFAULT_NAME = "query_plan_tool"
QUERYNODE_QUERY_STR_DESC = """\
Question we are asking. This is the query string that will be executed. \
"""
QUERYNODE_TOOL_NAME_DESC = """\
Name of the tool to execute the `query_str`. \
Should NOT be specified if there are subquestions to be specified, in which \
case child_nodes should be nonempty instead.\
"""
QUERYNODE_DEPENDENCIES_DESC = """\
List of sub-questions that need to be answered in order \
to answer the question given by `query_str`.\
Should be blank if there are no sub-questions to be specified, in which case \
`tool_name` is specified.\
"""
class QueryNode(BaseModel):
"""Query node.
A query node represents a query (query_str) that must be answered.
It can either be answered by a tool (tool_name), or by a list of child nodes
(child_nodes).
The tool_name and child_nodes fields are mutually exclusive.
"""
# NOTE: inspired from https://github.com/jxnl/openai_function_call/pull/3/files
id: int = Field(..., description="ID of the query node.")
query_str: str = Field(..., description=QUERYNODE_QUERY_STR_DESC)
tool_name: Optional[str] = Field(
default=None, description="Name of the tool to execute the `query_str`."
)
dependencies: List[int] = Field(
default_factory=list, description=QUERYNODE_DEPENDENCIES_DESC
)
class QueryPlan(BaseModel):
"""Query plan.
Contains a list of QueryNode objects (which is a recursive object).
Out of the list of QueryNode objects, one of them must be the root node.
The root node is the one that isn't a dependency of any other node.
"""
nodes: List[QueryNode] = Field(
...,
description="The original question we are asking.",
)
DEFAULT_DESCRIPTION_PREFIX = """\
This is a query plan tool that takes in a list of tools and executes a \
query plan over these tools to answer a query. The query plan is a DAG of query nodes.
Given a list of tool names and the query plan schema, you \
can choose to generate a query plan to answer a question.
The tool names and descriptions are as follows:
"""
class QueryPlanTool(BaseTool):
"""Query plan tool.
A tool that takes in a list of tools and executes a query plan.
"""
def __init__(
self,
query_engine_tools: List[BaseTool],
response_synthesizer: BaseSynthesizer,
name: str,
description_prefix: str,
) -> None:
"""Initialize."""
self._query_tools_dict = {t.metadata.name: t for t in query_engine_tools}
self._response_synthesizer = response_synthesizer
self._name = name
self._description_prefix = description_prefix
@classmethod
def from_defaults(
cls,
query_engine_tools: List[BaseTool],
response_synthesizer: Optional[BaseSynthesizer] = None,
name: Optional[str] = None,
description_prefix: Optional[str] = None,
) -> "QueryPlanTool":
"""Initialize from defaults."""
name = name or DEFAULT_NAME
description_prefix = description_prefix or DEFAULT_DESCRIPTION_PREFIX
response_synthesizer = response_synthesizer or get_response_synthesizer()
return cls(
query_engine_tools=query_engine_tools,
response_synthesizer=response_synthesizer,
name=name,
description_prefix=description_prefix,
)
@property
def metadata(self) -> ToolMetadata:
"""Metadata."""
tools_description = "\n\n".join(
[
f"Tool Name: {tool.metadata.name}\n"
+ f"Tool Description: {tool.metadata.description} "
for tool in self._query_tools_dict.values()
]
)
# TODO: fill in description with query engine tools.
description = f"""\
{self._description_prefix}\n\n
{tools_description}
"""
return ToolMetadata(description, self._name, fn_schema=QueryPlan)
def _execute_node(
self, node: QueryNode, nodes_dict: Dict[int, QueryNode]
) -> ToolOutput:
"""Execute node."""
print_text(f"Executing node {node.json()}\n", color="blue")
if len(node.dependencies) > 0:
print_text(
f"Executing {len(node.dependencies)} child nodes\n", color="pink"
)
child_query_nodes: List[QueryNode] = [
nodes_dict[dep] for dep in node.dependencies
]
# execute the child nodes first
child_responses: List[ToolOutput] = [
self._execute_node(child, nodes_dict) for child in child_query_nodes
]
# form the child Node/NodeWithScore objects
child_nodes = []
for child_query_node, child_response in zip(
child_query_nodes, child_responses
):
node_text = (
f"Query: {child_query_node.query_str}\n"
f"Response: {child_response!s}\n"
)
child_node = TextNode(text=node_text)
child_nodes.append(child_node)
# use response synthesizer to combine results
child_nodes_with_scores = [
NodeWithScore(node=n, score=1.0) for n in child_nodes
]
response_obj = self._response_synthesizer.synthesize(
query=node.query_str,
nodes=child_nodes_with_scores,
)
response = ToolOutput(
content=str(response_obj),
tool_name=node.query_str,
raw_input={"query": node.query_str},
raw_output=response_obj,
)
else:
# this is a leaf request, execute the query string using the specified tool
tool = self._query_tools_dict[node.tool_name]
print_text(f"Selected Tool: {tool.metadata}\n", color="pink")
response = tool(node.query_str)
print_text(
"Executed query, got response.\n"
f"Query: {node.query_str}\n"
f"Response: {response!s}\n",
color="blue",
)
return response
def _find_root_nodes(self, nodes_dict: Dict[int, QueryNode]) -> List[QueryNode]:
"""Find root node."""
# the root node is the one that isn't a dependency of any other node
node_counts = {node_id: 0 for node_id in nodes_dict}
for node in nodes_dict.values():
for dep in node.dependencies:
node_counts[dep] += 1
root_node_ids = [
node_id for node_id, count in node_counts.items() if count == 0
]
return [nodes_dict[node_id] for node_id in root_node_ids]
def __call__(self, *args: Any, **kwargs: Any) -> ToolOutput:
"""Call."""
# the kwargs represented as a JSON object
# should be a QueryPlan object
query_plan = QueryPlan(**kwargs)
nodes_dict = {node.id: node for node in query_plan.nodes}
root_nodes = self._find_root_nodes(nodes_dict)
if len(root_nodes) > 1:
raise ValueError("Query plan should have exactly one root node.")
return self._execute_node(root_nodes[0], nodes_dict)
| [
"llama_index.core.tools.types.ToolMetadata",
"llama_index.core.utils.print_text",
"llama_index.core.response_synthesizers.get_response_synthesizer",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.schema.TextNode",
"llama_index.core.schema.NodeWithScore"
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"""Query plan tool."""
from typing import Any, Dict, List, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.response_synthesizers import (
BaseSynthesizer,
get_response_synthesizer,
)
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core.tools.types import BaseTool, ToolMetadata, ToolOutput
from llama_index.core.utils import print_text
DEFAULT_NAME = "query_plan_tool"
QUERYNODE_QUERY_STR_DESC = """\
Question we are asking. This is the query string that will be executed. \
"""
QUERYNODE_TOOL_NAME_DESC = """\
Name of the tool to execute the `query_str`. \
Should NOT be specified if there are subquestions to be specified, in which \
case child_nodes should be nonempty instead.\
"""
QUERYNODE_DEPENDENCIES_DESC = """\
List of sub-questions that need to be answered in order \
to answer the question given by `query_str`.\
Should be blank if there are no sub-questions to be specified, in which case \
`tool_name` is specified.\
"""
class QueryNode(BaseModel):
"""Query node.
A query node represents a query (query_str) that must be answered.
It can either be answered by a tool (tool_name), or by a list of child nodes
(child_nodes).
The tool_name and child_nodes fields are mutually exclusive.
"""
# NOTE: inspired from https://github.com/jxnl/openai_function_call/pull/3/files
id: int = Field(..., description="ID of the query node.")
query_str: str = Field(..., description=QUERYNODE_QUERY_STR_DESC)
tool_name: Optional[str] = Field(
default=None, description="Name of the tool to execute the `query_str`."
)
dependencies: List[int] = Field(
default_factory=list, description=QUERYNODE_DEPENDENCIES_DESC
)
class QueryPlan(BaseModel):
"""Query plan.
Contains a list of QueryNode objects (which is a recursive object).
Out of the list of QueryNode objects, one of them must be the root node.
The root node is the one that isn't a dependency of any other node.
"""
nodes: List[QueryNode] = Field(
...,
description="The original question we are asking.",
)
DEFAULT_DESCRIPTION_PREFIX = """\
This is a query plan tool that takes in a list of tools and executes a \
query plan over these tools to answer a query. The query plan is a DAG of query nodes.
Given a list of tool names and the query plan schema, you \
can choose to generate a query plan to answer a question.
The tool names and descriptions are as follows:
"""
class QueryPlanTool(BaseTool):
"""Query plan tool.
A tool that takes in a list of tools and executes a query plan.
"""
def __init__(
self,
query_engine_tools: List[BaseTool],
response_synthesizer: BaseSynthesizer,
name: str,
description_prefix: str,
) -> None:
"""Initialize."""
self._query_tools_dict = {t.metadata.name: t for t in query_engine_tools}
self._response_synthesizer = response_synthesizer
self._name = name
self._description_prefix = description_prefix
@classmethod
def from_defaults(
cls,
query_engine_tools: List[BaseTool],
response_synthesizer: Optional[BaseSynthesizer] = None,
name: Optional[str] = None,
description_prefix: Optional[str] = None,
) -> "QueryPlanTool":
"""Initialize from defaults."""
name = name or DEFAULT_NAME
description_prefix = description_prefix or DEFAULT_DESCRIPTION_PREFIX
response_synthesizer = response_synthesizer or get_response_synthesizer()
return cls(
query_engine_tools=query_engine_tools,
response_synthesizer=response_synthesizer,
name=name,
description_prefix=description_prefix,
)
@property
def metadata(self) -> ToolMetadata:
"""Metadata."""
tools_description = "\n\n".join(
[
f"Tool Name: {tool.metadata.name}\n"
+ f"Tool Description: {tool.metadata.description} "
for tool in self._query_tools_dict.values()
]
)
# TODO: fill in description with query engine tools.
description = f"""\
{self._description_prefix}\n\n
{tools_description}
"""
return ToolMetadata(description, self._name, fn_schema=QueryPlan)
def _execute_node(
self, node: QueryNode, nodes_dict: Dict[int, QueryNode]
) -> ToolOutput:
"""Execute node."""
print_text(f"Executing node {node.json()}\n", color="blue")
if len(node.dependencies) > 0:
print_text(
f"Executing {len(node.dependencies)} child nodes\n", color="pink"
)
child_query_nodes: List[QueryNode] = [
nodes_dict[dep] for dep in node.dependencies
]
# execute the child nodes first
child_responses: List[ToolOutput] = [
self._execute_node(child, nodes_dict) for child in child_query_nodes
]
# form the child Node/NodeWithScore objects
child_nodes = []
for child_query_node, child_response in zip(
child_query_nodes, child_responses
):
node_text = (
f"Query: {child_query_node.query_str}\n"
f"Response: {child_response!s}\n"
)
child_node = TextNode(text=node_text)
child_nodes.append(child_node)
# use response synthesizer to combine results
child_nodes_with_scores = [
NodeWithScore(node=n, score=1.0) for n in child_nodes
]
response_obj = self._response_synthesizer.synthesize(
query=node.query_str,
nodes=child_nodes_with_scores,
)
response = ToolOutput(
content=str(response_obj),
tool_name=node.query_str,
raw_input={"query": node.query_str},
raw_output=response_obj,
)
else:
# this is a leaf request, execute the query string using the specified tool
tool = self._query_tools_dict[node.tool_name]
print_text(f"Selected Tool: {tool.metadata}\n", color="pink")
response = tool(node.query_str)
print_text(
"Executed query, got response.\n"
f"Query: {node.query_str}\n"
f"Response: {response!s}\n",
color="blue",
)
return response
def _find_root_nodes(self, nodes_dict: Dict[int, QueryNode]) -> List[QueryNode]:
"""Find root node."""
# the root node is the one that isn't a dependency of any other node
node_counts = {node_id: 0 for node_id in nodes_dict}
for node in nodes_dict.values():
for dep in node.dependencies:
node_counts[dep] += 1
root_node_ids = [
node_id for node_id, count in node_counts.items() if count == 0
]
return [nodes_dict[node_id] for node_id in root_node_ids]
def __call__(self, *args: Any, **kwargs: Any) -> ToolOutput:
"""Call."""
# the kwargs represented as a JSON object
# should be a QueryPlan object
query_plan = QueryPlan(**kwargs)
nodes_dict = {node.id: node for node in query_plan.nodes}
root_nodes = self._find_root_nodes(nodes_dict)
if len(root_nodes) > 1:
raise ValueError("Query plan should have exactly one root node.")
return self._execute_node(root_nodes[0], nodes_dict)
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"llama_index.core.utils.print_text",
"llama_index.core.response_synthesizers.get_response_synthesizer",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.schema.TextNode",
"llama_index.core.schema.NodeWithScore"
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from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.generic_utils import (
completion_to_chat_decorator,
stream_completion_to_chat_decorator,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.watsonx_utils import (
WATSONX_MODELS,
get_from_param_or_env_without_error,
watsonx_model_to_context_size,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class WatsonX(LLM):
"""IBM WatsonX LLM."""
model_id: str = Field(description="The Model to use.")
max_new_tokens: int = Field(description="The maximum number of tokens to generate.")
temperature: float = Field(description="The temperature to use for sampling.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional Kwargs for the WatsonX model"
)
model_info: Dict[str, Any] = Field(
default_factory=dict, description="Details about the selected model"
)
_model = PrivateAttr()
def __init__(
self,
credentials: Dict[str, Any],
model_id: Optional[str] = "ibm/mpt-7b-instruct2",
project_id: Optional[str] = None,
space_id: Optional[str] = None,
max_new_tokens: Optional[int] = 512,
temperature: Optional[float] = 0.1,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""Initialize params."""
if model_id not in WATSONX_MODELS:
raise ValueError(
f"Model name {model_id} not found in {WATSONX_MODELS.keys()}"
)
try:
from ibm_watson_machine_learning.foundation_models.model import Model
except ImportError as e:
raise ImportError(
"You must install the `ibm_watson_machine_learning` package to use WatsonX"
"please `pip install ibm_watson_machine_learning`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
project_id = get_from_param_or_env_without_error(
project_id, "IBM_WATSONX_PROJECT_ID"
)
space_id = get_from_param_or_env_without_error(space_id, "IBM_WATSONX_SPACE_ID")
if project_id is not None or space_id is not None:
self._model = Model(
model_id=model_id,
credentials=credentials,
project_id=project_id,
space_id=space_id,
)
else:
raise ValueError(
f"Did not find `project_id` or `space_id`, Please pass them as named parameters"
f" or as environment variables, `IBM_WATSONX_PROJECT_ID` or `IBM_WATSONX_SPACE_ID`."
)
super().__init__(
model_id=model_id,
temperature=temperature,
max_new_tokens=max_new_tokens,
additional_kwargs=additional_kwargs,
model_info=self._model.get_details(),
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(self) -> str:
"""Get Class Name."""
return "WatsonX_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=watsonx_model_to_context_size(self.model_id),
num_output=self.max_new_tokens,
model_name=self.model_id,
)
@property
def sample_model_kwargs(self) -> Dict[str, Any]:
"""Get a sample of Model kwargs that a user can pass to the model."""
try:
from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames
except ImportError as e:
raise ImportError(
"You must install the `ibm_watson_machine_learning` package to use WatsonX"
"please `pip install ibm_watson_machine_learning`"
) from e
params = GenTextParamsMetaNames().get_example_values()
params.pop("return_options")
return params
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"max_new_tokens": self.max_new_tokens,
"temperature": self.temperature,
}
return {**base_kwargs, **self.additional_kwargs}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {**self._model_kwargs, **kwargs}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._model.generate_text(prompt=prompt, params=all_kwargs)
return CompletionResponse(text=response)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream_response = self._model.generate_text_stream(
prompt=prompt, params=all_kwargs
)
def gen() -> CompletionResponseGen:
content = ""
for stream_delta in stream_response:
content += stream_delta
yield CompletionResponse(text=content, delta=stream_delta)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
chat_fn = completion_to_chat_decorator(self.complete)
return chat_fn(messages, **all_kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
chat_stream_fn = stream_completion_to_chat_decorator(self.stream_complete)
return chat_stream_fn(messages, **all_kwargs)
# Async Functions
# IBM Watson Machine Learning Package currently does not have Support for Async calls
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
raise NotImplementedError
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.llms.generic_utils.stream_completion_to_chat_decorator",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.llms.watsonx_utils.get_from_param_or_env_without_error",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.llms.watsonx_utils.watsonx_model_to_context_size",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.watsonx_utils.WATSONX_MODELS.keys",
"llama_index.legacy.callbacks.CallbackManager",
"llama_index.legacy.llms.generic_utils.completion_to_chat_decorator"
] | [((968, 1006), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Model to use."""'}), "(description='The Model to use.')\n", (973, 1006), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1033, 1095), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The maximum number of tokens to generate."""'}), "(description='The maximum number of tokens to generate.')\n", (1038, 1095), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1121, 1178), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The temperature to use for sampling."""'}), "(description='The temperature to use for sampling.')\n", (1126, 1178), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1219, 1306), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'dict', 'description': '"""Additional Kwargs for the WatsonX 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ibm_watson_machine_learning.foundation_models.model import Model\n'), ((4376, 4420), 'llama_index.legacy.llms.watsonx_utils.watsonx_model_to_context_size', 'watsonx_model_to_context_size', (['self.model_id'], {}), '(self.model_id)\n', (4405, 4420), False, 'from llama_index.legacy.llms.watsonx_utils import WATSONX_MODELS, get_from_param_or_env_without_error, watsonx_model_to_context_size\n'), ((5020, 5044), 'ibm_watson_machine_learning.metanames.GenTextParamsMetaNames', 'GenTextParamsMetaNames', ([], {}), '()\n', (5042, 5044), False, 'from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames\n'), ((6321, 6373), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'content', 'delta': 'stream_delta'}), '(text=content, delta=stream_delta)\n', (6339, 6373), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n'), ((2400, 2421), 'llama_index.legacy.llms.watsonx_utils.WATSONX_MODELS.keys', 'WATSONX_MODELS.keys', ([], {}), '()\n', (2419, 2421), False, 'from llama_index.legacy.llms.watsonx_utils import WATSONX_MODELS, get_from_param_or_env_without_error, watsonx_model_to_context_size\n')] |
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.generic_utils import (
completion_to_chat_decorator,
stream_completion_to_chat_decorator,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.watsonx_utils import (
WATSONX_MODELS,
get_from_param_or_env_without_error,
watsonx_model_to_context_size,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class WatsonX(LLM):
"""IBM WatsonX LLM."""
model_id: str = Field(description="The Model to use.")
max_new_tokens: int = Field(description="The maximum number of tokens to generate.")
temperature: float = Field(description="The temperature to use for sampling.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional Kwargs for the WatsonX model"
)
model_info: Dict[str, Any] = Field(
default_factory=dict, description="Details about the selected model"
)
_model = PrivateAttr()
def __init__(
self,
credentials: Dict[str, Any],
model_id: Optional[str] = "ibm/mpt-7b-instruct2",
project_id: Optional[str] = None,
space_id: Optional[str] = None,
max_new_tokens: Optional[int] = 512,
temperature: Optional[float] = 0.1,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""Initialize params."""
if model_id not in WATSONX_MODELS:
raise ValueError(
f"Model name {model_id} not found in {WATSONX_MODELS.keys()}"
)
try:
from ibm_watson_machine_learning.foundation_models.model import Model
except ImportError as e:
raise ImportError(
"You must install the `ibm_watson_machine_learning` package to use WatsonX"
"please `pip install ibm_watson_machine_learning`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
project_id = get_from_param_or_env_without_error(
project_id, "IBM_WATSONX_PROJECT_ID"
)
space_id = get_from_param_or_env_without_error(space_id, "IBM_WATSONX_SPACE_ID")
if project_id is not None or space_id is not None:
self._model = Model(
model_id=model_id,
credentials=credentials,
project_id=project_id,
space_id=space_id,
)
else:
raise ValueError(
f"Did not find `project_id` or `space_id`, Please pass them as named parameters"
f" or as environment variables, `IBM_WATSONX_PROJECT_ID` or `IBM_WATSONX_SPACE_ID`."
)
super().__init__(
model_id=model_id,
temperature=temperature,
max_new_tokens=max_new_tokens,
additional_kwargs=additional_kwargs,
model_info=self._model.get_details(),
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(self) -> str:
"""Get Class Name."""
return "WatsonX_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=watsonx_model_to_context_size(self.model_id),
num_output=self.max_new_tokens,
model_name=self.model_id,
)
@property
def sample_model_kwargs(self) -> Dict[str, Any]:
"""Get a sample of Model kwargs that a user can pass to the model."""
try:
from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames
except ImportError as e:
raise ImportError(
"You must install the `ibm_watson_machine_learning` package to use WatsonX"
"please `pip install ibm_watson_machine_learning`"
) from e
params = GenTextParamsMetaNames().get_example_values()
params.pop("return_options")
return params
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"max_new_tokens": self.max_new_tokens,
"temperature": self.temperature,
}
return {**base_kwargs, **self.additional_kwargs}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {**self._model_kwargs, **kwargs}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._model.generate_text(prompt=prompt, params=all_kwargs)
return CompletionResponse(text=response)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream_response = self._model.generate_text_stream(
prompt=prompt, params=all_kwargs
)
def gen() -> CompletionResponseGen:
content = ""
for stream_delta in stream_response:
content += stream_delta
yield CompletionResponse(text=content, delta=stream_delta)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
chat_fn = completion_to_chat_decorator(self.complete)
return chat_fn(messages, **all_kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
chat_stream_fn = stream_completion_to_chat_decorator(self.stream_complete)
return chat_stream_fn(messages, **all_kwargs)
# Async Functions
# IBM Watson Machine Learning Package currently does not have Support for Async calls
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
raise NotImplementedError
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.llms.generic_utils.stream_completion_to_chat_decorator",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.llms.watsonx_utils.get_from_param_or_env_without_error",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.llms.watsonx_utils.watsonx_model_to_context_size",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.watsonx_utils.WATSONX_MODELS.keys",
"llama_index.legacy.callbacks.CallbackManager",
"llama_index.legacy.llms.generic_utils.completion_to_chat_decorator"
] | [((968, 1006), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Model to use."""'}), "(description='The Model to use.')\n", (973, 1006), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1033, 1095), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The maximum number of tokens to generate."""'}), "(description='The maximum number of tokens to generate.')\n", (1038, 1095), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1121, 1178), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The temperature to use for sampling."""'}), "(description='The temperature to use for sampling.')\n", (1126, 1178), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1219, 1306), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'dict', 'description': '"""Additional Kwargs for the WatsonX 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CompletionResponseGen, LLMMetadata\n'), ((2400, 2421), 'llama_index.legacy.llms.watsonx_utils.WATSONX_MODELS.keys', 'WATSONX_MODELS.keys', ([], {}), '()\n', (2419, 2421), False, 'from llama_index.legacy.llms.watsonx_utils import WATSONX_MODELS, get_from_param_or_env_without_error, watsonx_model_to_context_size\n')] |
from typing import Any, Awaitable, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_TEMPERATURE
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.generic_utils import (
achat_to_completion_decorator,
acompletion_to_chat_decorator,
astream_chat_to_completion_decorator,
astream_completion_to_chat_decorator,
chat_to_completion_decorator,
completion_to_chat_decorator,
stream_chat_to_completion_decorator,
stream_completion_to_chat_decorator,
)
from llama_index.legacy.llms.litellm_utils import (
acompletion_with_retry,
completion_with_retry,
from_litellm_message,
is_function_calling_model,
openai_modelname_to_contextsize,
to_openai_message_dicts,
validate_litellm_api_key,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
DEFAULT_LITELLM_MODEL = "gpt-3.5-turbo"
class LiteLLM(LLM):
model: str = Field(
default=DEFAULT_LITELLM_MODEL,
description=(
"The LiteLLM model to use. "
"For complete list of providers https://docs.litellm.ai/docs/providers"
),
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use during generation.",
gte=0.0,
lte=1.0,
)
max_tokens: Optional[int] = Field(
description="The maximum number of tokens to generate.",
gt=0,
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs for the LLM API.",
# for all inputs https://docs.litellm.ai/docs/completion/input
)
max_retries: int = Field(
default=10, description="The maximum number of API retries."
)
def __init__(
self,
model: str = DEFAULT_LITELLM_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
api_type: Optional[str] = None,
api_base: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
if "custom_llm_provider" in kwargs:
if (
kwargs["custom_llm_provider"] != "ollama"
and kwargs["custom_llm_provider"] != "vllm"
): # don't check keys for local models
validate_litellm_api_key(api_key, api_type)
else: # by default assume it's a hosted endpoint
validate_litellm_api_key(api_key, api_type)
additional_kwargs = additional_kwargs or {}
if api_key is not None:
additional_kwargs["api_key"] = api_key
if api_type is not None:
additional_kwargs["api_type"] = api_type
if api_base is not None:
additional_kwargs["api_base"] = api_base
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
**kwargs,
)
def _get_model_name(self) -> str:
model_name = self.model
if "ft-" in model_name: # legacy fine-tuning
model_name = model_name.split(":")[0]
elif model_name.startswith("ft:"):
model_name = model_name.split(":")[1]
return model_name
@classmethod
def class_name(cls) -> str:
return "litellm_llm"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=openai_modelname_to_contextsize(self._get_model_name()),
num_output=self.max_tokens or -1,
is_chat_model=True,
is_function_calling_model=is_function_calling_model(self._get_model_name()),
model_name=self.model,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if self._is_chat_model:
chat_fn = self._chat
else:
chat_fn = completion_to_chat_decorator(self._complete)
return chat_fn(messages, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self._is_chat_model:
stream_chat_fn = self._stream_chat
else:
stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete)
return stream_chat_fn(messages, **kwargs)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
# litellm assumes all llms are chat llms
if self._is_chat_model:
complete_fn = chat_to_completion_decorator(self._chat)
else:
complete_fn = self._complete
return complete_fn(prompt, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
if self._is_chat_model:
stream_complete_fn = stream_chat_to_completion_decorator(self._stream_chat)
else:
stream_complete_fn = self._stream_complete
return stream_complete_fn(prompt, **kwargs)
@property
def _is_chat_model(self) -> bool:
# litellm assumes all llms are chat llms
return True
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
if "max_tokens" in all_kwargs and all_kwargs["max_tokens"] is None:
all_kwargs.pop(
"max_tokens"
) # don't send max_tokens == None, this throws errors for Non OpenAI providers
response = completion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=False,
**all_kwargs,
)
message_dict = response["choices"][0]["message"]
message = from_litellm_message(message_dict)
return ChatResponse(
message=message,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
if "max_tokens" in all_kwargs and all_kwargs["max_tokens"] is None:
all_kwargs.pop(
"max_tokens"
) # don't send max_tokens == None, this throws errors for Non OpenAI providers
def gen() -> ChatResponseGen:
content = ""
function_call: Optional[dict] = None
for response in completion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=True,
**all_kwargs,
):
delta = response["choices"][0]["delta"]
role = delta.get("role", "assistant")
content_delta = delta.get("content", "") or ""
content += content_delta
function_call_delta = delta.get("function_call", None)
if function_call_delta is not None:
if function_call is None:
function_call = function_call_delta
## ensure we do not add a blank function call
if function_call.get("function_name", "") is None:
del function_call["function_name"]
else:
function_call["arguments"] += function_call_delta["arguments"]
additional_kwargs = {}
if function_call is not None:
additional_kwargs["function_call"] = function_call
yield ChatResponse(
message=ChatMessage(
role=role,
content=content,
additional_kwargs=additional_kwargs,
),
delta=content_delta,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
return gen()
def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
raise NotImplementedError("litellm assumes all llms are chat llms.")
def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
raise NotImplementedError("litellm assumes all llms are chat llms.")
def _get_max_token_for_prompt(self, prompt: str) -> int:
try:
import tiktoken
except ImportError:
raise ImportError(
"Please install tiktoken to use the max_tokens=None feature."
)
context_window = self.metadata.context_window
try:
encoding = tiktoken.encoding_for_model(self._get_model_name())
except KeyError:
encoding = encoding = tiktoken.get_encoding(
"cl100k_base"
) # default to using cl10k_base
tokens = encoding.encode(prompt)
max_token = context_window - len(tokens)
if max_token <= 0:
raise ValueError(
f"The prompt is too long for the model. "
f"Please use a prompt that is less than {context_window} tokens."
)
return max_token
def _get_response_token_counts(self, raw_response: Any) -> dict:
"""Get the token usage reported by the response."""
if not isinstance(raw_response, dict):
return {}
usage = raw_response.get("usage", {})
return {
"prompt_tokens": usage.get("prompt_tokens", 0),
"completion_tokens": usage.get("completion_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
}
# ===== Async Endpoints =====
@llm_chat_callback()
async def achat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
achat_fn: Callable[..., Awaitable[ChatResponse]]
if self._is_chat_model:
achat_fn = self._achat
else:
achat_fn = acompletion_to_chat_decorator(self._acomplete)
return await achat_fn(messages, **kwargs)
@llm_chat_callback()
async def astream_chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponseAsyncGen:
astream_chat_fn: Callable[..., Awaitable[ChatResponseAsyncGen]]
if self._is_chat_model:
astream_chat_fn = self._astream_chat
else:
astream_chat_fn = astream_completion_to_chat_decorator(
self._astream_complete
)
return await astream_chat_fn(messages, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
if self._is_chat_model:
acomplete_fn = achat_to_completion_decorator(self._achat)
else:
acomplete_fn = self._acomplete
return await acomplete_fn(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
if self._is_chat_model:
astream_complete_fn = astream_chat_to_completion_decorator(
self._astream_chat
)
else:
astream_complete_fn = self._astream_complete
return await astream_complete_fn(prompt, **kwargs)
async def _achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = await acompletion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=False,
**all_kwargs,
)
message_dict = response["choices"][0]["message"]
message = from_litellm_message(message_dict)
return ChatResponse(
message=message,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
async def _astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
async def gen() -> ChatResponseAsyncGen:
content = ""
function_call: Optional[dict] = None
async for response in await acompletion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=True,
**all_kwargs,
):
delta = response["choices"][0]["delta"]
role = delta.get("role", "assistant")
content_delta = delta.get("content", "") or ""
content += content_delta
function_call_delta = delta.get("function_call", None)
if function_call_delta is not None:
if function_call is None:
function_call = function_call_delta
## ensure we do not add a blank function call
if function_call.get("function_name", "") is None:
del function_call["function_name"]
else:
function_call["arguments"] += function_call_delta["arguments"]
additional_kwargs = {}
if function_call is not None:
additional_kwargs["function_call"] = function_call
yield ChatResponse(
message=ChatMessage(
role=role,
content=content,
additional_kwargs=additional_kwargs,
),
delta=content_delta,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
return gen()
async def _acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
raise NotImplementedError("litellm assumes all llms are chat llms.")
async def _astream_complete(
self, prompt: str, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError("litellm assumes all llms are chat llms.")
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"llama_index.legacy.llms.generic_utils.astream_completion_to_chat_decorator",
"llama_index.legacy.llms.generic_utils.astream_chat_to_completion_decorator",
"llama_index.legacy.llms.generic_utils.acompletion_to_chat_decorator",
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"llama_index.legacy.llms.litellm_utils.from_litellm_message",
"llama_index.legacy.core.llms.types.ChatMessage",
"llama_index.legacy.llms.generic_utils.completion_to_chat_decorator",
"llama_index.legacy.llms.litellm_utils.to_openai_message_dicts",
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stream_chat_to_completion_decorator, stream_completion_to_chat_decorator\n'), ((12813, 12873), 'llama_index.legacy.llms.generic_utils.astream_completion_to_chat_decorator', 'astream_completion_to_chat_decorator', (['self._astream_complete'], {}), '(self._astream_complete)\n', (12849, 12873), False, 'from llama_index.legacy.llms.generic_utils import achat_to_completion_decorator, acompletion_to_chat_decorator, astream_chat_to_completion_decorator, astream_completion_to_chat_decorator, chat_to_completion_decorator, completion_to_chat_decorator, stream_chat_to_completion_decorator, stream_completion_to_chat_decorator\n'), ((13172, 13214), 'llama_index.legacy.llms.generic_utils.achat_to_completion_decorator', 'achat_to_completion_decorator', (['self._achat'], {}), '(self._achat)\n', (13201, 13214), False, 'from llama_index.legacy.llms.generic_utils import achat_to_completion_decorator, acompletion_to_chat_decorator, astream_chat_to_completion_decorator, astream_completion_to_chat_decorator, chat_to_completion_decorator, completion_to_chat_decorator, stream_chat_to_completion_decorator, stream_completion_to_chat_decorator\n'), ((13557, 13613), 'llama_index.legacy.llms.generic_utils.astream_chat_to_completion_decorator', 'astream_chat_to_completion_decorator', (['self._astream_chat'], {}), '(self._astream_chat)\n', (13593, 13613), False, 'from llama_index.legacy.llms.generic_utils import achat_to_completion_decorator, acompletion_to_chat_decorator, astream_chat_to_completion_decorator, astream_completion_to_chat_decorator, chat_to_completion_decorator, completion_to_chat_decorator, stream_chat_to_completion_decorator, stream_completion_to_chat_decorator\n'), ((14117, 14261), 'llama_index.legacy.llms.litellm_utils.acompletion_with_retry', 'acompletion_with_retry', ([], {'is_chat_model': 'self._is_chat_model', 'max_retries': 'self.max_retries', 'messages': 'message_dicts', 'stream': '(False)'}), '(is_chat_model=self._is_chat_model, max_retries=self.\n max_retries, messages=message_dicts, stream=False, **all_kwargs)\n', (14139, 14261), False, 'from llama_index.legacy.llms.litellm_utils import acompletion_with_retry, completion_with_retry, from_litellm_message, is_function_calling_model, openai_modelname_to_contextsize, to_openai_message_dicts, validate_litellm_api_key\n'), ((3266, 3309), 'llama_index.legacy.llms.litellm_utils.validate_litellm_api_key', 'validate_litellm_api_key', (['api_key', 'api_type'], {}), '(api_key, api_type)\n', (3290, 3309), False, 'from llama_index.legacy.llms.litellm_utils import acompletion_with_retry, completion_with_retry, from_litellm_message, is_function_calling_model, openai_modelname_to_contextsize, to_openai_message_dicts, validate_litellm_api_key\n'), ((11130, 11166), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['"""cl100k_base"""'], {}), "('cl100k_base')\n", (11151, 11166), False, 'import tiktoken\n'), ((15103, 15246), 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CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n'), ((16326, 16402), 'llama_index.legacy.core.llms.types.ChatMessage', 'ChatMessage', ([], {'role': 'role', 'content': 'content', 'additional_kwargs': 'additional_kwargs'}), '(role=role, content=content, additional_kwargs=additional_kwargs)\n', (16337, 16402), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n')] |
from typing import Any, Awaitable, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_TEMPERATURE
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.generic_utils import (
achat_to_completion_decorator,
acompletion_to_chat_decorator,
astream_chat_to_completion_decorator,
astream_completion_to_chat_decorator,
chat_to_completion_decorator,
completion_to_chat_decorator,
stream_chat_to_completion_decorator,
stream_completion_to_chat_decorator,
)
from llama_index.legacy.llms.litellm_utils import (
acompletion_with_retry,
completion_with_retry,
from_litellm_message,
is_function_calling_model,
openai_modelname_to_contextsize,
to_openai_message_dicts,
validate_litellm_api_key,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
DEFAULT_LITELLM_MODEL = "gpt-3.5-turbo"
class LiteLLM(LLM):
model: str = Field(
default=DEFAULT_LITELLM_MODEL,
description=(
"The LiteLLM model to use. "
"For complete list of providers https://docs.litellm.ai/docs/providers"
),
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use during generation.",
gte=0.0,
lte=1.0,
)
max_tokens: Optional[int] = Field(
description="The maximum number of tokens to generate.",
gt=0,
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs for the LLM API.",
# for all inputs https://docs.litellm.ai/docs/completion/input
)
max_retries: int = Field(
default=10, description="The maximum number of API retries."
)
def __init__(
self,
model: str = DEFAULT_LITELLM_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
api_type: Optional[str] = None,
api_base: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
if "custom_llm_provider" in kwargs:
if (
kwargs["custom_llm_provider"] != "ollama"
and kwargs["custom_llm_provider"] != "vllm"
): # don't check keys for local models
validate_litellm_api_key(api_key, api_type)
else: # by default assume it's a hosted endpoint
validate_litellm_api_key(api_key, api_type)
additional_kwargs = additional_kwargs or {}
if api_key is not None:
additional_kwargs["api_key"] = api_key
if api_type is not None:
additional_kwargs["api_type"] = api_type
if api_base is not None:
additional_kwargs["api_base"] = api_base
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
**kwargs,
)
def _get_model_name(self) -> str:
model_name = self.model
if "ft-" in model_name: # legacy fine-tuning
model_name = model_name.split(":")[0]
elif model_name.startswith("ft:"):
model_name = model_name.split(":")[1]
return model_name
@classmethod
def class_name(cls) -> str:
return "litellm_llm"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=openai_modelname_to_contextsize(self._get_model_name()),
num_output=self.max_tokens or -1,
is_chat_model=True,
is_function_calling_model=is_function_calling_model(self._get_model_name()),
model_name=self.model,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if self._is_chat_model:
chat_fn = self._chat
else:
chat_fn = completion_to_chat_decorator(self._complete)
return chat_fn(messages, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self._is_chat_model:
stream_chat_fn = self._stream_chat
else:
stream_chat_fn = stream_completion_to_chat_decorator(self._stream_complete)
return stream_chat_fn(messages, **kwargs)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
# litellm assumes all llms are chat llms
if self._is_chat_model:
complete_fn = chat_to_completion_decorator(self._chat)
else:
complete_fn = self._complete
return complete_fn(prompt, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
if self._is_chat_model:
stream_complete_fn = stream_chat_to_completion_decorator(self._stream_chat)
else:
stream_complete_fn = self._stream_complete
return stream_complete_fn(prompt, **kwargs)
@property
def _is_chat_model(self) -> bool:
# litellm assumes all llms are chat llms
return True
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
if "max_tokens" in all_kwargs and all_kwargs["max_tokens"] is None:
all_kwargs.pop(
"max_tokens"
) # don't send max_tokens == None, this throws errors for Non OpenAI providers
response = completion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=False,
**all_kwargs,
)
message_dict = response["choices"][0]["message"]
message = from_litellm_message(message_dict)
return ChatResponse(
message=message,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
if "max_tokens" in all_kwargs and all_kwargs["max_tokens"] is None:
all_kwargs.pop(
"max_tokens"
) # don't send max_tokens == None, this throws errors for Non OpenAI providers
def gen() -> ChatResponseGen:
content = ""
function_call: Optional[dict] = None
for response in completion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=True,
**all_kwargs,
):
delta = response["choices"][0]["delta"]
role = delta.get("role", "assistant")
content_delta = delta.get("content", "") or ""
content += content_delta
function_call_delta = delta.get("function_call", None)
if function_call_delta is not None:
if function_call is None:
function_call = function_call_delta
## ensure we do not add a blank function call
if function_call.get("function_name", "") is None:
del function_call["function_name"]
else:
function_call["arguments"] += function_call_delta["arguments"]
additional_kwargs = {}
if function_call is not None:
additional_kwargs["function_call"] = function_call
yield ChatResponse(
message=ChatMessage(
role=role,
content=content,
additional_kwargs=additional_kwargs,
),
delta=content_delta,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
return gen()
def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
raise NotImplementedError("litellm assumes all llms are chat llms.")
def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
raise NotImplementedError("litellm assumes all llms are chat llms.")
def _get_max_token_for_prompt(self, prompt: str) -> int:
try:
import tiktoken
except ImportError:
raise ImportError(
"Please install tiktoken to use the max_tokens=None feature."
)
context_window = self.metadata.context_window
try:
encoding = tiktoken.encoding_for_model(self._get_model_name())
except KeyError:
encoding = encoding = tiktoken.get_encoding(
"cl100k_base"
) # default to using cl10k_base
tokens = encoding.encode(prompt)
max_token = context_window - len(tokens)
if max_token <= 0:
raise ValueError(
f"The prompt is too long for the model. "
f"Please use a prompt that is less than {context_window} tokens."
)
return max_token
def _get_response_token_counts(self, raw_response: Any) -> dict:
"""Get the token usage reported by the response."""
if not isinstance(raw_response, dict):
return {}
usage = raw_response.get("usage", {})
return {
"prompt_tokens": usage.get("prompt_tokens", 0),
"completion_tokens": usage.get("completion_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
}
# ===== Async Endpoints =====
@llm_chat_callback()
async def achat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
achat_fn: Callable[..., Awaitable[ChatResponse]]
if self._is_chat_model:
achat_fn = self._achat
else:
achat_fn = acompletion_to_chat_decorator(self._acomplete)
return await achat_fn(messages, **kwargs)
@llm_chat_callback()
async def astream_chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponseAsyncGen:
astream_chat_fn: Callable[..., Awaitable[ChatResponseAsyncGen]]
if self._is_chat_model:
astream_chat_fn = self._astream_chat
else:
astream_chat_fn = astream_completion_to_chat_decorator(
self._astream_complete
)
return await astream_chat_fn(messages, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
if self._is_chat_model:
acomplete_fn = achat_to_completion_decorator(self._achat)
else:
acomplete_fn = self._acomplete
return await acomplete_fn(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
if self._is_chat_model:
astream_complete_fn = astream_chat_to_completion_decorator(
self._astream_chat
)
else:
astream_complete_fn = self._astream_complete
return await astream_complete_fn(prompt, **kwargs)
async def _achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = await acompletion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=False,
**all_kwargs,
)
message_dict = response["choices"][0]["message"]
message = from_litellm_message(message_dict)
return ChatResponse(
message=message,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
async def _astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
if not self._is_chat_model:
raise ValueError("This model is not a chat model.")
message_dicts = to_openai_message_dicts(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
async def gen() -> ChatResponseAsyncGen:
content = ""
function_call: Optional[dict] = None
async for response in await acompletion_with_retry(
is_chat_model=self._is_chat_model,
max_retries=self.max_retries,
messages=message_dicts,
stream=True,
**all_kwargs,
):
delta = response["choices"][0]["delta"]
role = delta.get("role", "assistant")
content_delta = delta.get("content", "") or ""
content += content_delta
function_call_delta = delta.get("function_call", None)
if function_call_delta is not None:
if function_call is None:
function_call = function_call_delta
## ensure we do not add a blank function call
if function_call.get("function_name", "") is None:
del function_call["function_name"]
else:
function_call["arguments"] += function_call_delta["arguments"]
additional_kwargs = {}
if function_call is not None:
additional_kwargs["function_call"] = function_call
yield ChatResponse(
message=ChatMessage(
role=role,
content=content,
additional_kwargs=additional_kwargs,
),
delta=content_delta,
raw=response,
additional_kwargs=self._get_response_token_counts(response),
)
return gen()
async def _acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
raise NotImplementedError("litellm assumes all llms are chat llms.")
async def _astream_complete(
self, prompt: str, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError("litellm assumes all llms are chat llms.")
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'llama_index.legacy.llms.litellm_utils.to_openai_message_dicts', 'to_openai_message_dicts', (['messages'], {}), '(messages)\n', (14876, 14886), False, 'from llama_index.legacy.llms.litellm_utils import acompletion_with_retry, completion_with_retry, from_litellm_message, is_function_calling_model, openai_modelname_to_contextsize, to_openai_message_dicts, validate_litellm_api_key\n'), ((3380, 3423), 'llama_index.legacy.llms.litellm_utils.validate_litellm_api_key', 'validate_litellm_api_key', (['api_key', 'api_type'], {}), '(api_key, api_type)\n', (3404, 3423), False, 'from llama_index.legacy.llms.litellm_utils import acompletion_with_retry, completion_with_retry, from_litellm_message, is_function_calling_model, openai_modelname_to_contextsize, to_openai_message_dicts, validate_litellm_api_key\n'), ((5229, 5273), 'llama_index.legacy.llms.generic_utils.completion_to_chat_decorator', 'completion_to_chat_decorator', (['self._complete'], {}), '(self._complete)\n', (5257, 5273), False, 'from llama_index.legacy.llms.generic_utils import achat_to_completion_decorator, acompletion_to_chat_decorator, astream_chat_to_completion_decorator, astream_completion_to_chat_decorator, chat_to_completion_decorator, completion_to_chat_decorator, stream_chat_to_completion_decorator, stream_completion_to_chat_decorator\n'), ((5573, 5631), 'llama_index.legacy.llms.generic_utils.stream_completion_to_chat_decorator', 'stream_completion_to_chat_decorator', (['self._stream_complete'], {}), '(self._stream_complete)\n', (5608, 5631), False, 'from llama_index.legacy.llms.generic_utils import achat_to_completion_decorator, acompletion_to_chat_decorator, astream_chat_to_completion_decorator, astream_completion_to_chat_decorator, chat_to_completion_decorator, completion_to_chat_decorator, stream_chat_to_completion_decorator, stream_completion_to_chat_decorator\n'), ((5934, 5974), 'llama_index.legacy.llms.generic_utils.chat_to_completion_decorator', 'chat_to_completion_decorator', (['self._chat'], {}), 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'completion_with_retry', ([], {'is_chat_model': 'self._is_chat_model', 'max_retries': 'self.max_retries', 'messages': 'message_dicts', 'stream': '(True)'}), '(is_chat_model=self._is_chat_model, max_retries=self.\n max_retries, messages=message_dicts, stream=True, **all_kwargs)\n', (8789, 8910), False, 'from llama_index.legacy.llms.litellm_utils import acompletion_with_retry, completion_with_retry, from_litellm_message, is_function_calling_model, openai_modelname_to_contextsize, to_openai_message_dicts, validate_litellm_api_key\n'), ((12356, 12402), 'llama_index.legacy.llms.generic_utils.acompletion_to_chat_decorator', 'acompletion_to_chat_decorator', (['self._acomplete'], {}), '(self._acomplete)\n', (12385, 12402), False, 'from llama_index.legacy.llms.generic_utils import achat_to_completion_decorator, acompletion_to_chat_decorator, astream_chat_to_completion_decorator, astream_completion_to_chat_decorator, chat_to_completion_decorator, completion_to_chat_decorator, 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import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset("MiniTruthfulQADataset", "./data")
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
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import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset("MiniTruthfulQADataset", "./data")
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 322), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""MiniTruthfulQADataset"""', '"""./data"""'], {}), "('MiniTruthfulQADataset', './data')\n", (287, 322), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((367, 419), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (398, 419), False, 'from llama_index.core import VectorStoreIndex\n'), ((512, 561), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack"""'], {}), "('RagEvaluatorPack', './pack')\n", (531, 561), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1412, 1436), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1434, 1436), False, 'import asyncio\n')] |
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_TEMPERATURE
# from mistralai.models.chat_completion import ChatMessage
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.generic_utils import (
achat_to_completion_decorator,
astream_chat_to_completion_decorator,
chat_to_completion_decorator,
get_from_param_or_env,
stream_chat_to_completion_decorator,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.mistralai_utils import (
mistralai_modelname_to_contextsize,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
DEFAULT_MISTRALAI_MODEL = "mistral-tiny"
DEFAULT_MISTRALAI_ENDPOINT = "https://api.mistral.ai"
DEFAULT_MISTRALAI_MAX_TOKENS = 512
class MistralAI(LLM):
model: str = Field(
default=DEFAULT_MISTRALAI_MODEL, description="The mistralai model to use."
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use for sampling.",
gte=0.0,
lte=1.0,
)
max_tokens: int = Field(
default=DEFAULT_MISTRALAI_MAX_TOKENS,
description="The maximum number of tokens to generate.",
gt=0,
)
timeout: float = Field(
default=120, description="The timeout to use in seconds.", gte=0
)
max_retries: int = Field(
default=5, description="The maximum number of API retries.", gte=0
)
safe_mode: bool = Field(
default=False,
description="The parameter to enforce guardrails in chat generations.",
)
random_seed: str = Field(
default=None, description="The random seed to use for sampling."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the MistralAI API."
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = DEFAULT_MISTRALAI_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_MISTRALAI_MAX_TOKENS,
timeout: int = 120,
max_retries: int = 5,
safe_mode: bool = False,
random_seed: Optional[int] = None,
api_key: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from mistralai.async_client import MistralAsyncClient
from mistralai.client import MistralClient
except ImportError as e:
raise ImportError(
"You must install the `mistralai` package to use mistralai."
"Please `pip install mistralai`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
api_key = get_from_param_or_env("api_key", api_key, "MISTRAL_API_KEY", "")
if not api_key:
raise ValueError(
"You must provide an API key to use mistralai. "
"You can either pass it in as an argument or set it `MISTRAL_API_KEY`."
)
self._client = MistralClient(
api_key=api_key,
endpoint=DEFAULT_MISTRALAI_ENDPOINT,
timeout=timeout,
max_retries=max_retries,
)
self._aclient = MistralAsyncClient(
api_key=api_key,
endpoint=DEFAULT_MISTRALAI_ENDPOINT,
timeout=timeout,
max_retries=max_retries,
)
super().__init__(
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
timeout=timeout,
max_retries=max_retries,
safe_mode=safe_mode,
random_seed=random_seed,
model=model,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "MistralAI_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=mistralai_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
safe_mode=self.safe_mode,
random_seed=self.random_seed,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"random_seed": self.random_seed,
"safe_mode": self.safe_mode,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=x.role, content=x.content) for x in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat(messages=messages, **all_kwargs)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=dict(response),
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=message.role, content=message.content)
for message in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat_stream(messages=messages, **all_kwargs)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for chunk in response:
content_delta = chunk.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=message.role, content=message.content)
for message in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._aclient.chat(messages=messages, **all_kwargs)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=dict(response),
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
acomplete_fn = achat_to_completion_decorator(self.achat)
return await acomplete_fn(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=x.role, content=x.content) for x in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._aclient.chat_stream(messages=messages, **all_kwargs)
async def gen() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for chunk in response:
content_delta = chunk.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, **kwargs)
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"llama_index.legacy.llms.generic_utils.astream_chat_to_completion_decorator",
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"llama_index.legacy.llms.generic_utils.achat_to_completion_decorator",
"llama_index.legacy.llms.generic_utils.chat_to_completion_decorator",
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from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_TEMPERATURE
# from mistralai.models.chat_completion import ChatMessage
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.generic_utils import (
achat_to_completion_decorator,
astream_chat_to_completion_decorator,
chat_to_completion_decorator,
get_from_param_or_env,
stream_chat_to_completion_decorator,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.llms.mistralai_utils import (
mistralai_modelname_to_contextsize,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
DEFAULT_MISTRALAI_MODEL = "mistral-tiny"
DEFAULT_MISTRALAI_ENDPOINT = "https://api.mistral.ai"
DEFAULT_MISTRALAI_MAX_TOKENS = 512
class MistralAI(LLM):
model: str = Field(
default=DEFAULT_MISTRALAI_MODEL, description="The mistralai model to use."
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use for sampling.",
gte=0.0,
lte=1.0,
)
max_tokens: int = Field(
default=DEFAULT_MISTRALAI_MAX_TOKENS,
description="The maximum number of tokens to generate.",
gt=0,
)
timeout: float = Field(
default=120, description="The timeout to use in seconds.", gte=0
)
max_retries: int = Field(
default=5, description="The maximum number of API retries.", gte=0
)
safe_mode: bool = Field(
default=False,
description="The parameter to enforce guardrails in chat generations.",
)
random_seed: str = Field(
default=None, description="The random seed to use for sampling."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the MistralAI API."
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = DEFAULT_MISTRALAI_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_MISTRALAI_MAX_TOKENS,
timeout: int = 120,
max_retries: int = 5,
safe_mode: bool = False,
random_seed: Optional[int] = None,
api_key: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from mistralai.async_client import MistralAsyncClient
from mistralai.client import MistralClient
except ImportError as e:
raise ImportError(
"You must install the `mistralai` package to use mistralai."
"Please `pip install mistralai`"
) from e
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
api_key = get_from_param_or_env("api_key", api_key, "MISTRAL_API_KEY", "")
if not api_key:
raise ValueError(
"You must provide an API key to use mistralai. "
"You can either pass it in as an argument or set it `MISTRAL_API_KEY`."
)
self._client = MistralClient(
api_key=api_key,
endpoint=DEFAULT_MISTRALAI_ENDPOINT,
timeout=timeout,
max_retries=max_retries,
)
self._aclient = MistralAsyncClient(
api_key=api_key,
endpoint=DEFAULT_MISTRALAI_ENDPOINT,
timeout=timeout,
max_retries=max_retries,
)
super().__init__(
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
timeout=timeout,
max_retries=max_retries,
safe_mode=safe_mode,
random_seed=random_seed,
model=model,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "MistralAI_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=mistralai_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
safe_mode=self.safe_mode,
random_seed=self.random_seed,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"random_seed": self.random_seed,
"safe_mode": self.safe_mode,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=x.role, content=x.content) for x in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat(messages=messages, **all_kwargs)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=dict(response),
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=message.role, content=message.content)
for message in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat_stream(messages=messages, **all_kwargs)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for chunk in response:
content_delta = chunk.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=message.role, content=message.content)
for message in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._aclient.chat(messages=messages, **all_kwargs)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content
),
raw=dict(response),
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
acomplete_fn = achat_to_completion_decorator(self.achat)
return await acomplete_fn(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
# convert messages to mistral ChatMessage
from mistralai.client import ChatMessage as mistral_chatmessage
messages = [
mistral_chatmessage(role=x.role, content=x.content) for x in messages
]
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._aclient.chat_stream(messages=messages, **all_kwargs)
async def gen() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for chunk in response:
content_delta = chunk.choices[0].delta.content
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, **kwargs)
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"llama_index.legacy.callbacks.CallbackManager",
"llama_index.legacy.llms.generic_utils.achat_to_completion_decorator",
"llama_index.legacy.llms.generic_utils.chat_to_completion_decorator",
"llama_index.legacy.llms.mistralai_utils.mistralai_modelname_to_contextsize"
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"""Base index classes."""
import logging
from abc import ABC, abstractmethod
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar, cast
from llama_index.legacy.chat_engine.types import BaseChatEngine, ChatMode
from llama_index.legacy.core.base_query_engine import BaseQueryEngine
from llama_index.legacy.core.base_retriever import BaseRetriever
from llama_index.legacy.data_structs.data_structs import IndexStruct
from llama_index.legacy.ingestion import run_transformations
from llama_index.legacy.schema import BaseNode, Document, IndexNode
from llama_index.legacy.service_context import ServiceContext
from llama_index.legacy.storage.docstore.types import BaseDocumentStore, RefDocInfo
from llama_index.legacy.storage.storage_context import StorageContext
IS = TypeVar("IS", bound=IndexStruct)
IndexType = TypeVar("IndexType", bound="BaseIndex")
logger = logging.getLogger(__name__)
class BaseIndex(Generic[IS], ABC):
"""Base LlamaIndex.
Args:
nodes (List[Node]): List of nodes to index
show_progress (bool): Whether to show tqdm progress bars. Defaults to False.
service_context (ServiceContext): Service context container (contains
components like LLM, Embeddings, etc.).
"""
index_struct_cls: Type[IS]
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IS] = None,
storage_context: Optional[StorageContext] = None,
service_context: Optional[ServiceContext] = None,
show_progress: bool = False,
**kwargs: Any,
) -> None:
"""Initialize with parameters."""
if index_struct is None and nodes is None and objects is None:
raise ValueError("One of nodes, objects, or index_struct must be provided.")
if index_struct is not None and nodes is not None:
raise ValueError("Only one of nodes or index_struct can be provided.")
# This is to explicitly make sure that the old UX is not used
if nodes is not None and len(nodes) >= 1 and not isinstance(nodes[0], BaseNode):
if isinstance(nodes[0], Document):
raise ValueError(
"The constructor now takes in a list of Node objects. "
"Since you are passing in a list of Document objects, "
"please use `from_documents` instead."
)
else:
raise ValueError("nodes must be a list of Node objects.")
self._service_context = service_context or ServiceContext.from_defaults()
self._storage_context = storage_context or StorageContext.from_defaults()
self._docstore = self._storage_context.docstore
self._show_progress = show_progress
self._vector_store = self._storage_context.vector_store
self._graph_store = self._storage_context.graph_store
objects = objects or []
self._object_map = {}
for obj in objects:
self._object_map[obj.index_id] = obj.obj
obj.obj = None # clear the object avoid serialization issues
with self._service_context.callback_manager.as_trace("index_construction"):
if index_struct is None:
nodes = nodes or []
index_struct = self.build_index_from_nodes(
nodes + objects # type: ignore
)
self._index_struct = index_struct
self._storage_context.index_store.add_index_struct(self._index_struct)
@classmethod
def from_documents(
cls: Type[IndexType],
documents: Sequence[Document],
storage_context: Optional[StorageContext] = None,
service_context: Optional[ServiceContext] = None,
show_progress: bool = False,
**kwargs: Any,
) -> IndexType:
"""Create index from documents.
Args:
documents (Optional[Sequence[BaseDocument]]): List of documents to
build the index from.
"""
storage_context = storage_context or StorageContext.from_defaults()
service_context = service_context or ServiceContext.from_defaults()
docstore = storage_context.docstore
with service_context.callback_manager.as_trace("index_construction"):
for doc in documents:
docstore.set_document_hash(doc.get_doc_id(), doc.hash)
nodes = run_transformations(
documents, # type: ignore
service_context.transformations,
show_progress=show_progress,
**kwargs,
)
return cls(
nodes=nodes,
storage_context=storage_context,
service_context=service_context,
show_progress=show_progress,
**kwargs,
)
@property
def index_struct(self) -> IS:
"""Get the index struct."""
return self._index_struct
@property
def index_id(self) -> str:
"""Get the index struct."""
return self._index_struct.index_id
def set_index_id(self, index_id: str) -> None:
"""Set the index id.
NOTE: if you decide to set the index_id on the index_struct manually,
you will need to explicitly call `add_index_struct` on the `index_store`
to update the index store.
Args:
index_id (str): Index id to set.
"""
# delete the old index struct
old_id = self._index_struct.index_id
self._storage_context.index_store.delete_index_struct(old_id)
# add the new index struct
self._index_struct.index_id = index_id
self._storage_context.index_store.add_index_struct(self._index_struct)
@property
def docstore(self) -> BaseDocumentStore:
"""Get the docstore corresponding to the index."""
return self._docstore
@property
def service_context(self) -> ServiceContext:
return self._service_context
@property
def storage_context(self) -> StorageContext:
return self._storage_context
@property
def summary(self) -> str:
return str(self._index_struct.summary)
@summary.setter
def summary(self, new_summary: str) -> None:
self._index_struct.summary = new_summary
self._storage_context.index_store.add_index_struct(self._index_struct)
@abstractmethod
def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IS:
"""Build the index from nodes."""
def build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IS:
"""Build the index from nodes."""
self._docstore.add_documents(nodes, allow_update=True)
return self._build_index_from_nodes(nodes)
@abstractmethod
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Index-specific logic for inserting nodes to the index struct."""
def insert_nodes(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert nodes."""
with self._service_context.callback_manager.as_trace("insert_nodes"):
self.docstore.add_documents(nodes, allow_update=True)
self._insert(nodes, **insert_kwargs)
self._storage_context.index_store.add_index_struct(self._index_struct)
def insert(self, document: Document, **insert_kwargs: Any) -> None:
"""Insert a document."""
with self._service_context.callback_manager.as_trace("insert"):
nodes = run_transformations(
[document],
self._service_context.transformations,
show_progress=self._show_progress,
)
self.insert_nodes(nodes, **insert_kwargs)
self.docstore.set_document_hash(document.get_doc_id(), document.hash)
@abstractmethod
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
"""Delete a node."""
def delete_nodes(
self,
node_ids: List[str],
delete_from_docstore: bool = False,
**delete_kwargs: Any,
) -> None:
"""Delete a list of nodes from the index.
Args:
doc_ids (List[str]): A list of doc_ids from the nodes to delete
"""
for node_id in node_ids:
self._delete_node(node_id, **delete_kwargs)
if delete_from_docstore:
self.docstore.delete_document(node_id, raise_error=False)
self._storage_context.index_store.add_index_struct(self._index_struct)
def delete(self, doc_id: str, **delete_kwargs: Any) -> None:
"""Delete a document from the index.
All nodes in the index related to the index will be deleted.
Args:
doc_id (str): A doc_id of the ingested document
"""
logger.warning(
"delete() is now deprecated, please refer to delete_ref_doc() to delete "
"ingested documents+nodes or delete_nodes to delete a list of nodes."
)
self.delete_ref_doc(doc_id)
def delete_ref_doc(
self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
) -> None:
"""Delete a document and it's nodes by using ref_doc_id."""
ref_doc_info = self.docstore.get_ref_doc_info(ref_doc_id)
if ref_doc_info is None:
logger.warning(f"ref_doc_id {ref_doc_id} not found, nothing deleted.")
return
self.delete_nodes(
ref_doc_info.node_ids,
delete_from_docstore=False,
**delete_kwargs,
)
if delete_from_docstore:
self.docstore.delete_ref_doc(ref_doc_id, raise_error=False)
def update(self, document: Document, **update_kwargs: Any) -> None:
"""Update a document and it's corresponding nodes.
This is equivalent to deleting the document and then inserting it again.
Args:
document (Union[BaseDocument, BaseIndex]): document to update
insert_kwargs (Dict): kwargs to pass to insert
delete_kwargs (Dict): kwargs to pass to delete
"""
logger.warning(
"update() is now deprecated, please refer to update_ref_doc() to update "
"ingested documents+nodes."
)
self.update_ref_doc(document, **update_kwargs)
def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None:
"""Update a document and it's corresponding nodes.
This is equivalent to deleting the document and then inserting it again.
Args:
document (Union[BaseDocument, BaseIndex]): document to update
insert_kwargs (Dict): kwargs to pass to insert
delete_kwargs (Dict): kwargs to pass to delete
"""
with self._service_context.callback_manager.as_trace("update"):
self.delete_ref_doc(
document.get_doc_id(),
delete_from_docstore=True,
**update_kwargs.pop("delete_kwargs", {}),
)
self.insert(document, **update_kwargs.pop("insert_kwargs", {}))
def refresh(
self, documents: Sequence[Document], **update_kwargs: Any
) -> List[bool]:
"""Refresh an index with documents that have changed.
This allows users to save LLM and Embedding model calls, while only
updating documents that have any changes in text or metadata. It
will also insert any documents that previously were not stored.
"""
logger.warning(
"refresh() is now deprecated, please refer to refresh_ref_docs() to "
"refresh ingested documents+nodes with an updated list of documents."
)
return self.refresh_ref_docs(documents, **update_kwargs)
def refresh_ref_docs(
self, documents: Sequence[Document], **update_kwargs: Any
) -> List[bool]:
"""Refresh an index with documents that have changed.
This allows users to save LLM and Embedding model calls, while only
updating documents that have any changes in text or metadata. It
will also insert any documents that previously were not stored.
"""
with self._service_context.callback_manager.as_trace("refresh"):
refreshed_documents = [False] * len(documents)
for i, document in enumerate(documents):
existing_doc_hash = self._docstore.get_document_hash(
document.get_doc_id()
)
if existing_doc_hash is None:
self.insert(document, **update_kwargs.pop("insert_kwargs", {}))
refreshed_documents[i] = True
elif existing_doc_hash != document.hash:
self.update_ref_doc(
document, **update_kwargs.pop("update_kwargs", {})
)
refreshed_documents[i] = True
return refreshed_documents
@property
@abstractmethod
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
"""Retrieve a dict mapping of ingested documents and their nodes+metadata."""
...
@abstractmethod
def as_retriever(self, **kwargs: Any) -> BaseRetriever:
...
def as_query_engine(self, **kwargs: Any) -> BaseQueryEngine:
# NOTE: lazy import
from llama_index.legacy.query_engine.retriever_query_engine import (
RetrieverQueryEngine,
)
retriever = self.as_retriever(**kwargs)
kwargs["retriever"] = retriever
if "service_context" not in kwargs:
kwargs["service_context"] = self._service_context
return RetrieverQueryEngine.from_args(**kwargs)
def as_chat_engine(
self, chat_mode: ChatMode = ChatMode.BEST, **kwargs: Any
) -> BaseChatEngine:
query_engine = self.as_query_engine(**kwargs)
if "service_context" not in kwargs:
kwargs["service_context"] = self._service_context
# resolve chat mode
if chat_mode in [ChatMode.REACT, ChatMode.OPENAI, ChatMode.BEST]:
# use an agent with query engine tool in these chat modes
# NOTE: lazy import
from llama_index.legacy.agent import AgentRunner
from llama_index.legacy.tools.query_engine import QueryEngineTool
# get LLM
service_context = cast(ServiceContext, kwargs["service_context"])
llm = service_context.llm
# convert query engine to tool
query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine)
return AgentRunner.from_llm(tools=[query_engine_tool], llm=llm, **kwargs)
if chat_mode == ChatMode.CONDENSE_QUESTION:
# NOTE: lazy import
from llama_index.legacy.chat_engine import CondenseQuestionChatEngine
return CondenseQuestionChatEngine.from_defaults(
query_engine=query_engine,
**kwargs,
)
elif chat_mode == ChatMode.CONTEXT:
from llama_index.legacy.chat_engine import ContextChatEngine
return ContextChatEngine.from_defaults(
retriever=self.as_retriever(**kwargs),
**kwargs,
)
elif chat_mode == ChatMode.CONDENSE_PLUS_CONTEXT:
from llama_index.legacy.chat_engine import CondensePlusContextChatEngine
return CondensePlusContextChatEngine.from_defaults(
retriever=self.as_retriever(**kwargs),
**kwargs,
)
elif chat_mode == ChatMode.SIMPLE:
from llama_index.legacy.chat_engine import SimpleChatEngine
return SimpleChatEngine.from_defaults(
**kwargs,
)
else:
raise ValueError(f"Unknown chat mode: {chat_mode}")
# legacy
BaseGPTIndex = BaseIndex
| [
"llama_index.legacy.agent.AgentRunner.from_llm",
"llama_index.legacy.ingestion.run_transformations",
"llama_index.legacy.query_engine.retriever_query_engine.RetrieverQueryEngine.from_args",
"llama_index.legacy.tools.query_engine.QueryEngineTool.from_defaults",
"llama_index.legacy.storage.storage_context.StorageContext.from_defaults",
"llama_index.legacy.service_context.ServiceContext.from_defaults",
"llama_index.legacy.chat_engine.CondenseQuestionChatEngine.from_defaults",
"llama_index.legacy.chat_engine.SimpleChatEngine.from_defaults"
] | [((793, 825), 'typing.TypeVar', 'TypeVar', (['"""IS"""'], {'bound': 'IndexStruct'}), "('IS', bound=IndexStruct)\n", (800, 825), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar, cast\n'), ((838, 877), 'typing.TypeVar', 'TypeVar', (['"""IndexType"""'], {'bound': '"""BaseIndex"""'}), "('IndexType', bound='BaseIndex')\n", (845, 877), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar, cast\n'), ((888, 915), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (905, 915), False, 'import logging\n'), ((13757, 13797), 'llama_index.legacy.query_engine.retriever_query_engine.RetrieverQueryEngine.from_args', 'RetrieverQueryEngine.from_args', ([], {}), '(**kwargs)\n', (13787, 13797), False, 'from llama_index.legacy.query_engine.retriever_query_engine import RetrieverQueryEngine\n'), ((2626, 2656), 'llama_index.legacy.service_context.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {}), '()\n', (2654, 2656), False, 'from llama_index.legacy.service_context import ServiceContext\n'), ((2708, 2738), 'llama_index.legacy.storage.storage_context.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {}), '()\n', (2736, 2738), False, 'from llama_index.legacy.storage.storage_context import StorageContext\n'), ((4137, 4167), 'llama_index.legacy.storage.storage_context.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {}), '()\n', (4165, 4167), False, 'from llama_index.legacy.storage.storage_context import StorageContext\n'), ((4213, 4243), 'llama_index.legacy.service_context.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {}), '()\n', (4241, 4243), False, 'from llama_index.legacy.service_context import ServiceContext\n'), ((4493, 4599), 'llama_index.legacy.ingestion.run_transformations', 'run_transformations', (['documents', 'service_context.transformations'], {'show_progress': 'show_progress'}), '(documents, service_context.transformations,\n show_progress=show_progress, **kwargs)\n', (4512, 4599), False, 'from llama_index.legacy.ingestion import run_transformations\n'), ((7604, 7713), 'llama_index.legacy.ingestion.run_transformations', 'run_transformations', (['[document]', 'self._service_context.transformations'], {'show_progress': 'self._show_progress'}), '([document], self._service_context.transformations,\n show_progress=self._show_progress)\n', (7623, 7713), False, 'from llama_index.legacy.ingestion import run_transformations\n'), ((14470, 14517), 'typing.cast', 'cast', (['ServiceContext', "kwargs['service_context']"], {}), "(ServiceContext, kwargs['service_context'])\n", (14474, 14517), False, 'from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar, cast\n'), ((14632, 14688), 'llama_index.legacy.tools.query_engine.QueryEngineTool.from_defaults', 'QueryEngineTool.from_defaults', ([], {'query_engine': 'query_engine'}), '(query_engine=query_engine)\n', (14661, 14688), False, 'from llama_index.legacy.tools.query_engine import QueryEngineTool\n'), ((14709, 14775), 'llama_index.legacy.agent.AgentRunner.from_llm', 'AgentRunner.from_llm', ([], {'tools': '[query_engine_tool]', 'llm': 'llm'}), '(tools=[query_engine_tool], llm=llm, **kwargs)\n', (14729, 14775), False, 'from llama_index.legacy.agent import AgentRunner\n'), ((14963, 15040), 'llama_index.legacy.chat_engine.CondenseQuestionChatEngine.from_defaults', 'CondenseQuestionChatEngine.from_defaults', ([], {'query_engine': 'query_engine'}), '(query_engine=query_engine, **kwargs)\n', (15003, 15040), False, 'from llama_index.legacy.chat_engine import CondenseQuestionChatEngine\n'), ((15793, 15833), 'llama_index.legacy.chat_engine.SimpleChatEngine.from_defaults', 'SimpleChatEngine.from_defaults', ([], {}), '(**kwargs)\n', (15823, 15833), False, 'from llama_index.legacy.chat_engine import SimpleChatEngine\n')] |
"""Base index classes."""
import logging
from abc import ABC, abstractmethod
from typing import Any, Dict, Generic, List, Optional, Sequence, Type, TypeVar, cast
from llama_index.legacy.chat_engine.types import BaseChatEngine, ChatMode
from llama_index.legacy.core.base_query_engine import BaseQueryEngine
from llama_index.legacy.core.base_retriever import BaseRetriever
from llama_index.legacy.data_structs.data_structs import IndexStruct
from llama_index.legacy.ingestion import run_transformations
from llama_index.legacy.schema import BaseNode, Document, IndexNode
from llama_index.legacy.service_context import ServiceContext
from llama_index.legacy.storage.docstore.types import BaseDocumentStore, RefDocInfo
from llama_index.legacy.storage.storage_context import StorageContext
IS = TypeVar("IS", bound=IndexStruct)
IndexType = TypeVar("IndexType", bound="BaseIndex")
logger = logging.getLogger(__name__)
class BaseIndex(Generic[IS], ABC):
"""Base LlamaIndex.
Args:
nodes (List[Node]): List of nodes to index
show_progress (bool): Whether to show tqdm progress bars. Defaults to False.
service_context (ServiceContext): Service context container (contains
components like LLM, Embeddings, etc.).
"""
index_struct_cls: Type[IS]
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IS] = None,
storage_context: Optional[StorageContext] = None,
service_context: Optional[ServiceContext] = None,
show_progress: bool = False,
**kwargs: Any,
) -> None:
"""Initialize with parameters."""
if index_struct is None and nodes is None and objects is None:
raise ValueError("One of nodes, objects, or index_struct must be provided.")
if index_struct is not None and nodes is not None:
raise ValueError("Only one of nodes or index_struct can be provided.")
# This is to explicitly make sure that the old UX is not used
if nodes is not None and len(nodes) >= 1 and not isinstance(nodes[0], BaseNode):
if isinstance(nodes[0], Document):
raise ValueError(
"The constructor now takes in a list of Node objects. "
"Since you are passing in a list of Document objects, "
"please use `from_documents` instead."
)
else:
raise ValueError("nodes must be a list of Node objects.")
self._service_context = service_context or ServiceContext.from_defaults()
self._storage_context = storage_context or StorageContext.from_defaults()
self._docstore = self._storage_context.docstore
self._show_progress = show_progress
self._vector_store = self._storage_context.vector_store
self._graph_store = self._storage_context.graph_store
objects = objects or []
self._object_map = {}
for obj in objects:
self._object_map[obj.index_id] = obj.obj
obj.obj = None # clear the object avoid serialization issues
with self._service_context.callback_manager.as_trace("index_construction"):
if index_struct is None:
nodes = nodes or []
index_struct = self.build_index_from_nodes(
nodes + objects # type: ignore
)
self._index_struct = index_struct
self._storage_context.index_store.add_index_struct(self._index_struct)
@classmethod
def from_documents(
cls: Type[IndexType],
documents: Sequence[Document],
storage_context: Optional[StorageContext] = None,
service_context: Optional[ServiceContext] = None,
show_progress: bool = False,
**kwargs: Any,
) -> IndexType:
"""Create index from documents.
Args:
documents (Optional[Sequence[BaseDocument]]): List of documents to
build the index from.
"""
storage_context = storage_context or StorageContext.from_defaults()
service_context = service_context or ServiceContext.from_defaults()
docstore = storage_context.docstore
with service_context.callback_manager.as_trace("index_construction"):
for doc in documents:
docstore.set_document_hash(doc.get_doc_id(), doc.hash)
nodes = run_transformations(
documents, # type: ignore
service_context.transformations,
show_progress=show_progress,
**kwargs,
)
return cls(
nodes=nodes,
storage_context=storage_context,
service_context=service_context,
show_progress=show_progress,
**kwargs,
)
@property
def index_struct(self) -> IS:
"""Get the index struct."""
return self._index_struct
@property
def index_id(self) -> str:
"""Get the index struct."""
return self._index_struct.index_id
def set_index_id(self, index_id: str) -> None:
"""Set the index id.
NOTE: if you decide to set the index_id on the index_struct manually,
you will need to explicitly call `add_index_struct` on the `index_store`
to update the index store.
Args:
index_id (str): Index id to set.
"""
# delete the old index struct
old_id = self._index_struct.index_id
self._storage_context.index_store.delete_index_struct(old_id)
# add the new index struct
self._index_struct.index_id = index_id
self._storage_context.index_store.add_index_struct(self._index_struct)
@property
def docstore(self) -> BaseDocumentStore:
"""Get the docstore corresponding to the index."""
return self._docstore
@property
def service_context(self) -> ServiceContext:
return self._service_context
@property
def storage_context(self) -> StorageContext:
return self._storage_context
@property
def summary(self) -> str:
return str(self._index_struct.summary)
@summary.setter
def summary(self, new_summary: str) -> None:
self._index_struct.summary = new_summary
self._storage_context.index_store.add_index_struct(self._index_struct)
@abstractmethod
def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IS:
"""Build the index from nodes."""
def build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IS:
"""Build the index from nodes."""
self._docstore.add_documents(nodes, allow_update=True)
return self._build_index_from_nodes(nodes)
@abstractmethod
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Index-specific logic for inserting nodes to the index struct."""
def insert_nodes(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert nodes."""
with self._service_context.callback_manager.as_trace("insert_nodes"):
self.docstore.add_documents(nodes, allow_update=True)
self._insert(nodes, **insert_kwargs)
self._storage_context.index_store.add_index_struct(self._index_struct)
def insert(self, document: Document, **insert_kwargs: Any) -> None:
"""Insert a document."""
with self._service_context.callback_manager.as_trace("insert"):
nodes = run_transformations(
[document],
self._service_context.transformations,
show_progress=self._show_progress,
)
self.insert_nodes(nodes, **insert_kwargs)
self.docstore.set_document_hash(document.get_doc_id(), document.hash)
@abstractmethod
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
"""Delete a node."""
def delete_nodes(
self,
node_ids: List[str],
delete_from_docstore: bool = False,
**delete_kwargs: Any,
) -> None:
"""Delete a list of nodes from the index.
Args:
doc_ids (List[str]): A list of doc_ids from the nodes to delete
"""
for node_id in node_ids:
self._delete_node(node_id, **delete_kwargs)
if delete_from_docstore:
self.docstore.delete_document(node_id, raise_error=False)
self._storage_context.index_store.add_index_struct(self._index_struct)
def delete(self, doc_id: str, **delete_kwargs: Any) -> None:
"""Delete a document from the index.
All nodes in the index related to the index will be deleted.
Args:
doc_id (str): A doc_id of the ingested document
"""
logger.warning(
"delete() is now deprecated, please refer to delete_ref_doc() to delete "
"ingested documents+nodes or delete_nodes to delete a list of nodes."
)
self.delete_ref_doc(doc_id)
def delete_ref_doc(
self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any
) -> None:
"""Delete a document and it's nodes by using ref_doc_id."""
ref_doc_info = self.docstore.get_ref_doc_info(ref_doc_id)
if ref_doc_info is None:
logger.warning(f"ref_doc_id {ref_doc_id} not found, nothing deleted.")
return
self.delete_nodes(
ref_doc_info.node_ids,
delete_from_docstore=False,
**delete_kwargs,
)
if delete_from_docstore:
self.docstore.delete_ref_doc(ref_doc_id, raise_error=False)
def update(self, document: Document, **update_kwargs: Any) -> None:
"""Update a document and it's corresponding nodes.
This is equivalent to deleting the document and then inserting it again.
Args:
document (Union[BaseDocument, BaseIndex]): document to update
insert_kwargs (Dict): kwargs to pass to insert
delete_kwargs (Dict): kwargs to pass to delete
"""
logger.warning(
"update() is now deprecated, please refer to update_ref_doc() to update "
"ingested documents+nodes."
)
self.update_ref_doc(document, **update_kwargs)
def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None:
"""Update a document and it's corresponding nodes.
This is equivalent to deleting the document and then inserting it again.
Args:
document (Union[BaseDocument, BaseIndex]): document to update
insert_kwargs (Dict): kwargs to pass to insert
delete_kwargs (Dict): kwargs to pass to delete
"""
with self._service_context.callback_manager.as_trace("update"):
self.delete_ref_doc(
document.get_doc_id(),
delete_from_docstore=True,
**update_kwargs.pop("delete_kwargs", {}),
)
self.insert(document, **update_kwargs.pop("insert_kwargs", {}))
def refresh(
self, documents: Sequence[Document], **update_kwargs: Any
) -> List[bool]:
"""Refresh an index with documents that have changed.
This allows users to save LLM and Embedding model calls, while only
updating documents that have any changes in text or metadata. It
will also insert any documents that previously were not stored.
"""
logger.warning(
"refresh() is now deprecated, please refer to refresh_ref_docs() to "
"refresh ingested documents+nodes with an updated list of documents."
)
return self.refresh_ref_docs(documents, **update_kwargs)
def refresh_ref_docs(
self, documents: Sequence[Document], **update_kwargs: Any
) -> List[bool]:
"""Refresh an index with documents that have changed.
This allows users to save LLM and Embedding model calls, while only
updating documents that have any changes in text or metadata. It
will also insert any documents that previously were not stored.
"""
with self._service_context.callback_manager.as_trace("refresh"):
refreshed_documents = [False] * len(documents)
for i, document in enumerate(documents):
existing_doc_hash = self._docstore.get_document_hash(
document.get_doc_id()
)
if existing_doc_hash is None:
self.insert(document, **update_kwargs.pop("insert_kwargs", {}))
refreshed_documents[i] = True
elif existing_doc_hash != document.hash:
self.update_ref_doc(
document, **update_kwargs.pop("update_kwargs", {})
)
refreshed_documents[i] = True
return refreshed_documents
@property
@abstractmethod
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
"""Retrieve a dict mapping of ingested documents and their nodes+metadata."""
...
@abstractmethod
def as_retriever(self, **kwargs: Any) -> BaseRetriever:
...
def as_query_engine(self, **kwargs: Any) -> BaseQueryEngine:
# NOTE: lazy import
from llama_index.legacy.query_engine.retriever_query_engine import (
RetrieverQueryEngine,
)
retriever = self.as_retriever(**kwargs)
kwargs["retriever"] = retriever
if "service_context" not in kwargs:
kwargs["service_context"] = self._service_context
return RetrieverQueryEngine.from_args(**kwargs)
def as_chat_engine(
self, chat_mode: ChatMode = ChatMode.BEST, **kwargs: Any
) -> BaseChatEngine:
query_engine = self.as_query_engine(**kwargs)
if "service_context" not in kwargs:
kwargs["service_context"] = self._service_context
# resolve chat mode
if chat_mode in [ChatMode.REACT, ChatMode.OPENAI, ChatMode.BEST]:
# use an agent with query engine tool in these chat modes
# NOTE: lazy import
from llama_index.legacy.agent import AgentRunner
from llama_index.legacy.tools.query_engine import QueryEngineTool
# get LLM
service_context = cast(ServiceContext, kwargs["service_context"])
llm = service_context.llm
# convert query engine to tool
query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine)
return AgentRunner.from_llm(tools=[query_engine_tool], llm=llm, **kwargs)
if chat_mode == ChatMode.CONDENSE_QUESTION:
# NOTE: lazy import
from llama_index.legacy.chat_engine import CondenseQuestionChatEngine
return CondenseQuestionChatEngine.from_defaults(
query_engine=query_engine,
**kwargs,
)
elif chat_mode == ChatMode.CONTEXT:
from llama_index.legacy.chat_engine import ContextChatEngine
return ContextChatEngine.from_defaults(
retriever=self.as_retriever(**kwargs),
**kwargs,
)
elif chat_mode == ChatMode.CONDENSE_PLUS_CONTEXT:
from llama_index.legacy.chat_engine import CondensePlusContextChatEngine
return CondensePlusContextChatEngine.from_defaults(
retriever=self.as_retriever(**kwargs),
**kwargs,
)
elif chat_mode == ChatMode.SIMPLE:
from llama_index.legacy.chat_engine import SimpleChatEngine
return SimpleChatEngine.from_defaults(
**kwargs,
)
else:
raise ValueError(f"Unknown chat mode: {chat_mode}")
# legacy
BaseGPTIndex = BaseIndex
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"llama_index.legacy.ingestion.run_transformations",
"llama_index.legacy.query_engine.retriever_query_engine.RetrieverQueryEngine.from_args",
"llama_index.legacy.tools.query_engine.QueryEngineTool.from_defaults",
"llama_index.legacy.storage.storage_context.StorageContext.from_defaults",
"llama_index.legacy.service_context.ServiceContext.from_defaults",
"llama_index.legacy.chat_engine.CondenseQuestionChatEngine.from_defaults",
"llama_index.legacy.chat_engine.SimpleChatEngine.from_defaults"
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import json
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import (
DEFAULT_TEMPERATURE,
)
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.bedrock_utils import (
BEDROCK_FOUNDATION_LLMS,
CHAT_ONLY_MODELS,
STREAMING_MODELS,
Provider,
completion_with_retry,
get_provider,
)
from llama_index.legacy.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class Bedrock(LLM):
model: str = Field(description="The modelId of the Bedrock model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_tokens: int = Field(description="The maximum number of tokens to generate.")
context_size: int = Field("The maximum number of tokens available for input.")
profile_name: Optional[str] = Field(
description="The name of aws profile to use. If not given, then the default profile is used."
)
aws_access_key_id: Optional[str] = Field(
description="AWS Access Key ID to use", exclude=True
)
aws_secret_access_key: Optional[str] = Field(
description="AWS Secret Access Key to use", exclude=True
)
aws_session_token: Optional[str] = Field(
description="AWS Session Token to use", exclude=True
)
region_name: Optional[str] = Field(
description="AWS region name to use. Uses region configured in AWS CLI if not passed",
exclude=True,
)
botocore_session: Optional[Any] = Field(
description="Use this Botocore session instead of creating a new default one.",
exclude=True,
)
botocore_config: Optional[Any] = Field(
description="Custom configuration object to use instead of the default generated one.",
exclude=True,
)
max_retries: int = Field(
default=10, description="The maximum number of API retries.", gt=0
)
timeout: float = Field(
default=60.0,
description="The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.",
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs for the bedrock invokeModel request.",
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
_provider: Provider = PrivateAttr()
def __init__(
self,
model: str,
temperature: Optional[float] = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = 512,
context_size: Optional[int] = None,
profile_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
region_name: Optional[str] = None,
botocore_session: Optional[Any] = None,
client: Optional[Any] = None,
timeout: Optional[float] = 60.0,
max_retries: Optional[int] = 10,
botocore_config: Optional[Any] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
if context_size is None and model not in BEDROCK_FOUNDATION_LLMS:
raise ValueError(
"`context_size` argument not provided and"
"model provided refers to a non-foundation model."
" Please specify the context_size"
)
session_kwargs = {
"profile_name": profile_name,
"region_name": region_name,
"aws_access_key_id": aws_access_key_id,
"aws_secret_access_key": aws_secret_access_key,
"aws_session_token": aws_session_token,
"botocore_session": botocore_session,
}
config = None
try:
import boto3
from botocore.config import Config
config = (
Config(
retries={"max_attempts": max_retries, "mode": "standard"},
connect_timeout=timeout,
read_timeout=timeout,
)
if botocore_config is None
else botocore_config
)
session = boto3.Session(**session_kwargs)
except ImportError:
raise ImportError(
"boto3 package not found, install with" "'pip install boto3'"
)
# Prior to general availability, custom boto3 wheel files were
# distributed that used the bedrock service to invokeModel.
# This check prevents any services still using those wheel files
# from breaking
if client is not None:
self._client = client
elif "bedrock-runtime" in session.get_available_services():
self._client = session.client("bedrock-runtime", config=config)
else:
self._client = session.client("bedrock", config=config)
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
context_size = context_size or BEDROCK_FOUNDATION_LLMS[model]
self._provider = get_provider(model)
messages_to_prompt = messages_to_prompt or self._provider.messages_to_prompt
completion_to_prompt = (
completion_to_prompt or self._provider.completion_to_prompt
)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
context_size=context_size,
profile_name=profile_name,
timeout=timeout,
max_retries=max_retries,
botocore_config=config,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "Bedrock_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=self.context_size,
num_output=self.max_tokens,
is_chat_model=self.model in CHAT_ONLY_MODELS,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
self._provider.max_tokens_key: self.max_tokens,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
if not formatted:
prompt = self.completion_to_prompt(prompt)
all_kwargs = self._get_all_kwargs(**kwargs)
request_body = self._provider.get_request_body(prompt, all_kwargs)
request_body_str = json.dumps(request_body)
response = completion_with_retry(
client=self._client,
model=self.model,
request_body=request_body_str,
max_retries=self.max_retries,
**all_kwargs,
)["body"].read()
response = json.loads(response)
return CompletionResponse(
text=self._provider.get_text_from_response(response), raw=response
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
if self.model in BEDROCK_FOUNDATION_LLMS and self.model not in STREAMING_MODELS:
raise ValueError(f"Model {self.model} does not support streaming")
if not formatted:
prompt = self.completion_to_prompt(prompt)
all_kwargs = self._get_all_kwargs(**kwargs)
request_body = self._provider.get_request_body(prompt, all_kwargs)
request_body_str = json.dumps(request_body)
response = completion_with_retry(
client=self._client,
model=self.model,
request_body=request_body_str,
max_retries=self.max_retries,
stream=True,
**all_kwargs,
)["body"]
def gen() -> CompletionResponseGen:
content = ""
for r in response:
r = json.loads(r["chunk"]["bytes"])
content_delta = self._provider.get_text_from_stream_response(r)
content += content_delta
yield CompletionResponse(text=content, delta=content_delta, raw=r)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""Chat asynchronously."""
# TODO: do synchronous chat for now
return self.chat(messages, **kwargs)
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
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import json
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import (
DEFAULT_TEMPERATURE,
)
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.bedrock_utils import (
BEDROCK_FOUNDATION_LLMS,
CHAT_ONLY_MODELS,
STREAMING_MODELS,
Provider,
completion_with_retry,
get_provider,
)
from llama_index.legacy.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class Bedrock(LLM):
model: str = Field(description="The modelId of the Bedrock model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_tokens: int = Field(description="The maximum number of tokens to generate.")
context_size: int = Field("The maximum number of tokens available for input.")
profile_name: Optional[str] = Field(
description="The name of aws profile to use. If not given, then the default profile is used."
)
aws_access_key_id: Optional[str] = Field(
description="AWS Access Key ID to use", exclude=True
)
aws_secret_access_key: Optional[str] = Field(
description="AWS Secret Access Key to use", exclude=True
)
aws_session_token: Optional[str] = Field(
description="AWS Session Token to use", exclude=True
)
region_name: Optional[str] = Field(
description="AWS region name to use. Uses region configured in AWS CLI if not passed",
exclude=True,
)
botocore_session: Optional[Any] = Field(
description="Use this Botocore session instead of creating a new default one.",
exclude=True,
)
botocore_config: Optional[Any] = Field(
description="Custom configuration object to use instead of the default generated one.",
exclude=True,
)
max_retries: int = Field(
default=10, description="The maximum number of API retries.", gt=0
)
timeout: float = Field(
default=60.0,
description="The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.",
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs for the bedrock invokeModel request.",
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
_provider: Provider = PrivateAttr()
def __init__(
self,
model: str,
temperature: Optional[float] = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = 512,
context_size: Optional[int] = None,
profile_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
region_name: Optional[str] = None,
botocore_session: Optional[Any] = None,
client: Optional[Any] = None,
timeout: Optional[float] = 60.0,
max_retries: Optional[int] = 10,
botocore_config: Optional[Any] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
if context_size is None and model not in BEDROCK_FOUNDATION_LLMS:
raise ValueError(
"`context_size` argument not provided and"
"model provided refers to a non-foundation model."
" Please specify the context_size"
)
session_kwargs = {
"profile_name": profile_name,
"region_name": region_name,
"aws_access_key_id": aws_access_key_id,
"aws_secret_access_key": aws_secret_access_key,
"aws_session_token": aws_session_token,
"botocore_session": botocore_session,
}
config = None
try:
import boto3
from botocore.config import Config
config = (
Config(
retries={"max_attempts": max_retries, "mode": "standard"},
connect_timeout=timeout,
read_timeout=timeout,
)
if botocore_config is None
else botocore_config
)
session = boto3.Session(**session_kwargs)
except ImportError:
raise ImportError(
"boto3 package not found, install with" "'pip install boto3'"
)
# Prior to general availability, custom boto3 wheel files were
# distributed that used the bedrock service to invokeModel.
# This check prevents any services still using those wheel files
# from breaking
if client is not None:
self._client = client
elif "bedrock-runtime" in session.get_available_services():
self._client = session.client("bedrock-runtime", config=config)
else:
self._client = session.client("bedrock", config=config)
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
context_size = context_size or BEDROCK_FOUNDATION_LLMS[model]
self._provider = get_provider(model)
messages_to_prompt = messages_to_prompt or self._provider.messages_to_prompt
completion_to_prompt = (
completion_to_prompt or self._provider.completion_to_prompt
)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
context_size=context_size,
profile_name=profile_name,
timeout=timeout,
max_retries=max_retries,
botocore_config=config,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "Bedrock_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=self.context_size,
num_output=self.max_tokens,
is_chat_model=self.model in CHAT_ONLY_MODELS,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
self._provider.max_tokens_key: self.max_tokens,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
if not formatted:
prompt = self.completion_to_prompt(prompt)
all_kwargs = self._get_all_kwargs(**kwargs)
request_body = self._provider.get_request_body(prompt, all_kwargs)
request_body_str = json.dumps(request_body)
response = completion_with_retry(
client=self._client,
model=self.model,
request_body=request_body_str,
max_retries=self.max_retries,
**all_kwargs,
)["body"].read()
response = json.loads(response)
return CompletionResponse(
text=self._provider.get_text_from_response(response), raw=response
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
if self.model in BEDROCK_FOUNDATION_LLMS and self.model not in STREAMING_MODELS:
raise ValueError(f"Model {self.model} does not support streaming")
if not formatted:
prompt = self.completion_to_prompt(prompt)
all_kwargs = self._get_all_kwargs(**kwargs)
request_body = self._provider.get_request_body(prompt, all_kwargs)
request_body_str = json.dumps(request_body)
response = completion_with_retry(
client=self._client,
model=self.model,
request_body=request_body_str,
max_retries=self.max_retries,
stream=True,
**all_kwargs,
)["body"]
def gen() -> CompletionResponseGen:
content = ""
for r in response:
r = json.loads(r["chunk"]["bytes"])
content_delta = self._provider.get_text_from_stream_response(r)
content += content_delta
yield CompletionResponse(text=content, delta=content_delta, raw=r)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""Chat asynchronously."""
# TODO: do synchronous chat for now
return self.chat(messages, **kwargs)
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.llms.generic_utils.stream_completion_response_to_chat_response",
"llama_index.legacy.llms.generic_utils.completion_response_to_chat_response",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.bedrock_utils.get_provider",
"llama_index.legacy.callbacks.CallbackManager",
"llama_index.legacy.llms.bedrock_utils.completion_with_retry"
] | [((1084, 1145), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The modelId of the Bedrock model to use."""'}), "(description='The modelId of the Bedrock model to use.')\n", (1089, 1145), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1171, 1228), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The temperature to use for sampling."""'}), "(description='The temperature to use for sampling.')\n", (1176, 1228), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1251, 1313), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The maximum number of tokens to generate."""'}), "(description='The maximum number of tokens to generate.')\n", (1256, 1313), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1338, 1396), 'llama_index.legacy.bridge.pydantic.Field', 'Field', (['"""The maximum number of tokens available for input."""'], {}), "('The maximum number of tokens available for input.')\n", (1343, 1396), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1431, 1541), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The name of aws profile to use. If not given, then the default profile is used."""'}), "(description=\n 'The name of aws profile to use. If not given, then the default profile is used.'\n )\n", (1436, 1541), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1585, 1644), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""AWS Access Key ID to use"""', 'exclude': '(True)'}), "(description='AWS Access Key ID to use', exclude=True)\n", (1590, 1644), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1702, 1765), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""AWS Secret Access Key to use"""', 'exclude': '(True)'}), "(description='AWS Secret Access Key to use', exclude=True)\n", (1707, 1765), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1819, 1878), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""AWS Session Token to use"""', 'exclude': '(True)'}), "(description='AWS Session Token to use', exclude=True)\n", (1824, 1878), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1926, 2041), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""AWS region name to use. 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{}), '()\n', (2945, 2947), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((7807, 7832), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (7830, 7832), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((8617, 8642), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (8640, 8642), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((9840, 9859), 'llama_index.legacy.llms.base.llm_chat_callback', 'llm_chat_callback', ([], {}), '()\n', (9857, 9859), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((6106, 6125), 'llama_index.legacy.llms.bedrock_utils.get_provider', 'get_provider', (['model'], {}), '(model)\n', (6118, 6125), False, 'from llama_index.legacy.llms.bedrock_utils import BEDROCK_FOUNDATION_LLMS, 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ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset(
"BraintrustCodaHelpDeskDataset", "./braintrust_codahdd"
)
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 344), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""BraintrustCodaHelpDeskDataset"""', '"""./braintrust_codahdd"""'], {}), "('BraintrustCodaHelpDeskDataset', './braintrust_codahdd')\n", (287, 344), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((403, 455), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (434, 455), False, 'from llama_index.core import VectorStoreIndex\n'), ((548, 603), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack_stuff"""'], {}), "('RagEvaluatorPack', './pack_stuff')\n", (567, 603), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1454, 1478), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1476, 1478), False, 'import asyncio\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset(
"BraintrustCodaHelpDeskDataset", "./braintrust_codahdd"
)
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 344), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""BraintrustCodaHelpDeskDataset"""', '"""./braintrust_codahdd"""'], {}), "('BraintrustCodaHelpDeskDataset', './braintrust_codahdd')\n", (287, 344), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((403, 455), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (434, 455), False, 'from llama_index.core import VectorStoreIndex\n'), ((548, 603), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack_stuff"""'], {}), "('RagEvaluatorPack', './pack_stuff')\n", (567, 603), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1454, 1478), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1476, 1478), False, 'import asyncio\n')] |
from typing import Any, Dict, Optional
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.constants import (
DEFAULT_NUM_OUTPUTS,
DEFAULT_TEMPERATURE,
)
from llama_index.legacy.core.llms.types import LLMMetadata
from llama_index.legacy.llms.generic_utils import get_from_param_or_env
from llama_index.legacy.llms.openai_like import OpenAILike
DEFAULT_API_BASE = "https://router.neutrinoapp.com/api/llm-router"
DEFAULT_ROUTER = "default"
MAX_CONTEXT_WINDOW = 200000
class Neutrino(OpenAILike):
model: str = Field(
description="The Neutrino router to use. See https://docs.neutrinoapp.com/router for details."
)
context_window: int = Field(
default=MAX_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model. Defaults to the largest supported model (Claude).",
gt=0,
)
is_chat_model: bool = Field(
default=True,
description=LLMMetadata.__fields__["is_chat_model"].field_info.description,
)
def __init__(
self,
model: Optional[str] = None,
router: str = DEFAULT_ROUTER,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 5,
api_base: Optional[str] = DEFAULT_API_BASE,
api_key: Optional[str] = None,
**kwargs: Any,
) -> None:
additional_kwargs = additional_kwargs or {}
api_base = get_from_param_or_env("api_base", api_base, "NEUTRINO_API_BASE")
api_key = get_from_param_or_env("api_key", api_key, "NEUTRINO_API_KEY")
model = model or router
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
api_base=api_base,
api_key=api_key,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
**kwargs,
)
@classmethod
def class_name(cls) -> str:
return "Neutrino_LLM"
| [
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.llms.generic_utils.get_from_param_or_env"
] | [((548, 659), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Neutrino router to use. See https://docs.neutrinoapp.com/router for details."""'}), "(description=\n 'The Neutrino router to use. See https://docs.neutrinoapp.com/router for details.'\n )\n", (553, 659), False, 'from llama_index.legacy.bridge.pydantic import Field\n'), ((690, 856), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': 'MAX_CONTEXT_WINDOW', 'description': '"""The maximum number of context tokens for the model. Defaults to the largest supported model (Claude)."""', 'gt': '(0)'}), "(default=MAX_CONTEXT_WINDOW, description=\n 'The maximum number of context tokens for the model. Defaults to the largest supported model (Claude).'\n , gt=0)\n", (695, 856), False, 'from llama_index.legacy.bridge.pydantic import Field\n'), ((904, 1004), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': '(True)', 'description': "LLMMetadata.__fields__['is_chat_model'].field_info.description"}), "(default=True, description=LLMMetadata.__fields__['is_chat_model'].\n field_info.description)\n", (909, 1004), False, 'from llama_index.legacy.bridge.pydantic import Field\n'), ((1519, 1583), 'llama_index.legacy.llms.generic_utils.get_from_param_or_env', 'get_from_param_or_env', (['"""api_base"""', 'api_base', '"""NEUTRINO_API_BASE"""'], {}), "('api_base', api_base, 'NEUTRINO_API_BASE')\n", (1540, 1583), False, 'from llama_index.legacy.llms.generic_utils import get_from_param_or_env\n'), ((1602, 1663), 'llama_index.legacy.llms.generic_utils.get_from_param_or_env', 'get_from_param_or_env', (['"""api_key"""', 'api_key', '"""NEUTRINO_API_KEY"""'], {}), "('api_key', api_key, 'NEUTRINO_API_KEY')\n", (1623, 1663), False, 'from llama_index.legacy.llms.generic_utils import get_from_param_or_env\n')] |
"""Tree-based index."""
from enum import Enum
from typing import Any, Dict, Optional, Sequence, Union
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.embeddings.base import BaseEmbedding
# from llama_index.core.data_structs.data_structs import IndexGraph
from llama_index.core.data_structs.data_structs import IndexGraph
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.common_tree.base import GPTTreeIndexBuilder
from llama_index.core.indices.tree.inserter import TreeIndexInserter
from llama_index.core.llms.llm import LLM
from llama_index.core.prompts import BasePromptTemplate
from llama_index.core.prompts.default_prompts import (
DEFAULT_INSERT_PROMPT,
DEFAULT_SUMMARY_PROMPT,
)
from llama_index.core.schema import BaseNode, IndexNode
from llama_index.core.service_context import ServiceContext
from llama_index.core.settings import (
Settings,
embed_model_from_settings_or_context,
llm_from_settings_or_context,
)
from llama_index.core.storage.docstore.types import RefDocInfo
class TreeRetrieverMode(str, Enum):
SELECT_LEAF = "select_leaf"
SELECT_LEAF_EMBEDDING = "select_leaf_embedding"
ALL_LEAF = "all_leaf"
ROOT = "root"
REQUIRE_TREE_MODES = {
TreeRetrieverMode.SELECT_LEAF,
TreeRetrieverMode.SELECT_LEAF_EMBEDDING,
TreeRetrieverMode.ROOT,
}
class TreeIndex(BaseIndex[IndexGraph]):
"""Tree Index.
The tree index is a tree-structured index, where each node is a summary of
the children nodes. During index construction, the tree is constructed
in a bottoms-up fashion until we end up with a set of root_nodes.
There are a few different options during query time (see :ref:`Ref-Query`).
The main option is to traverse down the tree from the root nodes.
A secondary answer is to directly synthesize the answer from the root nodes.
Args:
summary_template (Optional[BasePromptTemplate]): A Summarization Prompt
(see :ref:`Prompt-Templates`).
insert_prompt (Optional[BasePromptTemplate]): An Tree Insertion Prompt
(see :ref:`Prompt-Templates`).
num_children (int): The number of children each node should have.
build_tree (bool): Whether to build the tree during index construction.
show_progress (bool): Whether to show progress bars. Defaults to False.
"""
index_struct_cls = IndexGraph
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IndexGraph] = None,
llm: Optional[LLM] = None,
summary_template: Optional[BasePromptTemplate] = None,
insert_prompt: Optional[BasePromptTemplate] = None,
num_children: int = 10,
build_tree: bool = True,
use_async: bool = False,
show_progress: bool = False,
# deprecated
service_context: Optional[ServiceContext] = None,
**kwargs: Any,
) -> None:
"""Initialize params."""
# need to set parameters before building index in base class.
self.num_children = num_children
self.summary_template = summary_template or DEFAULT_SUMMARY_PROMPT
self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT
self.build_tree = build_tree
self._use_async = use_async
self._llm = llm or llm_from_settings_or_context(Settings, service_context)
super().__init__(
nodes=nodes,
index_struct=index_struct,
service_context=service_context,
show_progress=show_progress,
objects=objects,
**kwargs,
)
def as_retriever(
self,
retriever_mode: Union[str, TreeRetrieverMode] = TreeRetrieverMode.SELECT_LEAF,
embed_model: Optional[BaseEmbedding] = None,
**kwargs: Any,
) -> BaseRetriever:
# NOTE: lazy import
from llama_index.core.indices.tree.all_leaf_retriever import (
TreeAllLeafRetriever,
)
from llama_index.core.indices.tree.select_leaf_embedding_retriever import (
TreeSelectLeafEmbeddingRetriever,
)
from llama_index.core.indices.tree.select_leaf_retriever import (
TreeSelectLeafRetriever,
)
from llama_index.core.indices.tree.tree_root_retriever import (
TreeRootRetriever,
)
self._validate_build_tree_required(TreeRetrieverMode(retriever_mode))
if retriever_mode == TreeRetrieverMode.SELECT_LEAF:
return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs)
elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING:
embed_model = embed_model or embed_model_from_settings_or_context(
Settings, self._service_context
)
return TreeSelectLeafEmbeddingRetriever(
self, embed_model=embed_model, object_map=self._object_map, **kwargs
)
elif retriever_mode == TreeRetrieverMode.ROOT:
return TreeRootRetriever(self, object_map=self._object_map, **kwargs)
elif retriever_mode == TreeRetrieverMode.ALL_LEAF:
return TreeAllLeafRetriever(self, object_map=self._object_map, **kwargs)
else:
raise ValueError(f"Unknown retriever mode: {retriever_mode}")
def _validate_build_tree_required(self, retriever_mode: TreeRetrieverMode) -> None:
"""Check if index supports modes that require trees."""
if retriever_mode in REQUIRE_TREE_MODES and not self.build_tree:
raise ValueError(
"Index was constructed without building trees, "
f"but retriever mode {retriever_mode} requires trees."
)
def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph:
"""Build the index from nodes."""
index_builder = GPTTreeIndexBuilder(
self.num_children,
self.summary_template,
service_context=self.service_context,
llm=self._llm,
use_async=self._use_async,
show_progress=self._show_progress,
docstore=self._docstore,
)
return index_builder.build_from_nodes(nodes, build_tree=self.build_tree)
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert a document."""
# TODO: allow to customize insert prompt
inserter = TreeIndexInserter(
self.index_struct,
service_context=self.service_context,
llm=self._llm,
num_children=self.num_children,
insert_prompt=self.insert_prompt,
summary_prompt=self.summary_template,
docstore=self._docstore,
)
inserter.insert(nodes)
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
"""Delete a node."""
raise NotImplementedError("Delete not implemented for tree index.")
@property
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
"""Retrieve a dict mapping of ingested documents and their nodes+metadata."""
node_doc_ids = list(self.index_struct.all_nodes.values())
nodes = self.docstore.get_nodes(node_doc_ids)
all_ref_doc_info = {}
for node in nodes:
ref_node = node.source_node
if not ref_node:
continue
ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id)
if not ref_doc_info:
continue
all_ref_doc_info[ref_node.node_id] = ref_doc_info
return all_ref_doc_info
# legacy
GPTTreeIndex = TreeIndex
| [
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"llama_index.core.indices.tree.all_leaf_retriever.TreeAllLeafRetriever",
"llama_index.core.settings.embed_model_from_settings_or_context",
"llama_index.core.indices.common_tree.base.GPTTreeIndexBuilder"
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"""Tree-based index."""
from enum import Enum
from typing import Any, Dict, Optional, Sequence, Union
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.embeddings.base import BaseEmbedding
# from llama_index.core.data_structs.data_structs import IndexGraph
from llama_index.core.data_structs.data_structs import IndexGraph
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.common_tree.base import GPTTreeIndexBuilder
from llama_index.core.indices.tree.inserter import TreeIndexInserter
from llama_index.core.llms.llm import LLM
from llama_index.core.prompts import BasePromptTemplate
from llama_index.core.prompts.default_prompts import (
DEFAULT_INSERT_PROMPT,
DEFAULT_SUMMARY_PROMPT,
)
from llama_index.core.schema import BaseNode, IndexNode
from llama_index.core.service_context import ServiceContext
from llama_index.core.settings import (
Settings,
embed_model_from_settings_or_context,
llm_from_settings_or_context,
)
from llama_index.core.storage.docstore.types import RefDocInfo
class TreeRetrieverMode(str, Enum):
SELECT_LEAF = "select_leaf"
SELECT_LEAF_EMBEDDING = "select_leaf_embedding"
ALL_LEAF = "all_leaf"
ROOT = "root"
REQUIRE_TREE_MODES = {
TreeRetrieverMode.SELECT_LEAF,
TreeRetrieverMode.SELECT_LEAF_EMBEDDING,
TreeRetrieverMode.ROOT,
}
class TreeIndex(BaseIndex[IndexGraph]):
"""Tree Index.
The tree index is a tree-structured index, where each node is a summary of
the children nodes. During index construction, the tree is constructed
in a bottoms-up fashion until we end up with a set of root_nodes.
There are a few different options during query time (see :ref:`Ref-Query`).
The main option is to traverse down the tree from the root nodes.
A secondary answer is to directly synthesize the answer from the root nodes.
Args:
summary_template (Optional[BasePromptTemplate]): A Summarization Prompt
(see :ref:`Prompt-Templates`).
insert_prompt (Optional[BasePromptTemplate]): An Tree Insertion Prompt
(see :ref:`Prompt-Templates`).
num_children (int): The number of children each node should have.
build_tree (bool): Whether to build the tree during index construction.
show_progress (bool): Whether to show progress bars. Defaults to False.
"""
index_struct_cls = IndexGraph
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IndexGraph] = None,
llm: Optional[LLM] = None,
summary_template: Optional[BasePromptTemplate] = None,
insert_prompt: Optional[BasePromptTemplate] = None,
num_children: int = 10,
build_tree: bool = True,
use_async: bool = False,
show_progress: bool = False,
# deprecated
service_context: Optional[ServiceContext] = None,
**kwargs: Any,
) -> None:
"""Initialize params."""
# need to set parameters before building index in base class.
self.num_children = num_children
self.summary_template = summary_template or DEFAULT_SUMMARY_PROMPT
self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT
self.build_tree = build_tree
self._use_async = use_async
self._llm = llm or llm_from_settings_or_context(Settings, service_context)
super().__init__(
nodes=nodes,
index_struct=index_struct,
service_context=service_context,
show_progress=show_progress,
objects=objects,
**kwargs,
)
def as_retriever(
self,
retriever_mode: Union[str, TreeRetrieverMode] = TreeRetrieverMode.SELECT_LEAF,
embed_model: Optional[BaseEmbedding] = None,
**kwargs: Any,
) -> BaseRetriever:
# NOTE: lazy import
from llama_index.core.indices.tree.all_leaf_retriever import (
TreeAllLeafRetriever,
)
from llama_index.core.indices.tree.select_leaf_embedding_retriever import (
TreeSelectLeafEmbeddingRetriever,
)
from llama_index.core.indices.tree.select_leaf_retriever import (
TreeSelectLeafRetriever,
)
from llama_index.core.indices.tree.tree_root_retriever import (
TreeRootRetriever,
)
self._validate_build_tree_required(TreeRetrieverMode(retriever_mode))
if retriever_mode == TreeRetrieverMode.SELECT_LEAF:
return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs)
elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING:
embed_model = embed_model or embed_model_from_settings_or_context(
Settings, self._service_context
)
return TreeSelectLeafEmbeddingRetriever(
self, embed_model=embed_model, object_map=self._object_map, **kwargs
)
elif retriever_mode == TreeRetrieverMode.ROOT:
return TreeRootRetriever(self, object_map=self._object_map, **kwargs)
elif retriever_mode == TreeRetrieverMode.ALL_LEAF:
return TreeAllLeafRetriever(self, object_map=self._object_map, **kwargs)
else:
raise ValueError(f"Unknown retriever mode: {retriever_mode}")
def _validate_build_tree_required(self, retriever_mode: TreeRetrieverMode) -> None:
"""Check if index supports modes that require trees."""
if retriever_mode in REQUIRE_TREE_MODES and not self.build_tree:
raise ValueError(
"Index was constructed without building trees, "
f"but retriever mode {retriever_mode} requires trees."
)
def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph:
"""Build the index from nodes."""
index_builder = GPTTreeIndexBuilder(
self.num_children,
self.summary_template,
service_context=self.service_context,
llm=self._llm,
use_async=self._use_async,
show_progress=self._show_progress,
docstore=self._docstore,
)
return index_builder.build_from_nodes(nodes, build_tree=self.build_tree)
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert a document."""
# TODO: allow to customize insert prompt
inserter = TreeIndexInserter(
self.index_struct,
service_context=self.service_context,
llm=self._llm,
num_children=self.num_children,
insert_prompt=self.insert_prompt,
summary_prompt=self.summary_template,
docstore=self._docstore,
)
inserter.insert(nodes)
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
"""Delete a node."""
raise NotImplementedError("Delete not implemented for tree index.")
@property
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
"""Retrieve a dict mapping of ingested documents and their nodes+metadata."""
node_doc_ids = list(self.index_struct.all_nodes.values())
nodes = self.docstore.get_nodes(node_doc_ids)
all_ref_doc_info = {}
for node in nodes:
ref_node = node.source_node
if not ref_node:
continue
ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id)
if not ref_doc_info:
continue
all_ref_doc_info[ref_node.node_id] = ref_doc_info
return all_ref_doc_info
# legacy
GPTTreeIndex = TreeIndex
| [
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"""Tree-based index."""
from enum import Enum
from typing import Any, Dict, Optional, Sequence, Union
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.embeddings.base import BaseEmbedding
# from llama_index.core.data_structs.data_structs import IndexGraph
from llama_index.core.data_structs.data_structs import IndexGraph
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.common_tree.base import GPTTreeIndexBuilder
from llama_index.core.indices.tree.inserter import TreeIndexInserter
from llama_index.core.llms.llm import LLM
from llama_index.core.prompts import BasePromptTemplate
from llama_index.core.prompts.default_prompts import (
DEFAULT_INSERT_PROMPT,
DEFAULT_SUMMARY_PROMPT,
)
from llama_index.core.schema import BaseNode, IndexNode
from llama_index.core.service_context import ServiceContext
from llama_index.core.settings import (
Settings,
embed_model_from_settings_or_context,
llm_from_settings_or_context,
)
from llama_index.core.storage.docstore.types import RefDocInfo
class TreeRetrieverMode(str, Enum):
SELECT_LEAF = "select_leaf"
SELECT_LEAF_EMBEDDING = "select_leaf_embedding"
ALL_LEAF = "all_leaf"
ROOT = "root"
REQUIRE_TREE_MODES = {
TreeRetrieverMode.SELECT_LEAF,
TreeRetrieverMode.SELECT_LEAF_EMBEDDING,
TreeRetrieverMode.ROOT,
}
class TreeIndex(BaseIndex[IndexGraph]):
"""Tree Index.
The tree index is a tree-structured index, where each node is a summary of
the children nodes. During index construction, the tree is constructed
in a bottoms-up fashion until we end up with a set of root_nodes.
There are a few different options during query time (see :ref:`Ref-Query`).
The main option is to traverse down the tree from the root nodes.
A secondary answer is to directly synthesize the answer from the root nodes.
Args:
summary_template (Optional[BasePromptTemplate]): A Summarization Prompt
(see :ref:`Prompt-Templates`).
insert_prompt (Optional[BasePromptTemplate]): An Tree Insertion Prompt
(see :ref:`Prompt-Templates`).
num_children (int): The number of children each node should have.
build_tree (bool): Whether to build the tree during index construction.
show_progress (bool): Whether to show progress bars. Defaults to False.
"""
index_struct_cls = IndexGraph
def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
objects: Optional[Sequence[IndexNode]] = None,
index_struct: Optional[IndexGraph] = None,
llm: Optional[LLM] = None,
summary_template: Optional[BasePromptTemplate] = None,
insert_prompt: Optional[BasePromptTemplate] = None,
num_children: int = 10,
build_tree: bool = True,
use_async: bool = False,
show_progress: bool = False,
# deprecated
service_context: Optional[ServiceContext] = None,
**kwargs: Any,
) -> None:
"""Initialize params."""
# need to set parameters before building index in base class.
self.num_children = num_children
self.summary_template = summary_template or DEFAULT_SUMMARY_PROMPT
self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT
self.build_tree = build_tree
self._use_async = use_async
self._llm = llm or llm_from_settings_or_context(Settings, service_context)
super().__init__(
nodes=nodes,
index_struct=index_struct,
service_context=service_context,
show_progress=show_progress,
objects=objects,
**kwargs,
)
def as_retriever(
self,
retriever_mode: Union[str, TreeRetrieverMode] = TreeRetrieverMode.SELECT_LEAF,
embed_model: Optional[BaseEmbedding] = None,
**kwargs: Any,
) -> BaseRetriever:
# NOTE: lazy import
from llama_index.core.indices.tree.all_leaf_retriever import (
TreeAllLeafRetriever,
)
from llama_index.core.indices.tree.select_leaf_embedding_retriever import (
TreeSelectLeafEmbeddingRetriever,
)
from llama_index.core.indices.tree.select_leaf_retriever import (
TreeSelectLeafRetriever,
)
from llama_index.core.indices.tree.tree_root_retriever import (
TreeRootRetriever,
)
self._validate_build_tree_required(TreeRetrieverMode(retriever_mode))
if retriever_mode == TreeRetrieverMode.SELECT_LEAF:
return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs)
elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING:
embed_model = embed_model or embed_model_from_settings_or_context(
Settings, self._service_context
)
return TreeSelectLeafEmbeddingRetriever(
self, embed_model=embed_model, object_map=self._object_map, **kwargs
)
elif retriever_mode == TreeRetrieverMode.ROOT:
return TreeRootRetriever(self, object_map=self._object_map, **kwargs)
elif retriever_mode == TreeRetrieverMode.ALL_LEAF:
return TreeAllLeafRetriever(self, object_map=self._object_map, **kwargs)
else:
raise ValueError(f"Unknown retriever mode: {retriever_mode}")
def _validate_build_tree_required(self, retriever_mode: TreeRetrieverMode) -> None:
"""Check if index supports modes that require trees."""
if retriever_mode in REQUIRE_TREE_MODES and not self.build_tree:
raise ValueError(
"Index was constructed without building trees, "
f"but retriever mode {retriever_mode} requires trees."
)
def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph:
"""Build the index from nodes."""
index_builder = GPTTreeIndexBuilder(
self.num_children,
self.summary_template,
service_context=self.service_context,
llm=self._llm,
use_async=self._use_async,
show_progress=self._show_progress,
docstore=self._docstore,
)
return index_builder.build_from_nodes(nodes, build_tree=self.build_tree)
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert a document."""
# TODO: allow to customize insert prompt
inserter = TreeIndexInserter(
self.index_struct,
service_context=self.service_context,
llm=self._llm,
num_children=self.num_children,
insert_prompt=self.insert_prompt,
summary_prompt=self.summary_template,
docstore=self._docstore,
)
inserter.insert(nodes)
def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None:
"""Delete a node."""
raise NotImplementedError("Delete not implemented for tree index.")
@property
def ref_doc_info(self) -> Dict[str, RefDocInfo]:
"""Retrieve a dict mapping of ingested documents and their nodes+metadata."""
node_doc_ids = list(self.index_struct.all_nodes.values())
nodes = self.docstore.get_nodes(node_doc_ids)
all_ref_doc_info = {}
for node in nodes:
ref_node = node.source_node
if not ref_node:
continue
ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id)
if not ref_doc_info:
continue
all_ref_doc_info[ref_node.node_id] = ref_doc_info
return all_ref_doc_info
# legacy
GPTTreeIndex = TreeIndex
| [
"llama_index.core.settings.llm_from_settings_or_context",
"llama_index.core.indices.tree.tree_root_retriever.TreeRootRetriever",
"llama_index.core.indices.tree.select_leaf_retriever.TreeSelectLeafRetriever",
"llama_index.core.indices.tree.inserter.TreeIndexInserter",
"llama_index.core.indices.tree.select_leaf_embedding_retriever.TreeSelectLeafEmbeddingRetriever",
"llama_index.core.indices.tree.all_leaf_retriever.TreeAllLeafRetriever",
"llama_index.core.settings.embed_model_from_settings_or_context",
"llama_index.core.indices.common_tree.base.GPTTreeIndexBuilder"
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from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
EXAMPLE_URL = "https://clarifai.com/anthropic/completion/models/claude-v2"
class Clarifai(LLM):
model_url: Optional[str] = Field(
description=f"Full URL of the model. e.g. `{EXAMPLE_URL}`"
)
model_version_id: Optional[str] = Field(description="Model Version ID.")
app_id: Optional[str] = Field(description="Clarifai application ID of the model.")
user_id: Optional[str] = Field(description="Clarifai user ID of the model.")
pat: Optional[str] = Field(
description="Personal Access Tokens(PAT) to validate requests."
)
_model: Any = PrivateAttr()
_is_chat_model: bool = PrivateAttr()
def __init__(
self,
model_name: Optional[str] = None,
model_url: Optional[str] = None,
model_version_id: Optional[str] = "",
app_id: Optional[str] = None,
user_id: Optional[str] = None,
pat: Optional[str] = None,
temperature: float = 0.1,
max_tokens: int = 512,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
):
try:
import os
from clarifai.client.model import Model
except ImportError:
raise ImportError("ClarifaiLLM requires `pip install clarifai`.")
if pat is None and os.environ.get("CLARIFAI_PAT") is not None:
pat = os.environ.get("CLARIFAI_PAT")
if not pat and os.environ.get("CLARIFAI_PAT") is None:
raise ValueError(
"Set `CLARIFAI_PAT` as env variable or pass `pat` as constructor argument"
)
if model_url is not None and model_name is not None:
raise ValueError("You can only specify one of model_url or model_name.")
if model_url is None and model_name is None:
raise ValueError("You must specify one of model_url or model_name.")
if model_name is not None:
if app_id is None or user_id is None:
raise ValueError(
f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}"
)
else:
self._model = Model(
user_id=user_id,
app_id=app_id,
model_id=model_name,
model_version={"id": model_version_id},
pat=pat,
)
if model_url is not None:
self._model = Model(model_url, pat=pat)
model_name = self._model.id
self._is_chat_model = False
if "chat" in self._model.app_id or "chat" in self._model.id:
self._is_chat_model = True
additional_kwargs = additional_kwargs or {}
super().__init__(
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
model_name=model_name,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "ClarifaiLLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_tokens,
model_name=self._model,
is_chat_model=self._is_chat_model,
)
# TODO: When the Clarifai python SDK supports inference params, add here.
def chat(
self,
messages: Sequence[ChatMessage],
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> ChatResponse:
"""Chat endpoint for LLM."""
prompt = "".join([str(m) for m in messages])
try:
response = (
self._model.predict_by_bytes(
input_bytes=prompt.encode(encoding="UTF-8"),
input_type="text",
inference_params=inference_params,
)
.outputs[0]
.data.text.raw
)
except Exception as e:
raise Exception(f"Prediction failed: {e}")
return ChatResponse(message=ChatMessage(content=response))
def complete(
self,
prompt: str,
formatted: bool = False,
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> CompletionResponse:
"""Completion endpoint for LLM."""
try:
response = (
self._model.predict_by_bytes(
input_bytes=prompt.encode(encoding="utf-8"),
input_type="text",
inference_params=inference_params,
)
.outputs[0]
.data.text.raw
)
except Exception as e:
raise Exception(f"Prediction failed: {e}")
return CompletionResponse(text=response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError(
"Clarifai does not currently support streaming completion."
)
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError(
"Clarifai does not currently support streaming completion."
)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
raise NotImplementedError("Currently not supported.")
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return self.complete(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError("Currently not supported.")
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError("Clarifai does not currently support this function.")
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"llama_index.legacy.bridge.pydantic.Field",
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from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import (
llm_chat_callback,
llm_completion_callback,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
EXAMPLE_URL = "https://clarifai.com/anthropic/completion/models/claude-v2"
class Clarifai(LLM):
model_url: Optional[str] = Field(
description=f"Full URL of the model. e.g. `{EXAMPLE_URL}`"
)
model_version_id: Optional[str] = Field(description="Model Version ID.")
app_id: Optional[str] = Field(description="Clarifai application ID of the model.")
user_id: Optional[str] = Field(description="Clarifai user ID of the model.")
pat: Optional[str] = Field(
description="Personal Access Tokens(PAT) to validate requests."
)
_model: Any = PrivateAttr()
_is_chat_model: bool = PrivateAttr()
def __init__(
self,
model_name: Optional[str] = None,
model_url: Optional[str] = None,
model_version_id: Optional[str] = "",
app_id: Optional[str] = None,
user_id: Optional[str] = None,
pat: Optional[str] = None,
temperature: float = 0.1,
max_tokens: int = 512,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
):
try:
import os
from clarifai.client.model import Model
except ImportError:
raise ImportError("ClarifaiLLM requires `pip install clarifai`.")
if pat is None and os.environ.get("CLARIFAI_PAT") is not None:
pat = os.environ.get("CLARIFAI_PAT")
if not pat and os.environ.get("CLARIFAI_PAT") is None:
raise ValueError(
"Set `CLARIFAI_PAT` as env variable or pass `pat` as constructor argument"
)
if model_url is not None and model_name is not None:
raise ValueError("You can only specify one of model_url or model_name.")
if model_url is None and model_name is None:
raise ValueError("You must specify one of model_url or model_name.")
if model_name is not None:
if app_id is None or user_id is None:
raise ValueError(
f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}"
)
else:
self._model = Model(
user_id=user_id,
app_id=app_id,
model_id=model_name,
model_version={"id": model_version_id},
pat=pat,
)
if model_url is not None:
self._model = Model(model_url, pat=pat)
model_name = self._model.id
self._is_chat_model = False
if "chat" in self._model.app_id or "chat" in self._model.id:
self._is_chat_model = True
additional_kwargs = additional_kwargs or {}
super().__init__(
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
model_name=model_name,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "ClarifaiLLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_tokens,
model_name=self._model,
is_chat_model=self._is_chat_model,
)
# TODO: When the Clarifai python SDK supports inference params, add here.
def chat(
self,
messages: Sequence[ChatMessage],
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> ChatResponse:
"""Chat endpoint for LLM."""
prompt = "".join([str(m) for m in messages])
try:
response = (
self._model.predict_by_bytes(
input_bytes=prompt.encode(encoding="UTF-8"),
input_type="text",
inference_params=inference_params,
)
.outputs[0]
.data.text.raw
)
except Exception as e:
raise Exception(f"Prediction failed: {e}")
return ChatResponse(message=ChatMessage(content=response))
def complete(
self,
prompt: str,
formatted: bool = False,
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> CompletionResponse:
"""Completion endpoint for LLM."""
try:
response = (
self._model.predict_by_bytes(
input_bytes=prompt.encode(encoding="utf-8"),
input_type="text",
inference_params=inference_params,
)
.outputs[0]
.data.text.raw
)
except Exception as e:
raise Exception(f"Prediction failed: {e}")
return CompletionResponse(text=response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError(
"Clarifai does not currently support streaming completion."
)
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError(
"Clarifai does not currently support streaming completion."
)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
raise NotImplementedError("Currently not supported.")
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return self.complete(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError("Currently not supported.")
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError("Clarifai does not currently support this function.")
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"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.core.llms.types.ChatMessage",
"llama_index.legacy.bridge.pydantic.PrivateAttr"
] | [((762, 827), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': 'f"""Full URL of the model. e.g. `{EXAMPLE_URL}`"""'}), "(description=f'Full URL of the model. e.g. `{EXAMPLE_URL}`')\n", (767, 827), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((880, 918), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""Model Version ID."""'}), "(description='Model Version ID.')\n", (885, 918), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((947, 1005), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""Clarifai application ID of the model."""'}), "(description='Clarifai application ID of the model.')\n", (952, 1005), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1035, 1086), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""Clarifai user ID of the model."""'}), "(description='Clarifai user ID of the model.')\n", (1040, 1086), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1112, 1182), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""Personal Access Tokens(PAT) to validate requests."""'}), "(description='Personal Access Tokens(PAT) to validate requests.')\n", (1117, 1182), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1216, 1229), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (1227, 1229), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1257, 1270), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (1268, 1270), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((6542, 6561), 'llama_index.legacy.llms.base.llm_chat_callback', 'llm_chat_callback', ([], {}), '()\n', (6559, 6561), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((6735, 6760), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (6758, 6760), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((6934, 6953), 'llama_index.legacy.llms.base.llm_chat_callback', 'llm_chat_callback', ([], {}), '()\n', (6951, 6953), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((7142, 7167), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (7165, 7167), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((4359, 4497), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'context_window': 'self.context_window', 'num_output': 'self.max_tokens', 'model_name': 'self._model', 'is_chat_model': 'self._is_chat_model'}), '(context_window=self.context_window, num_output=self.max_tokens,\n model_name=self._model, is_chat_model=self._is_chat_model)\n', (4370, 4497), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n'), ((6035, 6068), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'response'}), '(text=response)\n', (6053, 6068), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n'), ((2360, 2390), 'os.environ.get', 'os.environ.get', (['"""CLARIFAI_PAT"""'], {}), "('CLARIFAI_PAT')\n", (2374, 2390), False, 'import os\n'), ((3434, 3459), 'clarifai.client.model.Model', 'Model', (['model_url'], {'pat': 'pat'}), '(model_url, pat=pat)\n', (3439, 3459), False, 'from clarifai.client.model import Model\n'), ((2298, 2328), 'os.environ.get', 'os.environ.get', (['"""CLARIFAI_PAT"""'], {}), "('CLARIFAI_PAT')\n", (2312, 2328), False, 'import os\n'), ((2415, 2445), 'os.environ.get', 'os.environ.get', (['"""CLARIFAI_PAT"""'], {}), "('CLARIFAI_PAT')\n", (2429, 2445), False, 'import os\n'), ((3146, 3258), 'clarifai.client.model.Model', 'Model', ([], {'user_id': 'user_id', 'app_id': 'app_id', 'model_id': 'model_name', 'model_version': "{'id': model_version_id}", 'pat': 'pat'}), "(user_id=user_id, app_id=app_id, model_id=model_name, model_version={\n 'id': model_version_id}, pat=pat)\n", (3151, 3258), False, 'from clarifai.client.model import Model\n'), ((5340, 5369), 'llama_index.legacy.core.llms.types.ChatMessage', 'ChatMessage', ([], {'content': 'response'}), '(content=response)\n', (5351, 5369), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata\n')] |
"""PII postprocessor."""
import json
from copy import deepcopy
from typing import Callable, Dict, List, Optional, Tuple
from llama_index.core.llms.llm import LLM
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle
DEFAULT_PII_TMPL = (
"The current context information is provided. \n"
"A task is also provided to mask the PII within the context. \n"
"Return the text, with all PII masked out, and a mapping of the original PII "
"to the masked PII. \n"
"Return the output of the task in JSON. \n"
"Context:\n"
"Hello Zhang Wei, I am John. "
"Your AnyCompany Financial Services, "
"LLC credit card account 1111-0000-1111-0008 "
"has a minimum payment of $24.53 that is due "
"by July 31st. Based on your autopay settings, we will withdraw your payment. "
"Task: Mask out the PII, replace each PII with a tag, and return the text. Return the mapping in JSON. \n"
"Output: \n"
"Hello [NAME1], I am [NAME2]. "
"Your AnyCompany Financial Services, "
"LLC credit card account [CREDIT_CARD_NUMBER] "
"has a minimum payment of $24.53 that is due "
"by [DATE_TIME]. Based on your autopay settings, we will withdraw your payment. "
"Output Mapping:\n"
'{{"NAME1": "Zhang Wei", "NAME2": "John", "CREDIT_CARD_NUMBER": "1111-0000-1111-0008", "DATE_TIME": "July 31st"}}\n'
"Context:\n{context_str}\n"
"Task: {query_str}\n"
"Output: \n"
""
)
class PIINodePostprocessor(BaseNodePostprocessor):
"""PII Node processor.
NOTE: this is a beta feature, the API might change.
Args:
llm (LLM): The local LLM to use for prediction.
"""
llm: LLM
pii_str_tmpl: str = DEFAULT_PII_TMPL
pii_node_info_key: str = "__pii_node_info__"
@classmethod
def class_name(cls) -> str:
return "PIINodePostprocessor"
def mask_pii(self, text: str) -> Tuple[str, Dict]:
"""Mask PII in text."""
pii_prompt = PromptTemplate(self.pii_str_tmpl)
# TODO: allow customization
task_str = (
"Mask out the PII, replace each PII with a tag, and return the text. "
"Return the mapping in JSON."
)
response = self.llm.predict(pii_prompt, context_str=text, query_str=task_str)
splits = response.split("Output Mapping:")
text_output = splits[0].strip()
json_str_output = splits[1].strip()
json_dict = json.loads(json_str_output)
return text_output, json_dict
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
"""Postprocess nodes."""
# swap out text from nodes, with the original node mappings
new_nodes = []
for node_with_score in nodes:
node = node_with_score.node
new_text, mapping_info = self.mask_pii(
node.get_content(metadata_mode=MetadataMode.LLM)
)
new_node = deepcopy(node)
new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key)
new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key)
new_node.metadata[self.pii_node_info_key] = mapping_info
new_node.set_content(new_text)
new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score))
return new_nodes
class NERPIINodePostprocessor(BaseNodePostprocessor):
"""NER PII Node processor.
Uses a HF transformers model.
"""
pii_node_info_key: str = "__pii_node_info__"
@classmethod
def class_name(cls) -> str:
return "NERPIINodePostprocessor"
def mask_pii(self, ner: Callable, text: str) -> Tuple[str, Dict]:
"""Mask PII in text."""
new_text = text
response = ner(text)
mapping = {}
for entry in response:
entity_group_tag = f"[{entry['entity_group']}_{entry['start']}]"
new_text = new_text.replace(entry["word"], entity_group_tag).strip()
mapping[entity_group_tag] = entry["word"]
return new_text, mapping
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
"""Postprocess nodes."""
from transformers import pipeline # pants: no-infer-dep
ner = pipeline("ner", grouped_entities=True)
# swap out text from nodes, with the original node mappings
new_nodes = []
for node_with_score in nodes:
node = node_with_score.node
new_text, mapping_info = self.mask_pii(
ner, node.get_content(metadata_mode=MetadataMode.LLM)
)
new_node = deepcopy(node)
new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key)
new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key)
new_node.metadata[self.pii_node_info_key] = mapping_info
new_node.set_content(new_text)
new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score))
return new_nodes
| [
"llama_index.core.prompts.base.PromptTemplate",
"llama_index.core.schema.NodeWithScore"
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"""PII postprocessor."""
import json
from copy import deepcopy
from typing import Callable, Dict, List, Optional, Tuple
from llama_index.core.llms.llm import LLM
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle
DEFAULT_PII_TMPL = (
"The current context information is provided. \n"
"A task is also provided to mask the PII within the context. \n"
"Return the text, with all PII masked out, and a mapping of the original PII "
"to the masked PII. \n"
"Return the output of the task in JSON. \n"
"Context:\n"
"Hello Zhang Wei, I am John. "
"Your AnyCompany Financial Services, "
"LLC credit card account 1111-0000-1111-0008 "
"has a minimum payment of $24.53 that is due "
"by July 31st. Based on your autopay settings, we will withdraw your payment. "
"Task: Mask out the PII, replace each PII with a tag, and return the text. Return the mapping in JSON. \n"
"Output: \n"
"Hello [NAME1], I am [NAME2]. "
"Your AnyCompany Financial Services, "
"LLC credit card account [CREDIT_CARD_NUMBER] "
"has a minimum payment of $24.53 that is due "
"by [DATE_TIME]. Based on your autopay settings, we will withdraw your payment. "
"Output Mapping:\n"
'{{"NAME1": "Zhang Wei", "NAME2": "John", "CREDIT_CARD_NUMBER": "1111-0000-1111-0008", "DATE_TIME": "July 31st"}}\n'
"Context:\n{context_str}\n"
"Task: {query_str}\n"
"Output: \n"
""
)
class PIINodePostprocessor(BaseNodePostprocessor):
"""PII Node processor.
NOTE: this is a beta feature, the API might change.
Args:
llm (LLM): The local LLM to use for prediction.
"""
llm: LLM
pii_str_tmpl: str = DEFAULT_PII_TMPL
pii_node_info_key: str = "__pii_node_info__"
@classmethod
def class_name(cls) -> str:
return "PIINodePostprocessor"
def mask_pii(self, text: str) -> Tuple[str, Dict]:
"""Mask PII in text."""
pii_prompt = PromptTemplate(self.pii_str_tmpl)
# TODO: allow customization
task_str = (
"Mask out the PII, replace each PII with a tag, and return the text. "
"Return the mapping in JSON."
)
response = self.llm.predict(pii_prompt, context_str=text, query_str=task_str)
splits = response.split("Output Mapping:")
text_output = splits[0].strip()
json_str_output = splits[1].strip()
json_dict = json.loads(json_str_output)
return text_output, json_dict
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
"""Postprocess nodes."""
# swap out text from nodes, with the original node mappings
new_nodes = []
for node_with_score in nodes:
node = node_with_score.node
new_text, mapping_info = self.mask_pii(
node.get_content(metadata_mode=MetadataMode.LLM)
)
new_node = deepcopy(node)
new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key)
new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key)
new_node.metadata[self.pii_node_info_key] = mapping_info
new_node.set_content(new_text)
new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score))
return new_nodes
class NERPIINodePostprocessor(BaseNodePostprocessor):
"""NER PII Node processor.
Uses a HF transformers model.
"""
pii_node_info_key: str = "__pii_node_info__"
@classmethod
def class_name(cls) -> str:
return "NERPIINodePostprocessor"
def mask_pii(self, ner: Callable, text: str) -> Tuple[str, Dict]:
"""Mask PII in text."""
new_text = text
response = ner(text)
mapping = {}
for entry in response:
entity_group_tag = f"[{entry['entity_group']}_{entry['start']}]"
new_text = new_text.replace(entry["word"], entity_group_tag).strip()
mapping[entity_group_tag] = entry["word"]
return new_text, mapping
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
"""Postprocess nodes."""
from transformers import pipeline # pants: no-infer-dep
ner = pipeline("ner", grouped_entities=True)
# swap out text from nodes, with the original node mappings
new_nodes = []
for node_with_score in nodes:
node = node_with_score.node
new_text, mapping_info = self.mask_pii(
ner, node.get_content(metadata_mode=MetadataMode.LLM)
)
new_node = deepcopy(node)
new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key)
new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key)
new_node.metadata[self.pii_node_info_key] = mapping_info
new_node.set_content(new_text)
new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score))
return new_nodes
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"llama_index.core.prompts.base.PromptTemplate",
"llama_index.core.schema.NodeWithScore"
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from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS, DEFAULT_TEMPERATURE
from llama_index.legacy.core.llms.types import ChatMessage, LLMMetadata
from llama_index.legacy.llms.everlyai_utils import everlyai_modelname_to_contextsize
from llama_index.legacy.llms.generic_utils import get_from_param_or_env
from llama_index.legacy.llms.openai import OpenAI
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
EVERLYAI_API_BASE = "https://everlyai.xyz/hosted"
DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
class EverlyAI(OpenAI):
def __init__(
self,
model: str = DEFAULT_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
api_key = get_from_param_or_env("api_key", api_key, "EverlyAI_API_KEY")
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
api_base=EVERLYAI_API_BASE,
api_key=api_key,
additional_kwargs=additional_kwargs,
max_retries=max_retries,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "EverlyAI_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=everlyai_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
)
@property
def _is_chat_model(self) -> bool:
return True
| [
"llama_index.legacy.callbacks.CallbackManager",
"llama_index.legacy.llms.generic_utils.get_from_param_or_env",
"llama_index.legacy.llms.everlyai_utils.everlyai_modelname_to_contextsize"
] | [((1525, 1586), 'llama_index.legacy.llms.generic_utils.get_from_param_or_env', 'get_from_param_or_env', (['"""api_key"""', 'api_key', '"""EverlyAI_API_KEY"""'], {}), "('api_key', api_key, 'EverlyAI_API_KEY')\n", (1546, 1586), False, 'from llama_index.legacy.llms.generic_utils import get_from_param_or_env\n'), ((1486, 1505), 'llama_index.legacy.callbacks.CallbackManager', 'CallbackManager', (['[]'], {}), '([])\n', (1501, 1505), False, 'from llama_index.legacy.callbacks import CallbackManager\n'), ((2357, 2402), 'llama_index.legacy.llms.everlyai_utils.everlyai_modelname_to_contextsize', 'everlyai_modelname_to_contextsize', (['self.model'], {}), '(self.model)\n', (2390, 2402), False, 'from llama_index.legacy.llms.everlyai_utils import everlyai_modelname_to_contextsize\n')] |
"""txtai reader."""
from typing import Any, Dict, List
import numpy as np
from llama_index.legacy.readers.base import BaseReader
from llama_index.legacy.schema import Document
class TxtaiReader(BaseReader):
"""txtai reader.
Retrieves documents through an existing in-memory txtai index.
These documents can then be used in a downstream LlamaIndex data structure.
If you wish use txtai itself as an index to to organize documents,
insert documents, and perform queries on them, please use VectorStoreIndex
with TxtaiVectorStore.
Args:
txtai_index (txtai.ann.ANN): A txtai Index object (required)
"""
def __init__(self, index: Any):
"""Initialize with parameters."""
import_err_msg = """
`txtai` package not found. For instructions on
how to install `txtai` please visit
https://neuml.github.io/txtai/install/
"""
try:
import txtai # noqa
except ImportError:
raise ImportError(import_err_msg)
self._index = index
def load_data(
self,
query: np.ndarray,
id_to_text_map: Dict[str, str],
k: int = 4,
separate_documents: bool = True,
) -> List[Document]:
"""Load data from txtai index.
Args:
query (np.ndarray): A 2D numpy array of query vectors.
id_to_text_map (Dict[str, str]): A map from ID's to text.
k (int): Number of nearest neighbors to retrieve. Defaults to 4.
separate_documents (Optional[bool]): Whether to return separate
documents. Defaults to True.
Returns:
List[Document]: A list of documents.
"""
search_result = self._index.search(query, k)
documents = []
for query_result in search_result:
for doc_id, _ in query_result:
doc_id = str(doc_id)
if doc_id not in id_to_text_map:
raise ValueError(
f"Document ID {doc_id} not found in id_to_text_map."
)
text = id_to_text_map[doc_id]
documents.append(Document(text=text))
if not separate_documents:
# join all documents into one
text_list = [doc.get_content() for doc in documents]
text = "\n\n".join(text_list)
documents = [Document(text=text)]
return documents
| [
"llama_index.legacy.schema.Document"
] | [((2425, 2444), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (2433, 2444), False, 'from llama_index.legacy.schema import Document\n'), ((2194, 2213), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (2202, 2213), False, 'from llama_index.legacy.schema import Document\n')] |
"""txtai reader."""
from typing import Any, Dict, List
import numpy as np
from llama_index.legacy.readers.base import BaseReader
from llama_index.legacy.schema import Document
class TxtaiReader(BaseReader):
"""txtai reader.
Retrieves documents through an existing in-memory txtai index.
These documents can then be used in a downstream LlamaIndex data structure.
If you wish use txtai itself as an index to to organize documents,
insert documents, and perform queries on them, please use VectorStoreIndex
with TxtaiVectorStore.
Args:
txtai_index (txtai.ann.ANN): A txtai Index object (required)
"""
def __init__(self, index: Any):
"""Initialize with parameters."""
import_err_msg = """
`txtai` package not found. For instructions on
how to install `txtai` please visit
https://neuml.github.io/txtai/install/
"""
try:
import txtai # noqa
except ImportError:
raise ImportError(import_err_msg)
self._index = index
def load_data(
self,
query: np.ndarray,
id_to_text_map: Dict[str, str],
k: int = 4,
separate_documents: bool = True,
) -> List[Document]:
"""Load data from txtai index.
Args:
query (np.ndarray): A 2D numpy array of query vectors.
id_to_text_map (Dict[str, str]): A map from ID's to text.
k (int): Number of nearest neighbors to retrieve. Defaults to 4.
separate_documents (Optional[bool]): Whether to return separate
documents. Defaults to True.
Returns:
List[Document]: A list of documents.
"""
search_result = self._index.search(query, k)
documents = []
for query_result in search_result:
for doc_id, _ in query_result:
doc_id = str(doc_id)
if doc_id not in id_to_text_map:
raise ValueError(
f"Document ID {doc_id} not found in id_to_text_map."
)
text = id_to_text_map[doc_id]
documents.append(Document(text=text))
if not separate_documents:
# join all documents into one
text_list = [doc.get_content() for doc in documents]
text = "\n\n".join(text_list)
documents = [Document(text=text)]
return documents
| [
"llama_index.legacy.schema.Document"
] | [((2425, 2444), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (2433, 2444), False, 'from llama_index.legacy.schema import Document\n'), ((2194, 2213), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (2202, 2213), False, 'from llama_index.legacy.schema import Document\n')] |
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
"""Single select prompt.
PromptTemplate to select one out of `num_choices` options provided in `context_list`,
given a query `query_str`.
Required template variables: `num_chunks`, `context_list`, `query_str`
"""
SingleSelectPrompt = PromptTemplate
"""Multiple select prompt.
PromptTemplate to select multiple candidates (up to `max_outputs`) out of `num_choices`
options provided in `context_list`, given a query `query_str`.
Required template variables: `num_chunks`, `context_list`, `query_str`,
`max_outputs`
"""
MultiSelectPrompt = PromptTemplate
# single select
DEFAULT_SINGLE_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered list "
"(1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return "
"the choice that is most relevant to the question: '{query_str}'\n"
)
DEFAULT_SINGLE_SELECT_PROMPT = PromptTemplate(
template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=PromptType.SINGLE_SELECT
)
# multiple select
DEFAULT_MULTI_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered "
"list (1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return the top choices "
"(no more than {max_outputs}, but only select what is needed) that "
"are most relevant to the question: '{query_str}'\n"
)
DEFAULT_MULTIPLE_SELECT_PROMPT = PromptTemplate(
template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=PromptType.MULTI_SELECT
)
# single pydantic select
DEFAULT_SINGLE_PYD_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered list "
"(1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, generate "
"the selection object and reason that is most relevant to the "
"question: '{query_str}'\n"
)
# multiple pydantic select
DEFAULT_MULTI_PYD_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered "
"list (1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return the top choice(s) "
"(no more than {max_outputs}, but only select what is needed) by generating "
"the selection object and reasons that are most relevant to the "
"question: '{query_str}'\n"
)
| [
"llama_index.core.prompts.base.PromptTemplate"
] | [((1156, 1257), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_SINGLE_SELECT_PROMPT_TMPL', 'prompt_type': 'PromptType.SINGLE_SELECT'}), '(template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=\n PromptType.SINGLE_SELECT)\n', (1170, 1257), False, 'from llama_index.core.prompts.base import PromptTemplate\n'), ((1812, 1911), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_MULTI_SELECT_PROMPT_TMPL', 'prompt_type': 'PromptType.MULTI_SELECT'}), '(template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=\n PromptType.MULTI_SELECT)\n', (1826, 1911), False, 'from llama_index.core.prompts.base import PromptTemplate\n')] |
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
"""Single select prompt.
PromptTemplate to select one out of `num_choices` options provided in `context_list`,
given a query `query_str`.
Required template variables: `num_chunks`, `context_list`, `query_str`
"""
SingleSelectPrompt = PromptTemplate
"""Multiple select prompt.
PromptTemplate to select multiple candidates (up to `max_outputs`) out of `num_choices`
options provided in `context_list`, given a query `query_str`.
Required template variables: `num_chunks`, `context_list`, `query_str`,
`max_outputs`
"""
MultiSelectPrompt = PromptTemplate
# single select
DEFAULT_SINGLE_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered list "
"(1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return "
"the choice that is most relevant to the question: '{query_str}'\n"
)
DEFAULT_SINGLE_SELECT_PROMPT = PromptTemplate(
template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=PromptType.SINGLE_SELECT
)
# multiple select
DEFAULT_MULTI_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered "
"list (1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return the top choices "
"(no more than {max_outputs}, but only select what is needed) that "
"are most relevant to the question: '{query_str}'\n"
)
DEFAULT_MULTIPLE_SELECT_PROMPT = PromptTemplate(
template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=PromptType.MULTI_SELECT
)
# single pydantic select
DEFAULT_SINGLE_PYD_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered list "
"(1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, generate "
"the selection object and reason that is most relevant to the "
"question: '{query_str}'\n"
)
# multiple pydantic select
DEFAULT_MULTI_PYD_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered "
"list (1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return the top choice(s) "
"(no more than {max_outputs}, but only select what is needed) by generating "
"the selection object and reasons that are most relevant to the "
"question: '{query_str}'\n"
)
| [
"llama_index.core.prompts.base.PromptTemplate"
] | [((1156, 1257), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_SINGLE_SELECT_PROMPT_TMPL', 'prompt_type': 'PromptType.SINGLE_SELECT'}), '(template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=\n PromptType.SINGLE_SELECT)\n', (1170, 1257), False, 'from llama_index.core.prompts.base import PromptTemplate\n'), ((1812, 1911), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_MULTI_SELECT_PROMPT_TMPL', 'prompt_type': 'PromptType.MULTI_SELECT'}), '(template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=\n PromptType.MULTI_SELECT)\n', (1826, 1911), False, 'from llama_index.core.prompts.base import PromptTemplate\n')] |
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
"""Single select prompt.
PromptTemplate to select one out of `num_choices` options provided in `context_list`,
given a query `query_str`.
Required template variables: `num_chunks`, `context_list`, `query_str`
"""
SingleSelectPrompt = PromptTemplate
"""Multiple select prompt.
PromptTemplate to select multiple candidates (up to `max_outputs`) out of `num_choices`
options provided in `context_list`, given a query `query_str`.
Required template variables: `num_chunks`, `context_list`, `query_str`,
`max_outputs`
"""
MultiSelectPrompt = PromptTemplate
# single select
DEFAULT_SINGLE_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered list "
"(1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return "
"the choice that is most relevant to the question: '{query_str}'\n"
)
DEFAULT_SINGLE_SELECT_PROMPT = PromptTemplate(
template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=PromptType.SINGLE_SELECT
)
# multiple select
DEFAULT_MULTI_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered "
"list (1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return the top choices "
"(no more than {max_outputs}, but only select what is needed) that "
"are most relevant to the question: '{query_str}'\n"
)
DEFAULT_MULTIPLE_SELECT_PROMPT = PromptTemplate(
template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=PromptType.MULTI_SELECT
)
# single pydantic select
DEFAULT_SINGLE_PYD_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered list "
"(1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, generate "
"the selection object and reason that is most relevant to the "
"question: '{query_str}'\n"
)
# multiple pydantic select
DEFAULT_MULTI_PYD_SELECT_PROMPT_TMPL = (
"Some choices are given below. It is provided in a numbered "
"list (1 to {num_choices}), "
"where each item in the list corresponds to a summary.\n"
"---------------------\n"
"{context_list}"
"\n---------------------\n"
"Using only the choices above and not prior knowledge, return the top choice(s) "
"(no more than {max_outputs}, but only select what is needed) by generating "
"the selection object and reasons that are most relevant to the "
"question: '{query_str}'\n"
)
| [
"llama_index.core.prompts.base.PromptTemplate"
] | [((1156, 1257), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_SINGLE_SELECT_PROMPT_TMPL', 'prompt_type': 'PromptType.SINGLE_SELECT'}), '(template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=\n PromptType.SINGLE_SELECT)\n', (1170, 1257), False, 'from llama_index.core.prompts.base import PromptTemplate\n'), ((1812, 1911), 'llama_index.core.prompts.base.PromptTemplate', 'PromptTemplate', ([], {'template': 'DEFAULT_MULTI_SELECT_PROMPT_TMPL', 'prompt_type': 'PromptType.MULTI_SELECT'}), '(template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=\n PromptType.MULTI_SELECT)\n', (1826, 1911), False, 'from llama_index.core.prompts.base import PromptTemplate\n')] |
"""Awadb reader."""
from typing import Any, List
import numpy as np
from llama_index.legacy.readers.base import BaseReader
from llama_index.legacy.schema import Document
class AwadbReader(BaseReader):
"""Awadb reader.
Retrieves documents through an existing awadb client.
These documents can then be used in a downstream LlamaIndex data structure.
Args:
client (awadb.client): An awadb client.
"""
def __init__(self, client: Any):
"""Initialize with parameters."""
import_err_msg = """
`faiss` package not found. For instructions on
how to install `faiss` please visit
https://github.com/facebookresearch/faiss/wiki/Installing-Faiss
"""
try:
pass
except ImportError:
raise ImportError(import_err_msg)
self.awadb_client = client
def load_data(
self,
query: np.ndarray,
k: int = 4,
separate_documents: bool = True,
) -> List[Document]:
"""Load data from Faiss.
Args:
query (np.ndarray): A 2D numpy array of query vectors.
k (int): Number of nearest neighbors to retrieve. Defaults to 4.
separate_documents (Optional[bool]): Whether to return separate
documents. Defaults to True.
Returns:
List[Document]: A list of documents.
"""
results = self.awadb_client.Search(
query,
k,
text_in_page_content=None,
meta_filter=None,
not_include_fields=None,
)
documents = []
for item_detail in results[0]["ResultItems"]:
documents.append(Document(text=item_detail["embedding_text"]))
if not separate_documents:
# join all documents into one
text_list = [doc.get_content() for doc in documents]
text = "\n\n".join(text_list)
documents = [Document(text=text)]
return documents
| [
"llama_index.legacy.schema.Document"
] | [((1780, 1824), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': "item_detail['embedding_text']"}), "(text=item_detail['embedding_text'])\n", (1788, 1824), False, 'from llama_index.legacy.schema import Document\n'), ((2042, 2061), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (2050, 2061), False, 'from llama_index.legacy.schema import Document\n')] |
"""Mongo client."""
from typing import Dict, Iterable, List, Optional, Union
from llama_index.legacy.readers.base import BaseReader
from llama_index.legacy.schema import Document
class SimpleMongoReader(BaseReader):
"""Simple mongo reader.
Concatenates each Mongo doc into Document used by LlamaIndex.
Args:
host (str): Mongo host.
port (int): Mongo port.
"""
def __init__(
self,
host: Optional[str] = None,
port: Optional[int] = None,
uri: Optional[str] = None,
) -> None:
"""Initialize with parameters."""
try:
from pymongo import MongoClient
except ImportError as err:
raise ImportError(
"`pymongo` package not found, please run `pip install pymongo`"
) from err
client: MongoClient
if uri:
client = MongoClient(uri)
elif host and port:
client = MongoClient(host, port)
else:
raise ValueError("Either `host` and `port` or `uri` must be provided.")
self.client = client
def _flatten(self, texts: List[Union[str, List[str]]]) -> List[str]:
result = []
for text in texts:
result += text if isinstance(text, list) else [text]
return result
def lazy_load_data(
self,
db_name: str,
collection_name: str,
field_names: List[str] = ["text"],
separator: str = "",
query_dict: Optional[Dict] = None,
max_docs: int = 0,
metadata_names: Optional[List[str]] = None,
) -> Iterable[Document]:
"""Load data from the input directory.
Args:
db_name (str): name of the database.
collection_name (str): name of the collection.
field_names(List[str]): names of the fields to be concatenated.
Defaults to ["text"]
separator (str): separator to be used between fields.
Defaults to ""
query_dict (Optional[Dict]): query to filter documents. Read more
at [official docs](https://www.mongodb.com/docs/manual/reference/method/db.collection.find/#std-label-method-find-query)
Defaults to None
max_docs (int): maximum number of documents to load.
Defaults to 0 (no limit)
metadata_names (Optional[List[str]]): names of the fields to be added
to the metadata attribute of the Document. Defaults to None
Returns:
List[Document]: A list of documents.
"""
db = self.client[db_name]
cursor = db[collection_name].find(filter=query_dict or {}, limit=max_docs)
for item in cursor:
try:
texts = [item[name] for name in field_names]
except KeyError as err:
raise ValueError(
f"{err.args[0]} field not found in Mongo document."
) from err
texts = self._flatten(texts)
text = separator.join(texts)
if metadata_names is None:
yield Document(text=text)
else:
try:
metadata = {name: item[name] for name in metadata_names}
except KeyError as err:
raise ValueError(
f"{err.args[0]} field not found in Mongo document."
) from err
yield Document(text=text, metadata=metadata)
| [
"llama_index.legacy.schema.Document"
] | [((887, 903), 'pymongo.MongoClient', 'MongoClient', (['uri'], {}), '(uri)\n', (898, 903), False, 'from pymongo import MongoClient\n'), ((953, 976), 'pymongo.MongoClient', 'MongoClient', (['host', 'port'], {}), '(host, port)\n', (964, 976), False, 'from pymongo import MongoClient\n'), ((3133, 3152), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (3141, 3152), False, 'from llama_index.legacy.schema import Document\n'), ((3476, 3514), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text', 'metadata': 'metadata'}), '(text=text, metadata=metadata)\n', (3484, 3514), False, 'from llama_index.legacy.schema import Document\n')] |
"""Mongo client."""
from typing import Dict, Iterable, List, Optional, Union
from llama_index.legacy.readers.base import BaseReader
from llama_index.legacy.schema import Document
class SimpleMongoReader(BaseReader):
"""Simple mongo reader.
Concatenates each Mongo doc into Document used by LlamaIndex.
Args:
host (str): Mongo host.
port (int): Mongo port.
"""
def __init__(
self,
host: Optional[str] = None,
port: Optional[int] = None,
uri: Optional[str] = None,
) -> None:
"""Initialize with parameters."""
try:
from pymongo import MongoClient
except ImportError as err:
raise ImportError(
"`pymongo` package not found, please run `pip install pymongo`"
) from err
client: MongoClient
if uri:
client = MongoClient(uri)
elif host and port:
client = MongoClient(host, port)
else:
raise ValueError("Either `host` and `port` or `uri` must be provided.")
self.client = client
def _flatten(self, texts: List[Union[str, List[str]]]) -> List[str]:
result = []
for text in texts:
result += text if isinstance(text, list) else [text]
return result
def lazy_load_data(
self,
db_name: str,
collection_name: str,
field_names: List[str] = ["text"],
separator: str = "",
query_dict: Optional[Dict] = None,
max_docs: int = 0,
metadata_names: Optional[List[str]] = None,
) -> Iterable[Document]:
"""Load data from the input directory.
Args:
db_name (str): name of the database.
collection_name (str): name of the collection.
field_names(List[str]): names of the fields to be concatenated.
Defaults to ["text"]
separator (str): separator to be used between fields.
Defaults to ""
query_dict (Optional[Dict]): query to filter documents. Read more
at [official docs](https://www.mongodb.com/docs/manual/reference/method/db.collection.find/#std-label-method-find-query)
Defaults to None
max_docs (int): maximum number of documents to load.
Defaults to 0 (no limit)
metadata_names (Optional[List[str]]): names of the fields to be added
to the metadata attribute of the Document. Defaults to None
Returns:
List[Document]: A list of documents.
"""
db = self.client[db_name]
cursor = db[collection_name].find(filter=query_dict or {}, limit=max_docs)
for item in cursor:
try:
texts = [item[name] for name in field_names]
except KeyError as err:
raise ValueError(
f"{err.args[0]} field not found in Mongo document."
) from err
texts = self._flatten(texts)
text = separator.join(texts)
if metadata_names is None:
yield Document(text=text)
else:
try:
metadata = {name: item[name] for name in metadata_names}
except KeyError as err:
raise ValueError(
f"{err.args[0]} field not found in Mongo document."
) from err
yield Document(text=text, metadata=metadata)
| [
"llama_index.legacy.schema.Document"
] | [((887, 903), 'pymongo.MongoClient', 'MongoClient', (['uri'], {}), '(uri)\n', (898, 903), False, 'from pymongo import MongoClient\n'), ((953, 976), 'pymongo.MongoClient', 'MongoClient', (['host', 'port'], {}), '(host, port)\n', (964, 976), False, 'from pymongo import MongoClient\n'), ((3133, 3152), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text'}), '(text=text)\n', (3141, 3152), False, 'from llama_index.legacy.schema import Document\n'), ((3476, 3514), 'llama_index.legacy.schema.Document', 'Document', ([], {'text': 'text', 'metadata': 'metadata'}), '(text=text, metadata=metadata)\n', (3484, 3514), False, 'from llama_index.legacy.schema import Document\n')] |
from typing import Any, Callable, Optional, Sequence
from typing_extensions import override
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class _BaseGradientLLM(CustomLLM):
_gradient = PrivateAttr()
_model = PrivateAttr()
# Config
max_tokens: Optional[int] = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The number of tokens to generate.",
gt=0,
lt=512,
)
# Gradient client config
access_token: Optional[str] = Field(
description="The Gradient access token to use.",
)
host: Optional[str] = Field(
description="The url of the Gradient service to access."
)
workspace_id: Optional[str] = Field(
description="The Gradient workspace id to use.",
)
is_chat_model: bool = Field(
default=False, description="Whether the model is a chat model."
)
def __init__(
self,
*,
access_token: Optional[str] = None,
host: Optional[str] = None,
max_tokens: Optional[int] = None,
workspace_id: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
is_chat_model: bool = False,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
super().__init__(
max_tokens=max_tokens,
access_token=access_token,
host=host,
workspace_id=workspace_id,
callback_manager=callback_manager,
is_chat_model=is_chat_model,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
**kwargs,
)
try:
from gradientai import Gradient
self._gradient = Gradient(
access_token=access_token, host=host, workspace_id=workspace_id
)
except ImportError as e:
raise ImportError(
"Could not import Gradient Python package. "
"Please install it with `pip install gradientai`."
) from e
def close(self) -> None:
self._gradient.close()
@llm_completion_callback()
@override
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return CompletionResponse(
text=self._model.complete(
query=prompt,
max_generated_token_count=self.max_tokens,
**kwargs,
).generated_output
)
@llm_completion_callback()
@override
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
grdt_reponse = await self._model.acomplete(
query=prompt,
max_generated_token_count=self.max_tokens,
**kwargs,
)
return CompletionResponse(text=grdt_reponse.generated_output)
@override
def stream_complete(
self,
prompt: str,
formatted: bool = False,
**kwargs: Any,
) -> CompletionResponseGen:
raise NotImplementedError
@property
@override
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=1024,
num_output=self.max_tokens or 20,
is_chat_model=self.is_chat_model,
is_function_calling_model=False,
model_name=self._model.id,
)
class GradientBaseModelLLM(_BaseGradientLLM):
base_model_slug: str = Field(
description="The slug of the base model to use.",
)
def __init__(
self,
*,
access_token: Optional[str] = None,
base_model_slug: str,
host: Optional[str] = None,
max_tokens: Optional[int] = None,
workspace_id: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
is_chat_model: bool = False,
) -> None:
super().__init__(
access_token=access_token,
base_model_slug=base_model_slug,
host=host,
max_tokens=max_tokens,
workspace_id=workspace_id,
callback_manager=callback_manager,
is_chat_model=is_chat_model,
)
self._model = self._gradient.get_base_model(
base_model_slug=base_model_slug,
)
class GradientModelAdapterLLM(_BaseGradientLLM):
model_adapter_id: str = Field(
description="The id of the model adapter to use.",
)
def __init__(
self,
*,
access_token: Optional[str] = None,
host: Optional[str] = None,
max_tokens: Optional[int] = None,
model_adapter_id: str,
workspace_id: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
is_chat_model: bool = False,
) -> None:
super().__init__(
access_token=access_token,
host=host,
max_tokens=max_tokens,
model_adapter_id=model_adapter_id,
workspace_id=workspace_id,
callback_manager=callback_manager,
is_chat_model=is_chat_model,
)
self._model = self._gradient.get_model_adapter(
model_adapter_id=model_adapter_id
)
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr"
] | [((660, 673), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (671, 673), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((687, 700), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (698, 700), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((747, 849), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': 'DEFAULT_NUM_OUTPUTS', 'description': '"""The number of tokens to generate."""', 'gt': '(0)', 'lt': '(512)'}), "(default=DEFAULT_NUM_OUTPUTS, description=\n 'The number of tokens to generate.', gt=0, lt=512)\n", (752, 849), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((948, 1002), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Gradient access token to use."""'}), "(description='The Gradient access token to use.')\n", (953, 1002), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1044, 1107), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The url of the Gradient service to access."""'}), "(description='The url of the Gradient service to access.')\n", (1049, 1107), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1156, 1210), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Gradient workspace id to use."""'}), "(description='The Gradient workspace id to use.')\n", (1161, 1210), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1252, 1322), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': '(False)', 'description': '"""Whether the model is a chat model."""'}), "(default=False, description='Whether the model is a chat model.')\n", (1257, 1322), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((3019, 3044), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (3042, 3044), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((3408, 3433), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (3431, 3433), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((4391, 4446), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The slug of the base model to use."""'}), "(description='The slug of the base model to use.')\n", (4396, 4446), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((5307, 5363), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The id of the model adapter to use."""'}), "(description='The id of the model adapter to use.')\n", (5312, 5363), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((3749, 3803), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'grdt_reponse.generated_output'}), '(text=grdt_reponse.generated_output)\n', (3767, 3803), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((4084, 4252), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'context_window': '(1024)', 'num_output': '(self.max_tokens or 20)', 'is_chat_model': 'self.is_chat_model', 'is_function_calling_model': '(False)', 'model_name': 'self._model.id'}), '(context_window=1024, num_output=self.max_tokens or 20,\n is_chat_model=self.is_chat_model, is_function_calling_model=False,\n model_name=self._model.id)\n', (4095, 4252), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2635, 2708), 'gradientai.Gradient', 'Gradient', ([], {'access_token': 'access_token', 'host': 'host', 'workspace_id': 'workspace_id'}), '(access_token=access_token, host=host, workspace_id=workspace_id)\n', (2643, 2708), False, 'from gradientai import Gradient\n')] |
from typing import Any, Callable, Optional, Sequence
from typing_extensions import override
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class _BaseGradientLLM(CustomLLM):
_gradient = PrivateAttr()
_model = PrivateAttr()
# Config
max_tokens: Optional[int] = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The number of tokens to generate.",
gt=0,
lt=512,
)
# Gradient client config
access_token: Optional[str] = Field(
description="The Gradient access token to use.",
)
host: Optional[str] = Field(
description="The url of the Gradient service to access."
)
workspace_id: Optional[str] = Field(
description="The Gradient workspace id to use.",
)
is_chat_model: bool = Field(
default=False, description="Whether the model is a chat model."
)
def __init__(
self,
*,
access_token: Optional[str] = None,
host: Optional[str] = None,
max_tokens: Optional[int] = None,
workspace_id: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
is_chat_model: bool = False,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
super().__init__(
max_tokens=max_tokens,
access_token=access_token,
host=host,
workspace_id=workspace_id,
callback_manager=callback_manager,
is_chat_model=is_chat_model,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
**kwargs,
)
try:
from gradientai import Gradient
self._gradient = Gradient(
access_token=access_token, host=host, workspace_id=workspace_id
)
except ImportError as e:
raise ImportError(
"Could not import Gradient Python package. "
"Please install it with `pip install gradientai`."
) from e
def close(self) -> None:
self._gradient.close()
@llm_completion_callback()
@override
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return CompletionResponse(
text=self._model.complete(
query=prompt,
max_generated_token_count=self.max_tokens,
**kwargs,
).generated_output
)
@llm_completion_callback()
@override
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
grdt_reponse = await self._model.acomplete(
query=prompt,
max_generated_token_count=self.max_tokens,
**kwargs,
)
return CompletionResponse(text=grdt_reponse.generated_output)
@override
def stream_complete(
self,
prompt: str,
formatted: bool = False,
**kwargs: Any,
) -> CompletionResponseGen:
raise NotImplementedError
@property
@override
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=1024,
num_output=self.max_tokens or 20,
is_chat_model=self.is_chat_model,
is_function_calling_model=False,
model_name=self._model.id,
)
class GradientBaseModelLLM(_BaseGradientLLM):
base_model_slug: str = Field(
description="The slug of the base model to use.",
)
def __init__(
self,
*,
access_token: Optional[str] = None,
base_model_slug: str,
host: Optional[str] = None,
max_tokens: Optional[int] = None,
workspace_id: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
is_chat_model: bool = False,
) -> None:
super().__init__(
access_token=access_token,
base_model_slug=base_model_slug,
host=host,
max_tokens=max_tokens,
workspace_id=workspace_id,
callback_manager=callback_manager,
is_chat_model=is_chat_model,
)
self._model = self._gradient.get_base_model(
base_model_slug=base_model_slug,
)
class GradientModelAdapterLLM(_BaseGradientLLM):
model_adapter_id: str = Field(
description="The id of the model adapter to use.",
)
def __init__(
self,
*,
access_token: Optional[str] = None,
host: Optional[str] = None,
max_tokens: Optional[int] = None,
model_adapter_id: str,
workspace_id: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
is_chat_model: bool = False,
) -> None:
super().__init__(
access_token=access_token,
host=host,
max_tokens=max_tokens,
model_adapter_id=model_adapter_id,
workspace_id=workspace_id,
callback_manager=callback_manager,
is_chat_model=is_chat_model,
)
self._model = self._gradient.get_model_adapter(
model_adapter_id=model_adapter_id
)
| [
"llama_index.legacy.core.llms.types.CompletionResponse",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr"
] | [((660, 673), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (671, 673), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((687, 700), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (698, 700), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((747, 849), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': 'DEFAULT_NUM_OUTPUTS', 'description': '"""The number of tokens to generate."""', 'gt': '(0)', 'lt': '(512)'}), "(default=DEFAULT_NUM_OUTPUTS, description=\n 'The number of tokens to generate.', gt=0, lt=512)\n", (752, 849), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((948, 1002), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Gradient access token to use."""'}), "(description='The Gradient access token to use.')\n", (953, 1002), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1044, 1107), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The url of the Gradient service to access."""'}), "(description='The url of the Gradient service to access.')\n", (1049, 1107), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1156, 1210), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The Gradient workspace id to use."""'}), "(description='The Gradient workspace id to use.')\n", (1161, 1210), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1252, 1322), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default': '(False)', 'description': '"""Whether the model is a chat model."""'}), "(default=False, description='Whether the model is a chat model.')\n", (1257, 1322), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((3019, 3044), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (3042, 3044), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((3408, 3433), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (3431, 3433), False, 'from llama_index.legacy.llms.base import llm_completion_callback\n'), ((4391, 4446), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The slug of the base model to use."""'}), "(description='The slug of the base model to use.')\n", (4396, 4446), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((5307, 5363), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The id of the model adapter to use."""'}), "(description='The id of the model adapter to use.')\n", (5312, 5363), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((3749, 3803), 'llama_index.legacy.core.llms.types.CompletionResponse', 'CompletionResponse', ([], {'text': 'grdt_reponse.generated_output'}), '(text=grdt_reponse.generated_output)\n', (3767, 3803), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((4084, 4252), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'context_window': '(1024)', 'num_output': '(self.max_tokens or 20)', 'is_chat_model': 'self.is_chat_model', 'is_function_calling_model': '(False)', 'model_name': 'self._model.id'}), '(context_window=1024, num_output=self.max_tokens or 20,\n is_chat_model=self.is_chat_model, is_function_calling_model=False,\n model_name=self._model.id)\n', (4095, 4252), False, 'from llama_index.legacy.core.llms.types import ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((2635, 2708), 'gradientai.Gradient', 'Gradient', ([], {'access_token': 'access_token', 'host': 'host', 'workspace_id': 'workspace_id'}), '(access_token=access_token, host=host, workspace_id=workspace_id)\n', (2643, 2708), False, 'from gradientai import Gradient\n')] |
from typing import Dict, Type
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
RECOGNIZED_EMBEDDINGS: Dict[str, Type[BaseEmbedding]] = {
MockEmbedding.class_name(): MockEmbedding,
}
# conditionals for llama-cloud support
try:
from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep
RECOGNIZED_EMBEDDINGS[OpenAIEmbedding.class_name()] = OpenAIEmbedding
except ImportError:
pass
try:
from llama_index.embeddings.azure_openai import (
AzureOpenAIEmbedding,
) # pants: no-infer-dep
RECOGNIZED_EMBEDDINGS[AzureOpenAIEmbedding.class_name()] = AzureOpenAIEmbedding
except ImportError:
pass
try:
from llama_index.embeddings.huggingface import (
HuggingFaceInferenceAPIEmbedding,
) # pants: no-infer-dep
RECOGNIZED_EMBEDDINGS[
HuggingFaceInferenceAPIEmbedding.class_name()
] = HuggingFaceInferenceAPIEmbedding
except ImportError:
pass
def load_embed_model(data: dict) -> BaseEmbedding:
"""Load Embedding by name."""
if isinstance(data, BaseEmbedding):
return data
name = data.get("class_name", None)
if name is None:
raise ValueError("Embedding loading requires a class_name")
if name not in RECOGNIZED_EMBEDDINGS:
raise ValueError(f"Invalid Embedding name: {name}")
return RECOGNIZED_EMBEDDINGS[name].from_dict(data)
| [
"llama_index.core.embeddings.mock_embed_model.MockEmbedding.class_name",
"llama_index.embeddings.huggingface.HuggingFaceInferenceAPIEmbedding.class_name",
"llama_index.embeddings.openai.OpenAIEmbedding.class_name",
"llama_index.embeddings.azure_openai.AzureOpenAIEmbedding.class_name"
] | [((229, 255), 'llama_index.core.embeddings.mock_embed_model.MockEmbedding.class_name', 'MockEmbedding.class_name', ([], {}), '()\n', (253, 255), False, 'from llama_index.core.embeddings.mock_embed_model import MockEmbedding\n'), ((431, 459), 'llama_index.embeddings.openai.OpenAIEmbedding.class_name', 'OpenAIEmbedding.class_name', ([], {}), '()\n', (457, 459), False, 'from llama_index.embeddings.openai import OpenAIEmbedding\n'), ((654, 687), 'llama_index.embeddings.azure_openai.AzureOpenAIEmbedding.class_name', 'AzureOpenAIEmbedding.class_name', ([], {}), '()\n', (685, 687), False, 'from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding\n'), ((907, 952), 'llama_index.embeddings.huggingface.HuggingFaceInferenceAPIEmbedding.class_name', 'HuggingFaceInferenceAPIEmbedding.class_name', ([], {}), '()\n', (950, 952), False, 'from llama_index.embeddings.huggingface import HuggingFaceInferenceAPIEmbedding\n')] |
from typing import Dict, Type
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
RECOGNIZED_EMBEDDINGS: Dict[str, Type[BaseEmbedding]] = {
MockEmbedding.class_name(): MockEmbedding,
}
# conditionals for llama-cloud support
try:
from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep
RECOGNIZED_EMBEDDINGS[OpenAIEmbedding.class_name()] = OpenAIEmbedding
except ImportError:
pass
try:
from llama_index.embeddings.azure_openai import (
AzureOpenAIEmbedding,
) # pants: no-infer-dep
RECOGNIZED_EMBEDDINGS[AzureOpenAIEmbedding.class_name()] = AzureOpenAIEmbedding
except ImportError:
pass
try:
from llama_index.embeddings.huggingface import (
HuggingFaceInferenceAPIEmbedding,
) # pants: no-infer-dep
RECOGNIZED_EMBEDDINGS[
HuggingFaceInferenceAPIEmbedding.class_name()
] = HuggingFaceInferenceAPIEmbedding
except ImportError:
pass
def load_embed_model(data: dict) -> BaseEmbedding:
"""Load Embedding by name."""
if isinstance(data, BaseEmbedding):
return data
name = data.get("class_name", None)
if name is None:
raise ValueError("Embedding loading requires a class_name")
if name not in RECOGNIZED_EMBEDDINGS:
raise ValueError(f"Invalid Embedding name: {name}")
return RECOGNIZED_EMBEDDINGS[name].from_dict(data)
| [
"llama_index.core.embeddings.mock_embed_model.MockEmbedding.class_name",
"llama_index.embeddings.huggingface.HuggingFaceInferenceAPIEmbedding.class_name",
"llama_index.embeddings.openai.OpenAIEmbedding.class_name",
"llama_index.embeddings.azure_openai.AzureOpenAIEmbedding.class_name"
] | [((229, 255), 'llama_index.core.embeddings.mock_embed_model.MockEmbedding.class_name', 'MockEmbedding.class_name', ([], {}), '()\n', (253, 255), False, 'from llama_index.core.embeddings.mock_embed_model import MockEmbedding\n'), ((431, 459), 'llama_index.embeddings.openai.OpenAIEmbedding.class_name', 'OpenAIEmbedding.class_name', ([], {}), '()\n', (457, 459), False, 'from llama_index.embeddings.openai import OpenAIEmbedding\n'), ((654, 687), 'llama_index.embeddings.azure_openai.AzureOpenAIEmbedding.class_name', 'AzureOpenAIEmbedding.class_name', ([], {}), '()\n', (685, 687), False, 'from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding\n'), ((907, 952), 'llama_index.embeddings.huggingface.HuggingFaceInferenceAPIEmbedding.class_name', 'HuggingFaceInferenceAPIEmbedding.class_name', ([], {}), '()\n', (950, 952), False, 'from llama_index.embeddings.huggingface import HuggingFaceInferenceAPIEmbedding\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core.evaluation import CorrectnessEvaluator
from llama_index.llms import OpenAI, Gemini
from llama_index.core import ServiceContext
import pandas as pd
async def main():
# DOWNLOAD LLAMADATASET
evaluator_dataset, _ = download_llama_dataset(
"MiniMtBenchSingleGradingDataset", "./mini_mt_bench_data"
)
# DEFINE EVALUATORS
gpt_4_context = ServiceContext.from_defaults(
llm=OpenAI(temperature=0, model="gpt-4"),
)
gpt_3p5_context = ServiceContext.from_defaults(
llm=OpenAI(temperature=0, model="gpt-3.5-turbo"),
)
gemini_pro_context = ServiceContext.from_defaults(
llm=Gemini(model="models/gemini-pro", temperature=0)
)
evaluators = {
"gpt-4": CorrectnessEvaluator(service_context=gpt_4_context),
"gpt-3.5": CorrectnessEvaluator(service_context=gpt_3p5_context),
"gemini-pro": CorrectnessEvaluator(service_context=gemini_pro_context),
}
# EVALUATE WITH PACK
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
EvaluatorBenchmarkerPack = download_llama_pack("EvaluatorBenchmarkerPack", "./pack")
evaluator_benchmarker = EvaluatorBenchmarkerPack(
evaluator=evaluators["gpt-3.5"],
eval_dataset=evaluator_dataset,
show_progress=True,
)
gpt_3p5_benchmark_df = await evaluator_benchmarker.arun(
batch_size=100, sleep_time_in_seconds=0
)
evaluator_benchmarker = EvaluatorBenchmarkerPack(
evaluator=evaluators["gpt-4"],
eval_dataset=evaluator_dataset,
show_progress=True,
)
gpt_4_benchmark_df = await evaluator_benchmarker.arun(
batch_size=100, sleep_time_in_seconds=0
)
evaluator_benchmarker = EvaluatorBenchmarkerPack(
evaluator=evaluators["gemini-pro"],
eval_dataset=evaluator_dataset,
show_progress=True,
)
gemini_pro_benchmark_df = await evaluator_benchmarker.arun(
batch_size=5, sleep_time_in_seconds=0.5
)
benchmark_df = pd.concat(
[
gpt_3p5_benchmark_df,
gpt_4_benchmark_df,
gemini_pro_benchmark_df,
],
axis=0,
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.llms.Gemini",
"llama_index.core.evaluation.CorrectnessEvaluator",
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.llms.OpenAI"
] | [((386, 471), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""MiniMtBenchSingleGradingDataset"""', '"""./mini_mt_bench_data"""'], {}), "('MiniMtBenchSingleGradingDataset',\n './mini_mt_bench_data')\n", (408, 471), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((1646, 1703), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""EvaluatorBenchmarkerPack"""', '"""./pack"""'], {}), "('EvaluatorBenchmarkerPack', './pack')\n", (1665, 1703), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((2580, 2670), 'pandas.concat', 'pd.concat', (['[gpt_3p5_benchmark_df, gpt_4_benchmark_df, gemini_pro_benchmark_df]'], {'axis': '(0)'}), '([gpt_3p5_benchmark_df, gpt_4_benchmark_df,\n gemini_pro_benchmark_df], axis=0)\n', (2589, 2670), True, 'import pandas as pd\n'), ((2801, 2825), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (2823, 2825), False, 'import asyncio\n'), ((890, 941), 'llama_index.core.evaluation.CorrectnessEvaluator', 'CorrectnessEvaluator', ([], {'service_context': 'gpt_4_context'}), '(service_context=gpt_4_context)\n', (910, 941), False, 'from llama_index.core.evaluation import CorrectnessEvaluator\n'), ((962, 1015), 'llama_index.core.evaluation.CorrectnessEvaluator', 'CorrectnessEvaluator', ([], {'service_context': 'gpt_3p5_context'}), '(service_context=gpt_3p5_context)\n', (982, 1015), False, 'from llama_index.core.evaluation import CorrectnessEvaluator\n'), ((1039, 1095), 'llama_index.core.evaluation.CorrectnessEvaluator', 'CorrectnessEvaluator', ([], {'service_context': 'gemini_pro_context'}), '(service_context=gemini_pro_context)\n', (1059, 1095), False, 'from llama_index.core.evaluation import CorrectnessEvaluator\n'), ((569, 605), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model': '"""gpt-4"""'}), "(temperature=0, model='gpt-4')\n", (575, 605), False, 'from llama_index.llms import OpenAI, Gemini\n'), ((678, 722), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'model': '"""gpt-3.5-turbo"""'}), "(temperature=0, model='gpt-3.5-turbo')\n", (684, 722), False, 'from llama_index.llms import OpenAI, Gemini\n'), ((798, 846), 'llama_index.llms.Gemini', 'Gemini', ([], {'model': '"""models/gemini-pro"""', 'temperature': '(0)'}), "(model='models/gemini-pro', temperature=0)\n", (804, 846), False, 'from llama_index.llms import OpenAI, Gemini\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset(
"PaulGrahamEssayDataset", "./paul_graham"
)
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 330), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""PaulGrahamEssayDataset"""', '"""./paul_graham"""'], {}), "('PaulGrahamEssayDataset', './paul_graham')\n", (287, 330), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((389, 441), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (420, 441), False, 'from llama_index.core import VectorStoreIndex\n'), ((534, 589), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack_stuff"""'], {}), "('RagEvaluatorPack', './pack_stuff')\n", (553, 589), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1440, 1464), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1462, 1464), False, 'import asyncio\n')] |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset(
"PaulGrahamEssayDataset", "./paul_graham"
)
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=20, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 330), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""PaulGrahamEssayDataset"""', '"""./paul_graham"""'], {}), "('PaulGrahamEssayDataset', './paul_graham')\n", (287, 330), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((389, 441), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', ([], {'documents': 'documents'}), '(documents=documents)\n', (420, 441), False, 'from llama_index.core import VectorStoreIndex\n'), ((534, 589), 'llama_index.core.llama_pack.download_llama_pack', 'download_llama_pack', (['"""RagEvaluatorPack"""', '"""./pack_stuff"""'], {}), "('RagEvaluatorPack', './pack_stuff')\n", (553, 589), False, 'from llama_index.core.llama_pack import download_llama_pack\n'), ((1440, 1464), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1462, 1464), False, 'import asyncio\n')] |
from typing import TYPE_CHECKING, Any, Optional
from llama_index.core.base.base_query_engine import BaseQueryEngine
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import (
LlamaIndexTool,
)
from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput
DEFAULT_NAME = "query_engine_tool"
DEFAULT_DESCRIPTION = """Useful for running a natural language query
against a knowledge base and get back a natural language response.
"""
class QueryEngineTool(AsyncBaseTool):
"""Query engine tool.
A tool making use of a query engine.
Args:
query_engine (BaseQueryEngine): A query engine.
metadata (ToolMetadata): The associated metadata of the query engine.
"""
def __init__(
self,
query_engine: BaseQueryEngine,
metadata: ToolMetadata,
resolve_input_errors: bool = True,
) -> None:
self._query_engine = query_engine
self._metadata = metadata
self._resolve_input_errors = resolve_input_errors
@classmethod
def from_defaults(
cls,
query_engine: BaseQueryEngine,
name: Optional[str] = None,
description: Optional[str] = None,
resolve_input_errors: bool = True,
) -> "QueryEngineTool":
name = name or DEFAULT_NAME
description = description or DEFAULT_DESCRIPTION
metadata = ToolMetadata(name=name, description=description)
return cls(
query_engine=query_engine,
metadata=metadata,
resolve_input_errors=resolve_input_errors,
)
@property
def query_engine(self) -> BaseQueryEngine:
return self._query_engine
@property
def metadata(self) -> ToolMetadata:
return self._metadata
def call(self, *args: Any, **kwargs: Any) -> ToolOutput:
if args is not None and len(args) > 0:
query_str = str(args[0])
elif kwargs is not None and "input" in kwargs:
# NOTE: this assumes our default function schema of `input`
query_str = kwargs["input"]
elif kwargs is not None and self._resolve_input_errors:
query_str = str(kwargs)
else:
raise ValueError(
"Cannot call query engine without specifying `input` parameter."
)
response = self._query_engine.query(query_str)
return ToolOutput(
content=str(response),
tool_name=self.metadata.name,
raw_input={"input": query_str},
raw_output=response,
)
async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput:
if args is not None and len(args) > 0:
query_str = str(args[0])
elif kwargs is not None and "input" in kwargs:
# NOTE: this assumes our default function schema of `input`
query_str = kwargs["input"]
elif kwargs is not None and self._resolve_input_errors:
query_str = str(kwargs)
else:
raise ValueError("Cannot call query engine without inputs")
response = await self._query_engine.aquery(query_str)
return ToolOutput(
content=str(response),
tool_name=self.metadata.name,
raw_input={"input": query_str},
raw_output=response,
)
def as_langchain_tool(self) -> "LlamaIndexTool":
from llama_index.core.langchain_helpers.agents.tools import (
IndexToolConfig,
LlamaIndexTool,
)
tool_config = IndexToolConfig(
query_engine=self.query_engine,
name=self.metadata.name,
description=self.metadata.description,
)
return LlamaIndexTool.from_tool_config(tool_config=tool_config)
| [
"llama_index.core.langchain_helpers.agents.tools.LlamaIndexTool.from_tool_config",
"llama_index.core.langchain_helpers.agents.tools.IndexToolConfig",
"llama_index.core.tools.types.ToolMetadata"
] | [((1402, 1450), 'llama_index.core.tools.types.ToolMetadata', 'ToolMetadata', ([], {'name': 'name', 'description': 'description'}), '(name=name, description=description)\n', (1414, 1450), False, 'from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput\n'), ((3560, 3675), 'llama_index.core.langchain_helpers.agents.tools.IndexToolConfig', 'IndexToolConfig', ([], {'query_engine': 'self.query_engine', 'name': 'self.metadata.name', 'description': 'self.metadata.description'}), '(query_engine=self.query_engine, name=self.metadata.name,\n description=self.metadata.description)\n', (3575, 3675), False, 'from llama_index.core.langchain_helpers.agents.tools import IndexToolConfig, LlamaIndexTool\n'), ((3734, 3790), 'llama_index.core.langchain_helpers.agents.tools.LlamaIndexTool.from_tool_config', 'LlamaIndexTool.from_tool_config', ([], {'tool_config': 'tool_config'}), '(tool_config=tool_config)\n', (3765, 3790), False, 'from llama_index.core.langchain_helpers.agents.tools import IndexToolConfig, LlamaIndexTool\n')] |
from typing import TYPE_CHECKING, Any, Optional
from llama_index.core.base.base_query_engine import BaseQueryEngine
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import (
LlamaIndexTool,
)
from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput
DEFAULT_NAME = "query_engine_tool"
DEFAULT_DESCRIPTION = """Useful for running a natural language query
against a knowledge base and get back a natural language response.
"""
class QueryEngineTool(AsyncBaseTool):
"""Query engine tool.
A tool making use of a query engine.
Args:
query_engine (BaseQueryEngine): A query engine.
metadata (ToolMetadata): The associated metadata of the query engine.
"""
def __init__(
self,
query_engine: BaseQueryEngine,
metadata: ToolMetadata,
resolve_input_errors: bool = True,
) -> None:
self._query_engine = query_engine
self._metadata = metadata
self._resolve_input_errors = resolve_input_errors
@classmethod
def from_defaults(
cls,
query_engine: BaseQueryEngine,
name: Optional[str] = None,
description: Optional[str] = None,
resolve_input_errors: bool = True,
) -> "QueryEngineTool":
name = name or DEFAULT_NAME
description = description or DEFAULT_DESCRIPTION
metadata = ToolMetadata(name=name, description=description)
return cls(
query_engine=query_engine,
metadata=metadata,
resolve_input_errors=resolve_input_errors,
)
@property
def query_engine(self) -> BaseQueryEngine:
return self._query_engine
@property
def metadata(self) -> ToolMetadata:
return self._metadata
def call(self, *args: Any, **kwargs: Any) -> ToolOutput:
if args is not None and len(args) > 0:
query_str = str(args[0])
elif kwargs is not None and "input" in kwargs:
# NOTE: this assumes our default function schema of `input`
query_str = kwargs["input"]
elif kwargs is not None and self._resolve_input_errors:
query_str = str(kwargs)
else:
raise ValueError(
"Cannot call query engine without specifying `input` parameter."
)
response = self._query_engine.query(query_str)
return ToolOutput(
content=str(response),
tool_name=self.metadata.name,
raw_input={"input": query_str},
raw_output=response,
)
async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput:
if args is not None and len(args) > 0:
query_str = str(args[0])
elif kwargs is not None and "input" in kwargs:
# NOTE: this assumes our default function schema of `input`
query_str = kwargs["input"]
elif kwargs is not None and self._resolve_input_errors:
query_str = str(kwargs)
else:
raise ValueError("Cannot call query engine without inputs")
response = await self._query_engine.aquery(query_str)
return ToolOutput(
content=str(response),
tool_name=self.metadata.name,
raw_input={"input": query_str},
raw_output=response,
)
def as_langchain_tool(self) -> "LlamaIndexTool":
from llama_index.core.langchain_helpers.agents.tools import (
IndexToolConfig,
LlamaIndexTool,
)
tool_config = IndexToolConfig(
query_engine=self.query_engine,
name=self.metadata.name,
description=self.metadata.description,
)
return LlamaIndexTool.from_tool_config(tool_config=tool_config)
| [
"llama_index.core.langchain_helpers.agents.tools.LlamaIndexTool.from_tool_config",
"llama_index.core.langchain_helpers.agents.tools.IndexToolConfig",
"llama_index.core.tools.types.ToolMetadata"
] | [((1402, 1450), 'llama_index.core.tools.types.ToolMetadata', 'ToolMetadata', ([], {'name': 'name', 'description': 'description'}), '(name=name, description=description)\n', (1414, 1450), False, 'from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput\n'), ((3560, 3675), 'llama_index.core.langchain_helpers.agents.tools.IndexToolConfig', 'IndexToolConfig', ([], {'query_engine': 'self.query_engine', 'name': 'self.metadata.name', 'description': 'self.metadata.description'}), '(query_engine=self.query_engine, name=self.metadata.name,\n description=self.metadata.description)\n', (3575, 3675), False, 'from llama_index.core.langchain_helpers.agents.tools import IndexToolConfig, LlamaIndexTool\n'), ((3734, 3790), 'llama_index.core.langchain_helpers.agents.tools.LlamaIndexTool.from_tool_config', 'LlamaIndexTool.from_tool_config', ([], {'tool_config': 'tool_config'}), '(tool_config=tool_config)\n', (3765, 3790), False, 'from llama_index.core.langchain_helpers.agents.tools import IndexToolConfig, LlamaIndexTool\n')] |
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.llms.generic_utils import chat_to_completion_decorator
from llama_index.legacy.llms.openai_utils import (
from_openai_message_dict,
to_openai_message_dicts,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class LlamaAPI(CustomLLM):
model: str = Field(description="The llama-api model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_tokens: int = Field(description="The maximum number of tokens to generate.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the llama-api API."
)
_client: Any = PrivateAttr()
def __init__(
self,
model: str = "llama-13b-chat",
temperature: float = 0.1,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
additional_kwargs: Optional[Dict[str, Any]] = None,
api_key: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from llamaapi import LlamaAPI as Client
except ImportError as e:
raise ImportError(
"llama_api not installed."
"Please install it with `pip install llamaapi`."
) from e
self._client = Client(api_key)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs or {},
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "llama_api_llm"
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
"max_length": self.max_tokens,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=4096,
num_output=DEFAULT_NUM_OUTPUTS,
is_chat_model=True,
is_function_calling_model=True,
model_name="llama-api",
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
message_dicts = to_openai_message_dicts(messages)
json_dict = {
"messages": message_dicts,
**self._model_kwargs,
**kwargs,
}
response = self._client.run(json_dict).json()
message_dict = response["choices"][0]["message"]
message = from_openai_message_dict(message_dict)
return ChatResponse(message=message, raw=response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError("stream_complete is not supported for LlamaAPI")
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError("stream_chat is not supported for LlamaAPI")
| [
"llama_index.legacy.llms.openai_utils.from_openai_message_dict",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.openai_utils.to_openai_message_dicts",
"llama_index.legacy.llms.generic_utils.chat_to_completion_decorator",
"llama_index.legacy.core.llms.types.ChatResponse"
] | [((868, 916), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The llama-api model to use."""'}), "(description='The llama-api model to use.')\n", (873, 916), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((942, 999), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The temperature to use for sampling."""'}), "(description='The temperature to use for sampling.')\n", (947, 999), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1022, 1084), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'description': '"""The maximum number of tokens to generate."""'}), "(description='The maximum number of tokens to generate.')\n", (1027, 1084), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1125, 1213), 'llama_index.legacy.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'dict', 'description': '"""Additional kwargs for the llama-api API."""'}), "(default_factory=dict, description=\n 'Additional kwargs for the llama-api API.')\n", (1130, 1213), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((1243, 1256), 'llama_index.legacy.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (1254, 1256), False, 'from llama_index.legacy.bridge.pydantic import Field, PrivateAttr\n'), ((3379, 3398), 'llama_index.legacy.llms.base.llm_chat_callback', 'llm_chat_callback', ([], {}), '()\n', (3396, 3398), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((3902, 3927), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (3925, 3927), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((4154, 4179), 'llama_index.legacy.llms.base.llm_completion_callback', 'llm_completion_callback', ([], {}), '()\n', (4177, 4179), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((4392, 4411), 'llama_index.legacy.llms.base.llm_chat_callback', 'llm_chat_callback', ([], {}), '()\n', (4409, 4411), False, 'from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback\n'), ((2205, 2220), 'llamaapi.LlamaAPI', 'Client', (['api_key'], {}), '(api_key)\n', (2211, 2220), True, 'from llamaapi import LlamaAPI as Client\n'), ((3161, 3305), 'llama_index.legacy.core.llms.types.LLMMetadata', 'LLMMetadata', ([], {'context_window': '(4096)', 'num_output': 'DEFAULT_NUM_OUTPUTS', 'is_chat_model': '(True)', 'is_function_calling_model': '(True)', 'model_name': '"""llama-api"""'}), "(context_window=4096, num_output=DEFAULT_NUM_OUTPUTS,\n is_chat_model=True, is_function_calling_model=True, model_name='llama-api')\n", (3172, 3305), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseGen, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((3507, 3540), 'llama_index.legacy.llms.openai_utils.to_openai_message_dicts', 'to_openai_message_dicts', (['messages'], {}), '(messages)\n', (3530, 3540), False, 'from llama_index.legacy.llms.openai_utils import from_openai_message_dict, to_openai_message_dicts\n'), ((3797, 3835), 'llama_index.legacy.llms.openai_utils.from_openai_message_dict', 'from_openai_message_dict', (['message_dict'], {}), '(message_dict)\n', (3821, 3835), False, 'from llama_index.legacy.llms.openai_utils import from_openai_message_dict, to_openai_message_dicts\n'), ((3852, 3895), 'llama_index.legacy.core.llms.types.ChatResponse', 'ChatResponse', ([], {'message': 'message', 'raw': 'response'}), '(message=message, raw=response)\n', (3864, 3895), False, 'from llama_index.legacy.core.llms.types import ChatMessage, ChatResponse, ChatResponseGen, CompletionResponse, CompletionResponseGen, LLMMetadata\n'), ((4063, 4102), 'llama_index.legacy.llms.generic_utils.chat_to_completion_decorator', 'chat_to_completion_decorator', (['self.chat'], {}), '(self.chat)\n', (4091, 4102), False, 'from llama_index.legacy.llms.generic_utils import chat_to_completion_decorator\n')] |
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback
from llama_index.legacy.llms.custom import CustomLLM
from llama_index.legacy.llms.generic_utils import chat_to_completion_decorator
from llama_index.legacy.llms.openai_utils import (
from_openai_message_dict,
to_openai_message_dicts,
)
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
class LlamaAPI(CustomLLM):
model: str = Field(description="The llama-api model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_tokens: int = Field(description="The maximum number of tokens to generate.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the llama-api API."
)
_client: Any = PrivateAttr()
def __init__(
self,
model: str = "llama-13b-chat",
temperature: float = 0.1,
max_tokens: int = DEFAULT_NUM_OUTPUTS,
additional_kwargs: Optional[Dict[str, Any]] = None,
api_key: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from llamaapi import LlamaAPI as Client
except ImportError as e:
raise ImportError(
"llama_api not installed."
"Please install it with `pip install llamaapi`."
) from e
self._client = Client(api_key)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs or {},
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "llama_api_llm"
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
"max_length": self.max_tokens,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=4096,
num_output=DEFAULT_NUM_OUTPUTS,
is_chat_model=True,
is_function_calling_model=True,
model_name="llama-api",
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
message_dicts = to_openai_message_dicts(messages)
json_dict = {
"messages": message_dicts,
**self._model_kwargs,
**kwargs,
}
response = self._client.run(json_dict).json()
message_dict = response["choices"][0]["message"]
message = from_openai_message_dict(message_dict)
return ChatResponse(message=message, raw=response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError("stream_complete is not supported for LlamaAPI")
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError("stream_chat is not supported for LlamaAPI")
| [
"llama_index.legacy.llms.openai_utils.from_openai_message_dict",
"llama_index.legacy.llms.base.llm_chat_callback",
"llama_index.legacy.bridge.pydantic.Field",
"llama_index.legacy.core.llms.types.LLMMetadata",
"llama_index.legacy.llms.base.llm_completion_callback",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.llms.openai_utils.to_openai_message_dicts",
"llama_index.legacy.llms.generic_utils.chat_to_completion_decorator",
"llama_index.legacy.core.llms.types.ChatResponse"
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"""Download tool from Llama Hub."""
from typing import Optional, Type
from llama_index.legacy.download.module import (
LLAMA_HUB_URL,
MODULE_TYPE,
download_llama_module,
track_download,
)
from llama_index.legacy.tools.tool_spec.base import BaseToolSpec
def download_tool(
tool_class: str,
llama_hub_url: str = LLAMA_HUB_URL,
refresh_cache: bool = False,
custom_path: Optional[str] = None,
) -> Type[BaseToolSpec]:
"""Download a single tool from Llama Hub.
Args:
tool_class: The name of the tool class you want to download,
such as `GmailToolSpec`.
refresh_cache: If true, the local cache will be skipped and the
loader will be fetched directly from the remote repo.
custom_path: Custom dirpath to download loader into.
Returns:
A Loader.
"""
tool_cls = download_llama_module(
tool_class,
llama_hub_url=llama_hub_url,
refresh_cache=refresh_cache,
custom_dir="tools",
custom_path=custom_path,
library_path="tools/library.json",
)
if not issubclass(tool_cls, BaseToolSpec):
raise ValueError(f"Tool class {tool_class} must be a subclass of BaseToolSpec.")
track_download(tool_class, MODULE_TYPE.TOOL)
return tool_cls
| [
"llama_index.legacy.download.module.download_llama_module",
"llama_index.legacy.download.module.track_download"
] | [((867, 1047), 'llama_index.legacy.download.module.download_llama_module', 'download_llama_module', (['tool_class'], {'llama_hub_url': 'llama_hub_url', 'refresh_cache': 'refresh_cache', 'custom_dir': '"""tools"""', 'custom_path': 'custom_path', 'library_path': '"""tools/library.json"""'}), "(tool_class, llama_hub_url=llama_hub_url,\n refresh_cache=refresh_cache, custom_dir='tools', custom_path=\n custom_path, library_path='tools/library.json')\n", (888, 1047), False, 'from llama_index.legacy.download.module import LLAMA_HUB_URL, MODULE_TYPE, download_llama_module, track_download\n'), ((1234, 1278), 'llama_index.legacy.download.module.track_download', 'track_download', (['tool_class', 'MODULE_TYPE.TOOL'], {}), '(tool_class, MODULE_TYPE.TOOL)\n', (1248, 1278), False, 'from llama_index.legacy.download.module import LLAMA_HUB_URL, MODULE_TYPE, download_llama_module, track_download\n')] |
"""Simple Engine."""
import json
import os
from typing import Any, Optional, Union
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.embeddings import BaseEmbedding
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
from llama_index.core.indices.base import BaseIndex
from llama_index.core.ingestion.pipeline import run_transformations
from llama_index.core.llms import LLM
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.response_synthesizers import (
BaseSynthesizer,
get_response_synthesizer,
)
from llama_index.core.retrievers import BaseRetriever
from llama_index.core.schema import (
BaseNode,
Document,
NodeWithScore,
QueryBundle,
QueryType,
TransformComponent,
)
from metagpt.rag.factories import (
get_index,
get_rag_embedding,
get_rag_llm,
get_rankers,
get_retriever,
)
from metagpt.rag.interface import NoEmbedding, RAGObject
from metagpt.rag.retrievers.base import ModifiableRAGRetriever, PersistableRAGRetriever
from metagpt.rag.retrievers.hybrid_retriever import SimpleHybridRetriever
from metagpt.rag.schema import (
BaseIndexConfig,
BaseRankerConfig,
BaseRetrieverConfig,
BM25RetrieverConfig,
ObjectNode,
)
from metagpt.utils.common import import_class
class SimpleEngine(RetrieverQueryEngine):
"""SimpleEngine is designed to be simple and straightforward.
It is a lightweight and easy-to-use search engine that integrates
document reading, embedding, indexing, retrieving, and ranking functionalities
into a single, straightforward workflow. It is designed to quickly set up a
search engine from a collection of documents.
"""
def __init__(
self,
retriever: BaseRetriever,
response_synthesizer: Optional[BaseSynthesizer] = None,
node_postprocessors: Optional[list[BaseNodePostprocessor]] = None,
callback_manager: Optional[CallbackManager] = None,
index: Optional[BaseIndex] = None,
) -> None:
super().__init__(
retriever=retriever,
response_synthesizer=response_synthesizer,
node_postprocessors=node_postprocessors,
callback_manager=callback_manager,
)
self.index = index
@classmethod
def from_docs(
cls,
input_dir: str = None,
input_files: list[str] = None,
transformations: Optional[list[TransformComponent]] = None,
embed_model: BaseEmbedding = None,
llm: LLM = None,
retriever_configs: list[BaseRetrieverConfig] = None,
ranker_configs: list[BaseRankerConfig] = None,
) -> "SimpleEngine":
"""From docs.
Must provide either `input_dir` or `input_files`.
Args:
input_dir: Path to the directory.
input_files: List of file paths to read (Optional; overrides input_dir, exclude).
transformations: Parse documents to nodes. Default [SentenceSplitter].
embed_model: Parse nodes to embedding. Must supported by llama index. Default OpenAIEmbedding.
llm: Must supported by llama index. Default OpenAI.
retriever_configs: Configuration for retrievers. If more than one config, will use SimpleHybridRetriever.
ranker_configs: Configuration for rankers.
"""
if not input_dir and not input_files:
raise ValueError("Must provide either `input_dir` or `input_files`.")
documents = SimpleDirectoryReader(input_dir=input_dir, input_files=input_files).load_data()
cls._fix_document_metadata(documents)
index = VectorStoreIndex.from_documents(
documents=documents,
transformations=transformations or [SentenceSplitter()],
embed_model=cls._resolve_embed_model(embed_model, retriever_configs),
)
return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
@classmethod
def from_objs(
cls,
objs: Optional[list[RAGObject]] = None,
transformations: Optional[list[TransformComponent]] = None,
embed_model: BaseEmbedding = None,
llm: LLM = None,
retriever_configs: list[BaseRetrieverConfig] = None,
ranker_configs: list[BaseRankerConfig] = None,
) -> "SimpleEngine":
"""From objs.
Args:
objs: List of RAGObject.
transformations: Parse documents to nodes. Default [SentenceSplitter].
embed_model: Parse nodes to embedding. Must supported by llama index. Default OpenAIEmbedding.
llm: Must supported by llama index. Default OpenAI.
retriever_configs: Configuration for retrievers. If more than one config, will use SimpleHybridRetriever.
ranker_configs: Configuration for rankers.
"""
if not objs and any(isinstance(config, BM25RetrieverConfig) for config in retriever_configs):
raise ValueError("In BM25RetrieverConfig, Objs must not be empty.")
objs = objs or []
nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
index = VectorStoreIndex(
nodes=nodes,
transformations=transformations or [SentenceSplitter()],
embed_model=cls._resolve_embed_model(embed_model, retriever_configs),
)
return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
@classmethod
def from_index(
cls,
index_config: BaseIndexConfig,
embed_model: BaseEmbedding = None,
llm: LLM = None,
retriever_configs: list[BaseRetrieverConfig] = None,
ranker_configs: list[BaseRankerConfig] = None,
) -> "SimpleEngine":
"""Load from previously maintained index by self.persist(), index_config contains persis_path."""
index = get_index(index_config, embed_model=cls._resolve_embed_model(embed_model, [index_config]))
return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
async def asearch(self, content: str, **kwargs) -> str:
"""Inplement tools.SearchInterface"""
return await self.aquery(content)
async def aretrieve(self, query: QueryType) -> list[NodeWithScore]:
"""Allow query to be str."""
query_bundle = QueryBundle(query) if isinstance(query, str) else query
nodes = await super().aretrieve(query_bundle)
self._try_reconstruct_obj(nodes)
return nodes
def add_docs(self, input_files: list[str]):
"""Add docs to retriever. retriever must has add_nodes func."""
self._ensure_retriever_modifiable()
documents = SimpleDirectoryReader(input_files=input_files).load_data()
self._fix_document_metadata(documents)
nodes = run_transformations(documents, transformations=self.index._transformations)
self._save_nodes(nodes)
def add_objs(self, objs: list[RAGObject]):
"""Adds objects to the retriever, storing each object's original form in metadata for future reference."""
self._ensure_retriever_modifiable()
nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
self._save_nodes(nodes)
def persist(self, persist_dir: Union[str, os.PathLike], **kwargs):
"""Persist."""
self._ensure_retriever_persistable()
self._persist(str(persist_dir), **kwargs)
@classmethod
def _from_index(
cls,
index: BaseIndex,
llm: LLM = None,
retriever_configs: list[BaseRetrieverConfig] = None,
ranker_configs: list[BaseRankerConfig] = None,
) -> "SimpleEngine":
llm = llm or get_rag_llm()
retriever = get_retriever(configs=retriever_configs, index=index) # Default index.as_retriever
rankers = get_rankers(configs=ranker_configs, llm=llm) # Default []
return cls(
retriever=retriever,
node_postprocessors=rankers,
response_synthesizer=get_response_synthesizer(llm=llm),
index=index,
)
def _ensure_retriever_modifiable(self):
self._ensure_retriever_of_type(ModifiableRAGRetriever)
def _ensure_retriever_persistable(self):
self._ensure_retriever_of_type(PersistableRAGRetriever)
def _ensure_retriever_of_type(self, required_type: BaseRetriever):
"""Ensure that self.retriever is required_type, or at least one of its components, if it's a SimpleHybridRetriever.
Args:
required_type: The class that the retriever is expected to be an instance of.
"""
if isinstance(self.retriever, SimpleHybridRetriever):
if not any(isinstance(r, required_type) for r in self.retriever.retrievers):
raise TypeError(
f"Must have at least one retriever of type {required_type.__name__} in SimpleHybridRetriever"
)
if not isinstance(self.retriever, required_type):
raise TypeError(f"The retriever is not of type {required_type.__name__}: {type(self.retriever)}")
def _save_nodes(self, nodes: list[BaseNode]):
self.retriever.add_nodes(nodes)
def _persist(self, persist_dir: str, **kwargs):
self.retriever.persist(persist_dir, **kwargs)
@staticmethod
def _try_reconstruct_obj(nodes: list[NodeWithScore]):
"""If node is object, then dynamically reconstruct object, and save object to node.metadata["obj"]."""
for node in nodes:
if node.metadata.get("is_obj", False):
obj_cls = import_class(node.metadata["obj_cls_name"], node.metadata["obj_mod_name"])
obj_dict = json.loads(node.metadata["obj_json"])
node.metadata["obj"] = obj_cls(**obj_dict)
@staticmethod
def _fix_document_metadata(documents: list[Document]):
"""LlamaIndex keep metadata['file_path'], which is unnecessary, maybe deleted in the near future."""
for doc in documents:
doc.excluded_embed_metadata_keys.append("file_path")
@staticmethod
def _resolve_embed_model(embed_model: BaseEmbedding = None, configs: list[Any] = None) -> BaseEmbedding:
if configs and all(isinstance(c, NoEmbedding) for c in configs):
return MockEmbedding(embed_dim=1)
return embed_model or get_rag_embedding()
| [
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.core.embeddings.mock_embed_model.MockEmbedding",
"llama_index.core.response_synthesizers.get_response_synthesizer",
"llama_index.core.ingestion.pipeline.run_transformations",
"llama_index.core.SimpleDirectoryReader",
"llama_index.core.schema.QueryBundle"
] | [((7145, 7220), 'llama_index.core.ingestion.pipeline.run_transformations', 'run_transformations', (['documents'], {'transformations': 'self.index._transformations'}), '(documents, transformations=self.index._transformations)\n', (7164, 7220), False, 'from llama_index.core.ingestion.pipeline import run_transformations\n'), ((8091, 8144), 'metagpt.rag.factories.get_retriever', 'get_retriever', ([], {'configs': 'retriever_configs', 'index': 'index'}), '(configs=retriever_configs, index=index)\n', (8104, 8144), False, 'from metagpt.rag.factories import get_index, get_rag_embedding, get_rag_llm, get_rankers, get_retriever\n'), ((8193, 8237), 'metagpt.rag.factories.get_rankers', 'get_rankers', ([], {'configs': 'ranker_configs', 'llm': 'llm'}), '(configs=ranker_configs, llm=llm)\n', (8204, 8237), False, 'from metagpt.rag.factories import get_index, get_rag_embedding, get_rag_llm, get_rankers, get_retriever\n'), ((6663, 6681), 'llama_index.core.schema.QueryBundle', 'QueryBundle', (['query'], {}), '(query)\n', (6674, 6681), False, 'from llama_index.core.schema import BaseNode, Document, NodeWithScore, QueryBundle, QueryType, TransformComponent\n'), ((8057, 8070), 'metagpt.rag.factories.get_rag_llm', 'get_rag_llm', ([], {}), '()\n', (8068, 8070), False, 'from metagpt.rag.factories import get_index, get_rag_embedding, get_rag_llm, get_rankers, get_retriever\n'), ((10657, 10683), 'llama_index.core.embeddings.mock_embed_model.MockEmbedding', 'MockEmbedding', ([], {'embed_dim': '(1)'}), '(embed_dim=1)\n', (10670, 10683), False, 'from llama_index.core.embeddings.mock_embed_model import MockEmbedding\n'), ((10715, 10734), 'metagpt.rag.factories.get_rag_embedding', 'get_rag_embedding', ([], {}), '()\n', (10732, 10734), False, 'from metagpt.rag.factories import get_index, get_rag_embedding, get_rag_llm, get_rankers, get_retriever\n'), ((3729, 3796), 'llama_index.core.SimpleDirectoryReader', 'SimpleDirectoryReader', ([], {'input_dir': 'input_dir', 'input_files': 'input_files'}), '(input_dir=input_dir, input_files=input_files)\n', (3750, 3796), False, 'from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n'), ((7022, 7068), 'llama_index.core.SimpleDirectoryReader', 'SimpleDirectoryReader', ([], {'input_files': 'input_files'}), '(input_files=input_files)\n', (7043, 7068), False, 'from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n'), ((8380, 8413), 'llama_index.core.response_synthesizers.get_response_synthesizer', 'get_response_synthesizer', ([], {'llm': 'llm'}), '(llm=llm)\n', (8404, 8413), False, 'from llama_index.core.response_synthesizers import BaseSynthesizer, get_response_synthesizer\n'), ((9956, 10030), 'metagpt.utils.common.import_class', 'import_class', (["node.metadata['obj_cls_name']", "node.metadata['obj_mod_name']"], {}), "(node.metadata['obj_cls_name'], node.metadata['obj_mod_name'])\n", (9968, 10030), False, 'from metagpt.utils.common import import_class\n'), ((10058, 10095), 'json.loads', 'json.loads', (["node.metadata['obj_json']"], {}), "(node.metadata['obj_json'])\n", (10068, 10095), False, 'import json\n'), ((5368, 5400), 'metagpt.rag.schema.ObjectNode.get_obj_metadata', 'ObjectNode.get_obj_metadata', (['obj'], {}), '(obj)\n', (5395, 5400), False, 'from metagpt.rag.schema import BaseIndexConfig, BaseRankerConfig, BaseRetrieverConfig, BM25RetrieverConfig, ObjectNode\n'), ((7518, 7550), 'metagpt.rag.schema.ObjectNode.get_obj_metadata', 'ObjectNode.get_obj_metadata', (['obj'], {}), '(obj)\n', (7545, 7550), False, 'from metagpt.rag.schema import BaseIndexConfig, BaseRankerConfig, BaseRetrieverConfig, BM25RetrieverConfig, ObjectNode\n'), ((3986, 4004), 'llama_index.core.node_parser.SentenceSplitter', 'SentenceSplitter', ([], {}), '()\n', (4002, 4004), False, 'from llama_index.core.node_parser import SentenceSplitter\n'), ((5526, 5544), 'llama_index.core.node_parser.SentenceSplitter', 'SentenceSplitter', ([], {}), '()\n', (5542, 5544), False, 'from llama_index.core.node_parser import SentenceSplitter\n')] |
import os
from typing import Optional, Dict
import openai
import pandas as pd
import llama_index
from llama_index.llms.openai import OpenAI
from llama_index.readers.schema.base import Document
from llama_index.readers import SimpleWebPageReader
from llama_index.prompts import PromptTemplate
from llama_index import ServiceContext, StorageContext, load_index_from_storage
from llama_index import LLMPredictor, OpenAIEmbedding
from llama_index.indices.vector_store.base import VectorStore
from llama_hub.github_repo import GithubClient, GithubRepositoryReader
from llama_hub.youtube_transcript import YoutubeTranscriptReader, is_youtube_video
from mindsdb.integrations.libs.base import BaseMLEngine
from mindsdb.utilities.config import Config
from mindsdb.utilities.security import is_private_url
from mindsdb.integrations.handlers.llama_index_handler import config
from mindsdb.integrations.handlers.llama_index_handler.github_loader_helper import (
_get_github_token,
_get_filter_file_extensions,
_get_filter_directories,
)
from mindsdb.integrations.utilities.handler_utils import get_api_key
def _validate_prompt_template(prompt_template: str):
if "{context_str}" not in prompt_template or "{query_str}" not in prompt_template:
raise Exception(
"Provided prompt template is invalid, missing `{context_str}`, `{query_str}`. Please ensure both placeholders are present and try again."
) # noqa
class LlamaIndexHandler(BaseMLEngine):
"""Integration with the LlamaIndex data framework for LLM applications."""
name = "llama_index"
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.generative = True
self.default_index_class = "GPTVectorStoreIndex"
self.supported_index_class = ["GPTVectorStoreIndex", "VectorStoreIndex"]
self.default_reader = "DFReader"
self.supported_reader = [
"DFReader",
"SimpleWebPageReader",
"GithubRepositoryReader",
"YoutubeTranscriptReader",
]
@staticmethod
def create_validation(target, args=None, **kwargs):
reader = args["using"].get("reader", "DFReader")
if reader not in config.data_loaders:
raise Exception(
f"Invalid reader argument. Please use one of {config.data_loaders.keys()}"
)
config_dict = config.data_loaders[reader]
missing_keys = [key for key in config_dict if key not in args["using"]]
if missing_keys:
raise Exception(f"{reader} requires {missing_keys} arguments")
if "prompt_template" in args["using"]:
_validate_prompt_template(args["using"]["prompt_template"])
if args["using"].get("mode") == "conversational":
for param in ("user_column", "assistant_column"):
if param not in args["using"]:
raise Exception(f"Conversational mode requires {param} parameter")
def create(
self,
target: str,
df: Optional[pd.DataFrame] = None,
args: Optional[Dict] = None,
) -> None:
if "using" not in args:
raise Exception(
"LlamaIndex engine requires a USING clause! Refer to its documentation for more details."
)
if "index_class" not in args["using"]:
args["using"]["index_class"] = self.default_index_class
elif args["using"]["index_class"] not in self.supported_index_class:
raise Exception(
f"Invalid index class argument. Please use one of {self.supported_index_class}"
)
if "reader" not in args["using"]:
args["using"]["reader"] = self.default_reader
elif args["using"]["reader"] not in self.supported_reader:
raise Exception(
f"Invalid operation mode. Please use one of {self.supported_reader}"
)
# workaround to create llama model without input data
if df is None or df.empty:
df = pd.DataFrame([{"text": ""}])
if args["using"]["reader"] == "DFReader":
dstrs = df.apply(
lambda x: ", ".join(
[f"{col}: {str(entry)}" for col, entry in zip(df.columns, x)]
),
axis=1,
)
reader = list(map(lambda x: Document(text=x), dstrs.tolist()))
elif args["using"]["reader"] == "SimpleWebPageReader":
url = args["using"]["source_url_link"]
config = Config()
is_cloud = config.get("cloud", False)
if is_cloud and is_private_url(url):
raise Exception(f"URL is private: {url}")
reader = SimpleWebPageReader(html_to_text=True).load_data([url])
elif args["using"]["reader"] == "GithubRepositoryReader":
engine_storage = self.engine_storage
key = "GITHUB_TOKEN"
github_token = get_api_key(
key, args["using"], engine_storage, strict=False
)
if github_token is None:
github_token = get_api_key(
key.lower(),
args["using"],
engine_storage,
strict=True,
)
github_client = GithubClient(github_token)
owner = args["using"]["owner"]
repo = args["using"]["repo"]
filter_file_extensions = _get_filter_file_extensions(args["using"])
filter_directories = _get_filter_directories(args["using"])
reader = GithubRepositoryReader(
github_client,
owner=owner,
repo=repo,
verbose=True,
filter_file_extensions=filter_file_extensions,
filter_directories=filter_directories,
).load_data(branch=args["using"].get("branch", "main"))
elif args["using"]["reader"] == "YoutubeTranscriptReader":
ytlinks = args["using"]["ytlinks"]
for link in ytlinks:
if not is_youtube_video(link):
raise Exception(f"Invalid youtube link: {link}")
reader = YoutubeTranscriptReader().load_data(ytlinks)
else:
raise Exception(
f"Invalid operation mode. Please use one of {self.supported_reader}."
)
self.model_storage.json_set("args", args)
index = self._setup_index(reader)
path = self.model_storage.folder_get("context")
index.storage_context.persist(persist_dir=path)
self.model_storage.folder_sync("context")
def update(self, args) -> None:
prompt_template = args["using"].get(
"prompt_template", args.get("prompt_template", None)
)
if prompt_template is not None:
_validate_prompt_template(prompt_template)
args_cur = self.model_storage.json_get("args")
args_cur["using"].update(args["using"])
# check new set of arguments
self.create_validation(None, args_cur)
self.model_storage.json_set("args", args_cur)
def predict(
self, df: Optional[pd.DataFrame] = None, args: Optional[Dict] = None
) -> pd.DataFrame:
pred_args = args["predict_params"] if args else {}
args = self.model_storage.json_get("args")
engine_kwargs = {}
if args["using"].get("mode") == "conversational":
user_column = args["using"]["user_column"]
assistant_column = args["using"]["assistant_column"]
messages = []
for row in df[:-1].to_dict("records"):
messages.append(f"user: {row[user_column]}")
messages.append(f"assistant: {row[assistant_column]}")
conversation = "\n".join(messages)
questions = [df.iloc[-1][user_column]]
if "prompt" in pred_args and pred_args["prompt"] is not None:
user_prompt = pred_args["prompt"]
else:
user_prompt = args["using"].get("prompt", "")
prompt_template = (
f"{user_prompt}\n"
f"---------------------\n"
f"We have provided context information below. \n"
f"{{context_str}}\n"
f"---------------------\n"
f"This is previous conversation history:\n"
f"{conversation}\n"
f"---------------------\n"
f"Given this information, please answer the question: {{query_str}}"
)
engine_kwargs["text_qa_template"] = PromptTemplate(prompt_template)
else:
input_column = args["using"].get("input_column", None)
prompt_template = args["using"].get(
"prompt_template", args.get("prompt_template", None)
)
if prompt_template is not None:
_validate_prompt_template(prompt_template)
engine_kwargs["text_qa_template"] = PromptTemplate(prompt_template)
if input_column is None:
raise Exception(
f"`input_column` must be provided at model creation time or through USING clause when predicting. Please try again."
) # noqa
if input_column not in df.columns:
raise Exception(
f'Column "{input_column}" not found in input data! Please try again.'
)
questions = df[input_column]
index_path = self.model_storage.folder_get("context")
storage_context = StorageContext.from_defaults(persist_dir=index_path)
service_context = self._get_service_context()
index = load_index_from_storage(
storage_context, service_context=service_context
)
query_engine = index.as_query_engine(**engine_kwargs)
results = []
for question in questions:
query_results = query_engine.query(
question
) # TODO: provide extra_info in explain_target col
results.append(query_results.response)
result_df = pd.DataFrame(
{"question": questions, args["target"]: results}
) # result_df['answer'].tolist()
return result_df
def _get_service_context(self):
args = self.model_storage.json_get("args")
engine_storage = self.engine_storage
openai_api_key = get_api_key('openai', args["using"], engine_storage, strict=True)
llm_kwargs = {"openai_api_key": openai_api_key}
if "temperature" in args["using"]:
llm_kwargs["temperature"] = args["using"]["temperature"]
if "model_name" in args["using"]:
llm_kwargs["model_name"] = args["using"]["model_name"]
if "max_tokens" in args["using"]:
llm_kwargs["max_tokens"] = args["using"]["max_tokens"]
llm = OpenAI(**llm_kwargs) # TODO: all usual params should go here
embed_model = OpenAIEmbedding(api_key=openai_api_key)
service_context = ServiceContext.from_defaults(
llm=llm,
embed_model=embed_model
)
return service_context
def _setup_index(self, documents):
args = self.model_storage.json_get("args")
indexer: VectorStore = getattr(llama_index, args["using"]["index_class"])
index = indexer.from_documents(
documents, service_context=self._get_service_context()
)
return index
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# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
import os
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.bridge.pydantic import Field, PrivateAttr
from llama_index.callbacks import CallbackManager
from llama_index.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS
from llama_index.llms.base import (
ChatMessage,
ChatResponse,
CompletionResponse,
LLMMetadata,
llm_chat_callback,
llm_completion_callback,
)
from llama_index.llms.custom import CustomLLM
from llama_index.llms.generic_utils import completion_response_to_chat_response
from llama_index.llms.generic_utils import (
messages_to_prompt as generic_messages_to_prompt,
)
from transformers import LlamaTokenizer
import gc
import json
import torch
import numpy as np
from tensorrt_llm.runtime import ModelConfig, SamplingConfig
import tensorrt_llm
from pathlib import Path
import uuid
import time
EOS_TOKEN = 2
PAD_TOKEN = 2
class TrtLlmAPI(CustomLLM):
model_path: Optional[str] = Field(
description="The path to the trt engine."
)
temperature: float = Field(description="The temperature to use for sampling.")
max_new_tokens: int = Field(description="The maximum number of tokens to generate.")
context_window: int = Field(
description="The maximum number of context tokens for the model."
)
messages_to_prompt: Callable = Field(
description="The function to convert messages to a prompt.", exclude=True
)
completion_to_prompt: Callable = Field(
description="The function to convert a completion to a prompt.", exclude=True
)
generate_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for generation."
)
model_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for model initialization."
)
verbose: bool = Field(description="Whether to print verbose output.")
_model: Any = PrivateAttr()
_model_config: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_max_new_tokens = PrivateAttr()
_sampling_config = PrivateAttr()
_verbose = PrivateAttr()
def __init__(
self,
model_path: Optional[str] = None,
engine_name: Optional[str] = None,
tokenizer_dir: Optional[str] = None,
temperature: float = 0.1,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
context_window: int = DEFAULT_CONTEXT_WINDOW,
messages_to_prompt: Optional[Callable] = None,
completion_to_prompt: Optional[Callable] = None,
callback_manager: Optional[CallbackManager] = None,
generate_kwargs: Optional[Dict[str, Any]] = None,
model_kwargs: Optional[Dict[str, Any]] = None,
verbose: bool = False
) -> None:
model_kwargs = model_kwargs or {}
model_kwargs.update({"n_ctx": context_window, "verbose": verbose})
self._max_new_tokens = max_new_tokens
self._verbose = verbose
# check if model is cached
if model_path is not None:
if not os.path.exists(model_path):
raise ValueError(
"Provided model path does not exist. "
"Please check the path or provide a model_url to download."
)
else:
engine_dir = model_path
engine_dir_path = Path(engine_dir)
config_path = engine_dir_path / 'config.json'
# config function
with open(config_path, 'r') as f:
config = json.load(f)
use_gpt_attention_plugin = config['plugin_config']['gpt_attention_plugin']
remove_input_padding = config['plugin_config']['remove_input_padding']
tp_size = config['builder_config']['tensor_parallel']
pp_size = config['builder_config']['pipeline_parallel']
world_size = tp_size * pp_size
assert world_size == tensorrt_llm.mpi_world_size(), \
f'Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})'
num_heads = config['builder_config']['num_heads'] // tp_size
hidden_size = config['builder_config']['hidden_size'] // tp_size
vocab_size = config['builder_config']['vocab_size']
num_layers = config['builder_config']['num_layers']
num_kv_heads = config['builder_config'].get('num_kv_heads', num_heads)
paged_kv_cache = config['plugin_config']['paged_kv_cache']
if config['builder_config'].get('multi_query_mode', False):
tensorrt_llm.logger.warning(
"`multi_query_mode` config is deprecated. Please rebuild the engine."
)
num_kv_heads = 1
num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size
self._model_config = ModelConfig(num_heads=num_heads,
num_kv_heads=num_kv_heads,
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
gpt_attention_plugin=use_gpt_attention_plugin,
paged_kv_cache=paged_kv_cache,
remove_input_padding=remove_input_padding)
assert pp_size == 1, 'Python runtime does not support pipeline parallelism'
world_size = tp_size * pp_size
runtime_rank = tensorrt_llm.mpi_rank()
runtime_mapping = tensorrt_llm.Mapping(world_size,
runtime_rank,
tp_size=tp_size,
pp_size=pp_size)
torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
self._tokenizer = LlamaTokenizer.from_pretrained(tokenizer_dir, legacy=False)
self._sampling_config = SamplingConfig(end_id=EOS_TOKEN,
pad_id=PAD_TOKEN,
num_beams=1,
temperature=temperature)
serialize_path = engine_dir_path / engine_name
with open(serialize_path, 'rb') as f:
engine_buffer = f.read()
decoder = tensorrt_llm.runtime.GenerationSession(self._model_config,
engine_buffer,
runtime_mapping,
debug_mode=False)
self._model = decoder
messages_to_prompt = messages_to_prompt or generic_messages_to_prompt
completion_to_prompt = completion_to_prompt or (lambda x: x)
generate_kwargs = generate_kwargs or {}
generate_kwargs.update(
{"temperature": temperature, "max_tokens": max_new_tokens}
)
super().__init__(
model_path=model_path,
temperature=temperature,
context_window=context_window,
max_new_tokens=max_new_tokens,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
callback_manager=callback_manager,
generate_kwargs=generate_kwargs,
model_kwargs=model_kwargs,
verbose=verbose,
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "TrtLlmAPI"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_new_tokens,
model_name=self.model_path,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
self.generate_kwargs.update({"stream": False})
is_formatted = kwargs.pop("formatted", False)
if not is_formatted:
prompt = self.completion_to_prompt(prompt)
input_text = prompt
input_ids, input_lengths = self.parse_input(input_text, self._tokenizer,
EOS_TOKEN,
self._model_config)
max_input_length = torch.max(input_lengths).item()
self._model.setup(input_lengths.size(0), max_input_length, self._max_new_tokens, 1) # beam size is set to 1
if self._verbose:
start_time = time.time()
output_ids = self._model.decode(input_ids, input_lengths, self._sampling_config)
torch.cuda.synchronize()
elapsed_time = None
if self._verbose:
end_time = time.time()
elapsed_time = end_time - start_time
output_txt, output_token_ids = self.get_output(output_ids,
input_lengths,
self._max_new_tokens,
self._tokenizer)
if self._verbose:
print(f"Input context length : {input_ids.shape[1]}")
print(f"Inference time : {elapsed_time:.2f} seconds")
print(f"Output context length : {len(output_token_ids)} ")
print(f"Inference token/sec : {(len(output_token_ids) / elapsed_time):2f}")
# call garbage collected after inference
torch.cuda.empty_cache()
gc.collect()
return CompletionResponse(text=output_txt, raw=self.generate_completion_dict(output_txt))
def parse_input(self, input_text: str, tokenizer, end_id: int,
remove_input_padding: bool):
input_tokens = []
input_tokens.append(
tokenizer.encode(input_text, add_special_tokens=False))
input_lengths = torch.tensor([len(x) for x in input_tokens],
dtype=torch.int32,
device='cuda')
if remove_input_padding:
input_ids = np.concatenate(input_tokens)
input_ids = torch.tensor(input_ids, dtype=torch.int32,
device='cuda').unsqueeze(0)
else:
input_ids = torch.nested.to_padded_tensor(
torch.nested.nested_tensor(input_tokens, dtype=torch.int32),
end_id).cuda()
return input_ids, input_lengths
def remove_extra_eos_ids(self, outputs):
outputs.reverse()
while outputs and outputs[0] == 2:
outputs.pop(0)
outputs.reverse()
outputs.append(2)
return outputs
def get_output(self, output_ids, input_lengths, max_output_len, tokenizer):
num_beams = output_ids.size(1)
output_text = ""
outputs = None
for b in range(input_lengths.size(0)):
for beam in range(num_beams):
output_begin = input_lengths[b]
output_end = input_lengths[b] + max_output_len
outputs = output_ids[b][beam][output_begin:output_end].tolist()
outputs = self.remove_extra_eos_ids(outputs)
output_text = tokenizer.decode(outputs)
return output_text, outputs
def generate_completion_dict(self, text_str):
"""
Generate a dictionary for text completion details.
Returns:
dict: A dictionary containing completion details.
"""
completion_id: str = f"cmpl-{str(uuid.uuid4())}"
created: int = int(time.time())
model_name: str = self._model if self._model is not None else self.model_path
return {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": text_str,
"index": 0,
"logprobs": None,
"finish_reason": 'stop'
}
],
"usage": {
"prompt_tokens": None,
"completion_tokens": None,
"total_tokens": None
}
}
@llm_completion_callback()
def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
pass
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from llama_index.core.callbacks.schema import CBEventType, EventPayload
from llama_index.core.llms import ChatMessage, ChatResponse
from llama_index.core.schema import NodeWithScore, TextNode
import chainlit as cl
@cl.on_chat_start
async def start():
await cl.Message(content="LlamaIndexCb").send()
cb = cl.LlamaIndexCallbackHandler()
cb.on_event_start(CBEventType.RETRIEVE, payload={})
await cl.sleep(0.2)
cb.on_event_end(
CBEventType.RETRIEVE,
payload={
EventPayload.NODES: [
NodeWithScore(node=TextNode(text="This is text1"), score=1)
]
},
)
cb.on_event_start(CBEventType.LLM)
await cl.sleep(0.2)
response = ChatResponse(message=ChatMessage(content="This is the LLM response"))
cb.on_event_end(
CBEventType.LLM,
payload={
EventPayload.RESPONSE: response,
EventPayload.PROMPT: "This is the LLM prompt",
},
)
| [
"llama_index.core.schema.TextNode",
"llama_index.core.llms.ChatMessage"
] | [((316, 346), 'chainlit.LlamaIndexCallbackHandler', 'cl.LlamaIndexCallbackHandler', ([], {}), '()\n', (344, 346), True, 'import chainlit as cl\n'), ((415, 428), 'chainlit.sleep', 'cl.sleep', (['(0.2)'], {}), '(0.2)\n', (423, 428), True, 'import chainlit as cl\n'), ((691, 704), 'chainlit.sleep', 'cl.sleep', (['(0.2)'], {}), '(0.2)\n', (699, 704), True, 'import chainlit as cl\n'), ((742, 789), 'llama_index.core.llms.ChatMessage', 'ChatMessage', ([], {'content': '"""This is the LLM response"""'}), "(content='This is the LLM response')\n", (753, 789), False, 'from llama_index.core.llms import ChatMessage, ChatResponse\n'), ((264, 298), 'chainlit.Message', 'cl.Message', ([], {'content': '"""LlamaIndexCb"""'}), "(content='LlamaIndexCb')\n", (274, 298), True, 'import chainlit as cl\n'), ((568, 598), 'llama_index.core.schema.TextNode', 'TextNode', ([], {'text': '"""This is text1"""'}), "(text='This is text1')\n", (576, 598), False, 'from llama_index.core.schema import NodeWithScore, TextNode\n')] |
import requests
from bs4 import BeautifulSoup
from llama_index import GPTSimpleVectorIndex
from llama_index.readers.database import DatabaseReader
from env import settings
from logger import logger
from .base import BaseToolSet, SessionGetter, ToolScope, tool
class RequestsGet(BaseToolSet):
@tool(
name="Requests Get",
description="A portal to the internet. "
"Use this when you need to get specific content from a website."
"Input should be a url (i.e. https://www.google.com)."
"The output will be the text response of the GET request.",
)
def get(self, url: str) -> str:
"""Run the tool."""
html = requests.get(url).text
soup = BeautifulSoup(html)
non_readable_tags = soup.find_all(
["script", "style", "header", "footer", "form"]
)
for non_readable_tag in non_readable_tags:
non_readable_tag.extract()
content = soup.get_text("\n", strip=True)
if len(content) > 300:
content = content[:300] + "..."
logger.debug(
f"\nProcessed RequestsGet, Input Url: {url} " f"Output Contents: {content}"
)
return content
class WineDB(BaseToolSet):
def __init__(self):
db = DatabaseReader(
scheme="postgresql", # Database Scheme
host=settings["WINEDB_HOST"], # Database Host
port="5432", # Database Port
user="alphadom", # Database User
password=settings["WINEDB_PASSWORD"], # Database Password
dbname="postgres", # Database Name
)
self.columns = ["nameEn", "nameKo", "description"]
concat_columns = str(",'-',".join([f'"{i}"' for i in self.columns]))
query = f"""
SELECT
Concat({concat_columns})
FROM wine
"""
documents = db.load_data(query=query)
self.index = GPTSimpleVectorIndex(documents)
@tool(
name="Wine Recommendation",
description="A tool to recommend wines based on a user's input. "
"Inputs are necessary factors for wine recommendations, such as the user's mood today, side dishes to eat with wine, people to drink wine with, what things you want to do, the scent and taste of their favorite wine."
"The output will be a list of recommended wines."
"The tool is based on a database of wine reviews, which is stored in a database.",
)
def recommend(self, query: str) -> str:
"""Run the tool."""
results = self.index.query(query)
wine = "\n".join(
[
f"{i}:{j}"
for i, j in zip(
self.columns, results.source_nodes[0].source_text.split("-")
)
]
)
output = results.response + "\n\n" + wine
logger.debug(
f"\nProcessed WineDB, Input Query: {query} " f"Output Wine: {wine}"
)
return output
class ExitConversation(BaseToolSet):
@tool(
name="Exit Conversation",
description="A tool to exit the conversation. "
"Use this when you want to exit the conversation. "
"The input should be a message that the conversation is over.",
scope=ToolScope.SESSION,
)
def exit(self, message: str, get_session: SessionGetter) -> str:
"""Run the tool."""
_, executor = get_session()
del executor
logger.debug(f"\nProcessed ExitConversation.")
return message
| [
"llama_index.readers.database.DatabaseReader",
"llama_index.GPTSimpleVectorIndex"
] | [((713, 732), 'bs4.BeautifulSoup', 'BeautifulSoup', (['html'], {}), '(html)\n', (726, 732), False, 'from bs4 import BeautifulSoup\n'), ((1073, 1166), 'logger.logger.debug', 'logger.debug', (['f"""\nProcessed RequestsGet, Input Url: {url} Output Contents: {content}"""'], {}), '(\n f"""\nProcessed RequestsGet, Input Url: {url} Output Contents: {content}""")\n', (1085, 1166), False, 'from logger import logger\n'), ((1275, 1437), 'llama_index.readers.database.DatabaseReader', 'DatabaseReader', ([], {'scheme': '"""postgresql"""', 'host': "settings['WINEDB_HOST']", 'port': '"""5432"""', 'user': '"""alphadom"""', 'password': "settings['WINEDB_PASSWORD']", 'dbname': '"""postgres"""'}), "(scheme='postgresql', host=settings['WINEDB_HOST'], port=\n '5432', user='alphadom', password=settings['WINEDB_PASSWORD'], dbname=\n 'postgres')\n", (1289, 1437), False, 'from llama_index.readers.database import DatabaseReader\n'), ((1937, 1968), 'llama_index.GPTSimpleVectorIndex', 'GPTSimpleVectorIndex', (['documents'], {}), '(documents)\n', (1957, 1968), False, 'from llama_index import GPTSimpleVectorIndex\n'), ((2867, 2952), 'logger.logger.debug', 'logger.debug', (['f"""\nProcessed WineDB, Input Query: {query} Output Wine: {wine}"""'], {}), '(f"""\nProcessed WineDB, Input Query: {query} Output Wine: {wine}"""\n )\n', (2879, 2952), False, 'from logger import logger\n'), ((3468, 3517), 'logger.logger.debug', 'logger.debug', (['f"""\nProcessed ExitConversation."""'], {}), '(f"""\nProcessed ExitConversation.""")\n', (3480, 3517), False, 'from logger import logger\n'), ((675, 692), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (687, 692), False, 'import requests\n')] |
try:
from llama_index import Document
from llama_index.text_splitter import SentenceSplitter
except ImportError:
from llama_index.core import Document
from llama_index.core.text_splitter import SentenceSplitter
def llama_index_sentence_splitter(
documents: list[str], document_ids: list[str], chunk_size=256
):
chunk_overlap = min(chunk_size / 4, min(chunk_size / 2, 64))
chunks = []
node_parser = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
docs = [[Document(text=doc)] for doc in documents]
for doc_id, doc in zip(document_ids, docs):
chunks += [
{"document_id": doc_id, "content": node.text} for node in node_parser(doc)
]
return chunks
| [
"llama_index.core.text_splitter.SentenceSplitter",
"llama_index.core.Document"
] | [((432, 500), 'llama_index.core.text_splitter.SentenceSplitter', 'SentenceSplitter', ([], {'chunk_size': 'chunk_size', 'chunk_overlap': 'chunk_overlap'}), '(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n', (448, 500), False, 'from llama_index.core.text_splitter import SentenceSplitter\n'), ((514, 532), 'llama_index.core.Document', 'Document', ([], {'text': 'doc'}), '(text=doc)\n', (522, 532), False, 'from llama_index.core import Document\n')] |
"""
Creates RAG dataset for tutorial notebooks and persists to disk.
"""
import argparse
import logging
import sys
from typing import List, Optional
import llama_index
import numpy as np
import pandas as pd
from gcsfs import GCSFileSystem
from llama_index import ServiceContext, StorageContext, load_index_from_storage
from llama_index.callbacks import CallbackManager, OpenInferenceCallbackHandler
from llama_index.callbacks.open_inference_callback import as_dataframe
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms import OpenAI
from phoenix.experimental.evals.retrievals import (
classify_relevance,
compute_precisions_at_k,
)
from tqdm import tqdm
def create_user_feedback(
first_document_relevances: List[Optional[bool]],
second_document_relevances: List[Optional[bool]],
) -> List[Optional[bool]]:
"""_summary_
Args:
first_document_relevances (List[Optional[bool]]): _description_
second_document_relevances (List[Optional[bool]]): _description_
Returns:
List[Optional[bool]]: _description_
"""
if len(first_document_relevances) != len(second_document_relevances):
raise ValueError()
first_document_relevances_array = np.array(first_document_relevances)
second_document_relevances_array = np.array(second_document_relevances)
failed_retrieval_mask = ~first_document_relevances_array & ~second_document_relevances_array
num_failed_retrievals = failed_retrieval_mask.sum()
num_thumbs_down = int(0.75 * num_failed_retrievals)
failed_retrieval_indexes = np.where(failed_retrieval_mask)[0]
thumbs_down_mask = np.random.choice(
failed_retrieval_indexes, size=num_thumbs_down, replace=False
)
successful_retrieval_mask = ~failed_retrieval_mask
num_successful_retrievals = successful_retrieval_mask.sum()
num_thumbs_up = int(0.25 * num_successful_retrievals)
successful_retrieval_indexes = np.where(successful_retrieval_mask)[0]
thumbs_up_mask = np.random.choice(
successful_retrieval_indexes, size=num_thumbs_up, replace=False
)
user_feedback_array = np.full(len(first_document_relevances), np.nan, dtype=np.float32)
user_feedback_array[thumbs_down_mask] = -1.0
user_feedback_array[thumbs_up_mask] = 1.0
return [None if np.isnan(value) else value for value in user_feedback_array.tolist()]
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG, stream=sys.stdout)
parser = argparse.ArgumentParser()
parser.add_argument("--index-path", type=str, required=True, help="Path to persisted index.")
parser.add_argument(
"--use-gcs",
action="store_true",
help="If this flag is set, the index will be loaded from GCS.",
)
parser.add_argument(
"--query-path", type=str, required=True, help="Path to CSV file containing queries."
)
parser.add_argument(
"--output-path", type=str, required=True, help="Path to output Parquet file."
)
args = parser.parse_args()
llama_index.prompts.default_prompts.DEFAULT_TEXT_QA_PROMPT_TMPL = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information, "
"answer the question and be as helpful as possible: {query_str}\n"
) # This prompt has been tweaked to make the system less conservative for demo purposes.
queries = pd.read_csv(args.query_path)["Question"].tolist()
file_system = GCSFileSystem(project="public-assets-275721") if args.use_gcs else None
storage_context = StorageContext.from_defaults(
fs=file_system,
persist_dir=args.index_path,
)
callback_handler = OpenInferenceCallbackHandler()
service_context = ServiceContext.from_defaults(
llm=OpenAI(model="text-davinci-003"),
embed_model=OpenAIEmbedding(model="text-embedding-ada-002"),
callback_manager=CallbackManager(handlers=[callback_handler]),
)
index = load_index_from_storage(
storage_context,
service_context=service_context,
)
query_engine = index.as_query_engine()
logging.info("Running queries")
for query in tqdm(queries):
query_engine.query(query)
query_dataframe = as_dataframe(callback_handler.flush_query_data_buffer())
document_dataframe = as_dataframe(callback_handler.flush_node_data_buffer())
query_texts = query_dataframe[":feature.text:prompt"].tolist()
list_of_document_id_lists = query_dataframe[
":feature.[str].retrieved_document_ids:prompt"
].tolist()
document_id_to_text = dict(
zip(document_dataframe["id"].to_list(), document_dataframe["node_text"].to_list())
)
first_document_texts, second_document_texts = [
[
document_id_to_text[document_ids[document_index]]
for document_ids in list_of_document_id_lists
]
for document_index in [0, 1]
]
logging.info("Computing LLM-assisted ranking metrics")
first_document_relevances, second_document_relevances = [
[
classify_relevance(query_text, document_text, model_name="gpt-4")
for query_text, document_text in tqdm(zip(query_texts, first_document_texts))
]
for document_texts in [first_document_texts, second_document_texts]
]
list_of_precisions_at_k_lists = [
compute_precisions_at_k([rel0, rel1])
for rel0, rel1 in zip(first_document_relevances, second_document_relevances)
]
precisions_at_1, precisions_at_2 = [
[precisions_at_k[index] for precisions_at_k in list_of_precisions_at_k_lists]
for index in [0, 1]
]
document_similarity_0, document_similarity_1 = [
[
scores[index]
for scores in query_dataframe[
":feature.[float].retrieved_document_scores:prompt"
].tolist()
]
for index in [0, 1]
]
user_feedback = create_user_feedback(first_document_relevances, second_document_relevances)
logging.info(
f"Thumbs up: {sum([value == 1.0 for value in user_feedback]) / len(user_feedback)}"
)
logging.info(
f"Thumbs down: {sum([value == -1.0 for value in user_feedback]) / len(user_feedback)}"
)
query_dataframe = query_dataframe.assign(
**{
":tag.bool:relevance_0": first_document_relevances,
":tag.bool:relevance_1": second_document_relevances,
":tag.float:precision_at_1": precisions_at_1,
":tag.float:precision_at_2": precisions_at_2,
":tag.float:document_similarity_0": document_similarity_0,
":tag.float:document_similarity_1": document_similarity_1,
":tag.float:user_feedback": user_feedback,
}
)
query_dataframe.to_parquet(args.output_path)
| [
"llama_index.callbacks.OpenInferenceCallbackHandler",
"llama_index.StorageContext.from_defaults",
"llama_index.llms.OpenAI",
"llama_index.load_index_from_storage",
"llama_index.callbacks.CallbackManager",
"llama_index.embeddings.openai.OpenAIEmbedding"
] | [((1235, 1270), 'numpy.array', 'np.array', (['first_document_relevances'], {}), '(first_document_relevances)\n', (1243, 1270), True, 'import numpy as np\n'), ((1310, 1346), 'numpy.array', 'np.array', (['second_document_relevances'], {}), '(second_document_relevances)\n', (1318, 1346), True, 'import numpy as np\n'), ((1645, 1724), 'numpy.random.choice', 'np.random.choice', (['failed_retrieval_indexes'], {'size': 'num_thumbs_down', 'replace': '(False)'}), '(failed_retrieval_indexes, size=num_thumbs_down, replace=False)\n', (1661, 1724), True, 'import numpy as np\n'), ((2011, 2097), 'numpy.random.choice', 'np.random.choice', (['successful_retrieval_indexes'], {'size': 'num_thumbs_up', 'replace': '(False)'}), '(successful_retrieval_indexes, size=num_thumbs_up, replace=\n False)\n', (2027, 2097), True, 'import numpy as np\n'), ((2417, 2476), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.DEBUG', 'stream': 'sys.stdout'}), '(level=logging.DEBUG, stream=sys.stdout)\n', (2436, 2476), False, 'import logging\n'), ((2491, 2516), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2514, 2516), False, 'import argparse\n'), ((3637, 3710), 'llama_index.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'fs': 'file_system', 'persist_dir': 'args.index_path'}), '(fs=file_system, persist_dir=args.index_path)\n', (3665, 3710), False, 'from llama_index import ServiceContext, StorageContext, load_index_from_storage\n'), ((3757, 3787), 'llama_index.callbacks.OpenInferenceCallbackHandler', 'OpenInferenceCallbackHandler', ([], {}), '()\n', (3785, 3787), False, 'from llama_index.callbacks import CallbackManager, OpenInferenceCallbackHandler\n'), ((4044, 4117), 'llama_index.load_index_from_storage', 'load_index_from_storage', (['storage_context'], {'service_context': 'service_context'}), '(storage_context, service_context=service_context)\n', (4067, 4117), False, 'from llama_index import ServiceContext, StorageContext, load_index_from_storage\n'), ((4189, 4220), 'logging.info', 'logging.info', (['"""Running queries"""'], {}), "('Running queries')\n", (4201, 4220), False, 'import logging\n'), ((4238, 4251), 'tqdm.tqdm', 'tqdm', (['queries'], {}), '(queries)\n', (4242, 4251), False, 'from tqdm import tqdm\n'), ((5004, 5058), 'logging.info', 'logging.info', (['"""Computing LLM-assisted ranking metrics"""'], {}), "('Computing LLM-assisted ranking metrics')\n", (5016, 5058), False, 'import logging\n'), ((1587, 1618), 'numpy.where', 'np.where', (['failed_retrieval_mask'], {}), '(failed_retrieval_mask)\n', (1595, 1618), True, 'import numpy as np\n'), ((1951, 1986), 'numpy.where', 'np.where', (['successful_retrieval_mask'], {}), '(successful_retrieval_mask)\n', (1959, 1986), True, 'import numpy as np\n'), ((3543, 3588), 'gcsfs.GCSFileSystem', 'GCSFileSystem', ([], {'project': '"""public-assets-275721"""'}), "(project='public-assets-275721')\n", (3556, 3588), False, 'from gcsfs import GCSFileSystem\n'), ((5437, 5474), 'phoenix.experimental.evals.retrievals.compute_precisions_at_k', 'compute_precisions_at_k', (['[rel0, rel1]'], {}), '([rel0, rel1])\n', (5460, 5474), False, 'from phoenix.experimental.evals.retrievals import classify_relevance, compute_precisions_at_k\n'), ((2314, 2329), 'numpy.isnan', 'np.isnan', (['value'], {}), '(value)\n', (2322, 2329), True, 'import numpy as np\n'), ((3852, 3884), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""text-davinci-003"""'}), "(model='text-davinci-003')\n", (3858, 3884), False, 'from llama_index.llms import OpenAI\n'), ((3906, 3953), 'llama_index.embeddings.openai.OpenAIEmbedding', 'OpenAIEmbedding', ([], {'model': '"""text-embedding-ada-002"""'}), "(model='text-embedding-ada-002')\n", (3921, 3953), False, 'from llama_index.embeddings.openai import OpenAIEmbedding\n'), ((3980, 4024), 'llama_index.callbacks.CallbackManager', 'CallbackManager', ([], {'handlers': '[callback_handler]'}), '(handlers=[callback_handler])\n', (3995, 4024), False, 'from llama_index.callbacks import CallbackManager, OpenInferenceCallbackHandler\n'), ((5143, 5208), 'phoenix.experimental.evals.retrievals.classify_relevance', 'classify_relevance', (['query_text', 'document_text'], {'model_name': '"""gpt-4"""'}), "(query_text, document_text, model_name='gpt-4')\n", (5161, 5208), False, 'from phoenix.experimental.evals.retrievals import classify_relevance, compute_precisions_at_k\n'), ((3475, 3503), 'pandas.read_csv', 'pd.read_csv', (['args.query_path'], {}), '(args.query_path)\n', (3486, 3503), True, 'import pandas as pd\n')] |
import logging
import os
import time
import typing
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional
import numpy as np
from llama_index.core.schema import BaseNode, MetadataMode, TextNode
from llama_index.core.vector_stores.types import (
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
from llama_index.core.vector_stores.utils import (
legacy_metadata_dict_to_node,
metadata_dict_to_node,
node_to_metadata_dict,
)
if TYPE_CHECKING:
import vearch
logger = logging.getLogger(__name__)
class VearchVectorStore(VectorStore):
"""
Vearch vector store:
embeddings are stored within a Vearch table.
when query, the index uses Vearch to query for the top
k most similar nodes.
Args:
chroma_collection (chromadb.api.models.Collection.Collection):
ChromaDB collection instance
"""
flat_metadata: bool = True
stores_text: bool = True
_DEFAULT_TABLE_NAME = "liama_index_vearch"
_DEFAULT_CLUSTER_DB_NAME = "liama_index_vearch_client_db"
_DEFAULT_VERSION = 1
def __init__(
self,
path_or_url: Optional[str] = None,
table_name: str = _DEFAULT_TABLE_NAME,
db_name: str = _DEFAULT_CLUSTER_DB_NAME,
flag: int = _DEFAULT_VERSION,
**kwargs: Any,
) -> None:
"""
Initialize vearch vector store
flag 1 for cluster,0 for standalone.
"""
try:
if flag:
import vearch_cluster
else:
import vearch
except ImportError:
raise ValueError(
"Could not import suitable python package."
"Please install it with `pip install vearch or vearch_cluster."
)
if flag:
if path_or_url is None:
raise ValueError("Please input url of cluster")
if not db_name:
db_name = self._DEFAULT_CLUSTER_DB_NAME
db_name += "_"
db_name += str(uuid.uuid4()).split("-")[-1]
self.using_db_name = db_name
self.url = path_or_url
self.vearch = vearch_cluster.VearchCluster(path_or_url)
else:
if path_or_url is None:
metadata_path = os.getcwd().replace("\\", "/")
else:
metadata_path = path_or_url
if not os.path.isdir(metadata_path):
os.makedirs(metadata_path)
log_path = os.path.join(metadata_path, "log")
if not os.path.isdir(log_path):
os.makedirs(log_path)
self.vearch = vearch.Engine(metadata_path, log_path)
self.using_metapath = metadata_path
if not table_name:
table_name = self._DEFAULT_TABLE_NAME
table_name += "_"
table_name += str(uuid.uuid4()).split("-")[-1]
self.using_table_name = table_name
self.flag = flag
@property
def client(self) -> Any:
"""Get client."""
return self.vearch
def _get_matadata_field(self, metadatas: Optional[List[dict]] = None) -> None:
field_list = []
if metadatas:
for key, value in metadatas[0].items():
if isinstance(value, int):
field_list.append({"field": key, "type": "int"})
continue
if isinstance(value, str):
field_list.append({"field": key, "type": "str"})
continue
if isinstance(value, float):
field_list.append({"field": key, "type": "float"})
continue
else:
raise ValueError("Please check data type,support int, str, float")
self.field_list = field_list
def _add_texts(
self,
ids: Iterable[str],
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
**kwargs: Any,
) -> List[str]:
"""
Returns:
List of ids from adding the texts into the vectorstore.
"""
if embeddings is None:
raise ValueError("embeddings is None")
self._get_matadata_field(metadatas)
if self.flag:
dbs_list = self.vearch.list_dbs()
if self.using_db_name not in dbs_list:
create_db_code = self.vearch.create_db(self.using_db_name)
if not create_db_code:
raise ValueError("create db failed!!!")
space_list = self.vearch.list_spaces(self.using_db_name)
if self.using_table_name not in space_list:
create_space_code = self._create_space(len(embeddings[0]))
if not create_space_code:
raise ValueError("create space failed!!!")
docid = []
if embeddings is not None and metadatas is not None:
meta_field_list = [i["field"] for i in self.field_list]
for text, metadata, embed, id_d in zip(
texts, metadatas, embeddings, ids
):
profiles: typing.Dict[str, Any] = {}
profiles["text"] = text
for f in meta_field_list:
profiles[f] = metadata[f]
embed_np = np.array(embed)
profiles["text_embedding"] = {
"feature": (embed_np / np.linalg.norm(embed_np)).tolist()
}
insert_res = self.vearch.insert_one(
self.using_db_name, self.using_table_name, profiles, id_d
)
if insert_res["status"] == 200:
docid.append(insert_res["_id"])
continue
else:
retry_insert = self.vearch.insert_one(
self.using_db_name, self.using_table_name, profiles
)
docid.append(retry_insert["_id"])
continue
else:
table_path = os.path.join(
self.using_metapath, self.using_table_name + ".schema"
)
if not os.path.exists(table_path):
dim = len(embeddings[0])
response_code = self._create_table(dim)
if response_code:
raise ValueError("create table failed!!!")
if embeddings is not None and metadatas is not None:
doc_items = []
meta_field_list = [i["field"] for i in self.field_list]
for text, metadata, embed, id_d in zip(
texts, metadatas, embeddings, ids
):
profiles_v: typing.Dict[str, Any] = {}
profiles_v["text"] = text
profiles_v["_id"] = id_d
for f in meta_field_list:
profiles_v[f] = metadata[f]
embed_np = np.array(embed)
profiles_v["text_embedding"] = embed_np / np.linalg.norm(embed_np)
doc_items.append(profiles_v)
docid = self.vearch.add(doc_items)
t_time = 0
while len(docid) != len(embeddings):
time.sleep(0.5)
if t_time > 6:
break
t_time += 1
self.vearch.dump()
return docid
def _create_table(
self,
dim: int = 1024,
) -> int:
"""
Create Standalone VectorStore Table.
Args:
dim:dimension of vector.
fields_list: the field you want to store.
Return:
code,0 for success,1 for failed.
"""
type_dict = {
"int": vearch.dataType.INT,
"str": vearch.dataType.STRING,
"float": vearch.dataType.FLOAT,
}
engine_info = {
"index_size": 1,
"retrieval_type": "HNSW",
"retrieval_param": {
"metric_type": "InnerProduct",
"nlinks": -1,
"efConstruction": -1,
},
}
filed_list_add = self.field_list.append({"field": "text", "type": "str"})
fields = [
vearch.GammaFieldInfo(fi["field"], type_dict[fi["type"]])
for fi in filed_list_add
]
vector_field = vearch.GammaVectorInfo(
name="text_embedding",
type=vearch.dataType.VECTOR,
is_index=True,
dimension=dim,
model_id="",
store_type="MemoryOnly",
store_param={"cache_size": 10000},
)
return self.vearch.create_table(
engine_info,
name=self.using_table_name,
fields=fields,
vector_field=vector_field,
)
def _create_space(
self,
dim: int = 1024,
) -> int:
"""
Create Cluster VectorStore space.
Args:
dim:dimension of vector.
Return:
code,0 failed for ,1 for success.
"""
type_dict = {"int": "integer", "str": "string", "float": "float"}
space_config = {
"name": self.using_table_name,
"partition_num": 1,
"replica_num": 1,
"engine": {
"index_size": 1,
"retrieval_type": "HNSW",
"retrieval_param": {
"metric_type": "InnerProduct",
"nlinks": -1,
"efConstruction": -1,
},
},
}
tmp_proer = {
"text": {"type": "string"},
"text_embedding": {
"type": "vector",
"index": True,
"dimension": dim,
"store_type": "MemoryOnly",
},
}
for item in self.field_list:
tmp_proer[item["field"]] = {"type": type_dict[item["type"]]}
space_config["properties"] = tmp_proer
return self.vearch.create_space(self.using_db_name, space_config)
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
if not self.vearch:
raise ValueError("Vearch Engine is not initialized")
embeddings = []
metadatas = []
ids = []
texts = []
for node in nodes:
embeddings.append(node.get_embedding())
metadatas.append(
node_to_metadata_dict(
node, remove_text=True, flat_metadata=self.flat_metadata
)
)
ids.append(node.node_id)
texts.append(node.get_content(metadata_mode=MetadataMode.NONE) or "")
return self._add_texts(
ids=ids,
texts=texts,
metadatas=metadatas,
embeddings=embeddings,
)
def query(
self,
query: VectorStoreQuery,
**kwargs: Any,
) -> VectorStoreQueryResult:
"""
Query index for top k most similar nodes.
Args:
query : vector store query.
Returns:
VectorStoreQueryResult: Query results.
"""
meta_filters = {}
if query.filters is not None:
for filter_ in query.filters.legacy_filters():
meta_filters[filter_.key] = filter_.value
if self.flag:
meta_field_list = self.vearch.get_space(
self.using_db_name, self.using_table_name
)
meta_field_list.remove("text_embedding")
embed = query.query_embedding
if embed is None:
raise ValueError("query.query_embedding is None")
k = query.similarity_top_k
if self.flag:
query_data = {
"query": {
"sum": [
{
"field": "text_embedding",
"feature": (embed / np.linalg.norm(embed)).tolist(),
}
],
},
"retrieval_param": {"metric_type": "InnerProduct", "efSearch": 64},
"size": k,
"fields": meta_field_list,
}
query_result = self.vearch.search(
self.using_db_name, self.using_table_name, query_data
)
res = query_result["hits"]["hits"]
else:
query_data = {
"vector": [
{
"field": "text_embedding",
"feature": embed / np.linalg.norm(embed),
}
],
"fields": [],
"retrieval_param": {"metric_type": "InnerProduct", "efSearch": 64},
"topn": k,
}
query_result = self.vearch.search(query_data)
res = query_result[0]["result_items"]
nodes = []
similarities = []
ids = []
for item in res:
content = ""
meta_data = {}
node_id = ""
if self.flag:
score = item["_score"]
item = item["_source"]
for item_key in item:
if item_key == "text":
content = item[item_key]
continue
elif item_key == "_id":
node_id = item[item_key]
ids.append(node_id)
continue
if self.flag != 1 and item_key == "score":
score = item[item_key]
continue
meta_data[item_key] = item[item_key]
similarities.append(score)
try:
node = metadata_dict_to_node(meta_data)
node.set_content(content)
except Exception:
metadata, node_info, relationships = legacy_metadata_dict_to_node(
meta_data
)
node = TextNode(
text=content,
id_=node_id,
metadata=metadata,
start_char_idx=node_info.get("start", None),
end_char_idx=node_info.get("end", None),
relationships=relationships,
)
nodes.append(node)
return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
def _delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> Optional[bool]:
"""
Delete the documents which have the specified ids.
Args:
ids: The ids of the embedding vectors.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful.
False otherwise, None if not implemented.
"""
ret: Optional[bool] = None
tmp_res = []
if ids is None or len(ids) == 0:
return ret
for _id in ids:
if self.flag:
ret = self.vearch.delete(self.using_db_name, self.using_table_name, _id)
else:
ret = self.vearch.del_doc(_id)
tmp_res.append(ret)
return all(i == 0 for i in tmp_res)
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""Delete nodes using with ref_doc_id.
Args:
ref_doc_id (str): The doc_id of the document to delete.
Returns:
None
"""
if len(ref_doc_id) == 0:
return
ids: List[str] = []
ids.append(ref_doc_id)
self._delete(ids)
| [
"llama_index.core.vector_stores.types.VectorStoreQueryResult",
"llama_index.core.vector_stores.utils.node_to_metadata_dict",
"llama_index.core.vector_stores.utils.metadata_dict_to_node",
"llama_index.core.vector_stores.utils.legacy_metadata_dict_to_node"
] | [((524, 551), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (541, 551), False, 'import logging\n'), ((8582, 8767), 'vearch.GammaVectorInfo', 'vearch.GammaVectorInfo', ([], {'name': '"""text_embedding"""', 'type': 'vearch.dataType.VECTOR', 'is_index': '(True)', 'dimension': 'dim', 'model_id': '""""""', 'store_type': '"""MemoryOnly"""', 'store_param': "{'cache_size': 10000}"}), "(name='text_embedding', type=vearch.dataType.VECTOR,\n is_index=True, dimension=dim, model_id='', store_type='MemoryOnly',\n store_param={'cache_size': 10000})\n", (8604, 8767), False, 'import vearch\n'), ((14615, 14686), 'llama_index.core.vector_stores.types.VectorStoreQueryResult', 'VectorStoreQueryResult', ([], {'nodes': 'nodes', 'similarities': 'similarities', 'ids': 'ids'}), '(nodes=nodes, similarities=similarities, ids=ids)\n', (14637, 14686), False, 'from llama_index.core.vector_stores.types import VectorStore, VectorStoreQuery, VectorStoreQueryResult\n'), ((2180, 2221), 'vearch_cluster.VearchCluster', 'vearch_cluster.VearchCluster', (['path_or_url'], {}), '(path_or_url)\n', (2208, 2221), False, 'import vearch_cluster\n'), ((2512, 2546), 'os.path.join', 'os.path.join', (['metadata_path', '"""log"""'], {}), "(metadata_path, 'log')\n", (2524, 2546), False, 'import os\n'), ((2655, 2693), 'vearch.Engine', 'vearch.Engine', (['metadata_path', 'log_path'], {}), '(metadata_path, log_path)\n', (2668, 2693), False, 'import vearch\n'), ((6216, 6284), 'os.path.join', 'os.path.join', (['self.using_metapath', "(self.using_table_name + '.schema')"], {}), "(self.using_metapath, self.using_table_name + '.schema')\n", (6228, 6284), False, 'import os\n'), ((8454, 8511), 'vearch.GammaFieldInfo', 'vearch.GammaFieldInfo', (["fi['field']", "type_dict[fi['type']]"], {}), "(fi['field'], type_dict[fi['type']])\n", (8475, 8511), False, 'import vearch\n'), ((2416, 2444), 'os.path.isdir', 'os.path.isdir', (['metadata_path'], {}), '(metadata_path)\n', (2429, 2444), False, 'import os\n'), ((2462, 2488), 'os.makedirs', 'os.makedirs', (['metadata_path'], {}), '(metadata_path)\n', (2473, 2488), False, 'import os\n'), ((2566, 2589), 'os.path.isdir', 'os.path.isdir', (['log_path'], {}), '(log_path)\n', (2579, 2589), False, 'import os\n'), ((2607, 2628), 'os.makedirs', 'os.makedirs', (['log_path'], {}), '(log_path)\n', (2618, 2628), False, 'import os\n'), ((6334, 6360), 'os.path.exists', 'os.path.exists', (['table_path'], {}), '(table_path)\n', (6348, 6360), False, 'import os\n'), ((10709, 10788), 'llama_index.core.vector_stores.utils.node_to_metadata_dict', 'node_to_metadata_dict', (['node'], {'remove_text': '(True)', 'flat_metadata': 'self.flat_metadata'}), '(node, remove_text=True, flat_metadata=self.flat_metadata)\n', (10730, 10788), False, 'from llama_index.core.vector_stores.utils import legacy_metadata_dict_to_node, metadata_dict_to_node, node_to_metadata_dict\n'), ((14001, 14033), 'llama_index.core.vector_stores.utils.metadata_dict_to_node', 'metadata_dict_to_node', (['meta_data'], {}), '(meta_data)\n', (14022, 14033), False, 'from llama_index.core.vector_stores.utils import legacy_metadata_dict_to_node, metadata_dict_to_node, node_to_metadata_dict\n'), ((5418, 5433), 'numpy.array', 'np.array', (['embed'], {}), '(embed)\n', (5426, 5433), True, 'import numpy as np\n'), ((7132, 7147), 'numpy.array', 'np.array', (['embed'], {}), '(embed)\n', (7140, 7147), True, 'import numpy as np\n'), ((7435, 7450), 'time.sleep', 'time.sleep', (['(0.5)'], {}), '(0.5)\n', (7445, 7450), False, 'import time\n'), ((14159, 14198), 'llama_index.core.vector_stores.utils.legacy_metadata_dict_to_node', 'legacy_metadata_dict_to_node', (['meta_data'], {}), '(meta_data)\n', (14187, 14198), False, 'from llama_index.core.vector_stores.utils import legacy_metadata_dict_to_node, metadata_dict_to_node, node_to_metadata_dict\n'), ((2304, 2315), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (2313, 2315), False, 'import os\n'), ((7210, 7234), 'numpy.linalg.norm', 'np.linalg.norm', (['embed_np'], {}), '(embed_np)\n', (7224, 7234), True, 'import numpy as np\n'), ((2879, 2891), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (2889, 2891), False, 'import uuid\n'), ((12842, 12863), 'numpy.linalg.norm', 'np.linalg.norm', (['embed'], {}), '(embed)\n', (12856, 12863), True, 'import numpy as np\n'), ((2049, 2061), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (2059, 2061), False, 'import uuid\n'), ((5532, 5556), 'numpy.linalg.norm', 'np.linalg.norm', (['embed_np'], {}), '(embed_np)\n', (5546, 5556), True, 'import numpy as np\n'), ((12210, 12231), 'numpy.linalg.norm', 'np.linalg.norm', (['embed'], {}), '(embed)\n', (12224, 12231), True, 'import numpy as np\n')] |
# ENTER YOUR OPENAPI KEY IN OPENAI_API_KEY ENV VAR FIRST
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, download_loader
savePath = f'/{os.path.dirname(__file__)}/indexes/index.json'
#
# index = GPTSimpleVectorIndex(documents)#, llm_predictor=llm_predictor)
index = GPTSimpleVectorIndex.load_from_disk(savePath)
response = index.query("Summarize the vulnerability CVE-2021-23406", response_mode="tree_summarize")
print(response)
print('Sources are ', response.get_formatted_sources())
| [
"llama_index.GPTSimpleVectorIndex.load_from_disk"
] | [((303, 348), 'llama_index.GPTSimpleVectorIndex.load_from_disk', 'GPTSimpleVectorIndex.load_from_disk', (['savePath'], {}), '(savePath)\n', (338, 348), False, 'from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, download_loader\n')] |
from typing import Optional, Union
from llama_index import ServiceContext
from llama_index.callbacks import CallbackManager
from llama_index.embeddings.utils import EmbedType
from llama_index.extractors import (
EntityExtractor,
KeywordExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
TitleExtractor,
)
from llama_index.llms.utils import LLMType
from llama_index.prompts import PromptTemplate
from llama_index.prompts.base import BasePromptTemplate
from llama_index.text_splitter import SentenceSplitter
from autollm.callbacks.cost_calculating import CostCalculatingHandler
from autollm.utils.llm_utils import set_default_prompt_template
class AutoServiceContext:
"""AutoServiceContext extends the functionality of LlamaIndex's ServiceContext to include token
counting.
"""
@staticmethod
def from_defaults(
llm: Optional[LLMType] = "default",
embed_model: Optional[EmbedType] = "default",
system_prompt: str = None,
query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
enable_cost_calculator: bool = False,
chunk_size: Optional[int] = 512,
chunk_overlap: Optional[int] = 100,
context_window: Optional[int] = None,
enable_title_extractor: bool = False,
enable_summary_extractor: bool = False,
enable_qa_extractor: bool = False,
enable_keyword_extractor: bool = False,
enable_entity_extractor: bool = False,
**kwargs) -> ServiceContext:
"""
Create a ServiceContext with default parameters with extended enable_token_counting functionality. If
enable_token_counting is True, tracks the number of tokens used by the LLM for each query.
Parameters:
llm (LLM): The LLM to use for the query engine. Defaults to gpt-3.5-turbo.
embed_model (BaseEmbedding): The embedding model to use for the query engine. Defaults to OpenAIEmbedding.
system_prompt (str): The system prompt to use for the query engine.
query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine.
enable_cost_calculator (bool): Flag to enable cost calculator logging.
chunk_size (int): The token chunk size for each chunk.
chunk_overlap (int): The token overlap between each chunk.
context_window (int): The maximum context size that will get sent to the LLM.
enable_title_extractor (bool): Flag to enable title extractor.
enable_summary_extractor (bool): Flag to enable summary extractor.
enable_qa_extractor (bool): Flag to enable question answering extractor.
enable_keyword_extractor (bool): Flag to enable keyword extractor.
enable_entity_extractor (bool): Flag to enable entity extractor.
**kwargs: Arbitrary keyword arguments.
Returns:
ServiceContext: The initialized ServiceContext from default parameters with extra token counting functionality.
"""
if not system_prompt and not query_wrapper_prompt:
system_prompt, query_wrapper_prompt = set_default_prompt_template()
# Convert query_wrapper_prompt to PromptTemplate if it is a string
if isinstance(query_wrapper_prompt, str):
query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt)
callback_manager: CallbackManager = kwargs.get('callback_manager', CallbackManager())
kwargs.pop(
'callback_manager', None) # Make sure callback_manager is not passed to ServiceContext twice
if enable_cost_calculator:
llm_model_name = llm.metadata.model_name if not "default" else "gpt-3.5-turbo"
callback_manager.add_handler(CostCalculatingHandler(model_name=llm_model_name, verbose=True))
sentence_splitter = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
transformations = [sentence_splitter]
if enable_entity_extractor:
transformations.append(EntityExtractor())
if enable_keyword_extractor:
transformations.append(KeywordExtractor(llm=llm, keywords=5))
if enable_summary_extractor:
transformations.append(SummaryExtractor(llm=llm, summaries=["prev", "self"]))
if enable_title_extractor:
transformations.append(TitleExtractor(llm=llm, nodes=5))
if enable_qa_extractor:
transformations.append(QuestionsAnsweredExtractor(llm=llm, questions=5))
service_context = ServiceContext.from_defaults(
llm=llm,
embed_model=embed_model,
transformations=transformations,
system_prompt=system_prompt,
query_wrapper_prompt=query_wrapper_prompt,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
context_window=context_window,
callback_manager=callback_manager,
**kwargs)
return service_context
| [
"llama_index.extractors.TitleExtractor",
"llama_index.extractors.QuestionsAnsweredExtractor",
"llama_index.ServiceContext.from_defaults",
"llama_index.prompts.PromptTemplate",
"llama_index.extractors.SummaryExtractor",
"llama_index.extractors.EntityExtractor",
"llama_index.extractors.KeywordExtractor",
"llama_index.callbacks.CallbackManager",
"llama_index.text_splitter.SentenceSplitter"
] | [((3952, 4020), 'llama_index.text_splitter.SentenceSplitter', 'SentenceSplitter', ([], {'chunk_size': 'chunk_size', 'chunk_overlap': 'chunk_overlap'}), '(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n', (3968, 4020), False, 'from llama_index.text_splitter import SentenceSplitter\n'), ((4643, 4954), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm': 'llm', 'embed_model': 'embed_model', 'transformations': 'transformations', 'system_prompt': 'system_prompt', 'query_wrapper_prompt': 'query_wrapper_prompt', 'chunk_size': 'chunk_size', 'chunk_overlap': 'chunk_overlap', 'context_window': 'context_window', 'callback_manager': 'callback_manager'}), '(llm=llm, embed_model=embed_model,\n transformations=transformations, system_prompt=system_prompt,\n query_wrapper_prompt=query_wrapper_prompt, chunk_size=chunk_size,\n chunk_overlap=chunk_overlap, context_window=context_window,\n callback_manager=callback_manager, **kwargs)\n', (4671, 4954), False, 'from llama_index import ServiceContext\n'), ((3233, 3262), 'autollm.utils.llm_utils.set_default_prompt_template', 'set_default_prompt_template', ([], {}), '()\n', (3260, 3262), False, 'from autollm.utils.llm_utils import set_default_prompt_template\n'), ((3423, 3468), 'llama_index.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': 'query_wrapper_prompt'}), '(template=query_wrapper_prompt)\n', (3437, 3468), False, 'from llama_index.prompts import PromptTemplate\n'), ((3545, 3562), 'llama_index.callbacks.CallbackManager', 'CallbackManager', ([], {}), '()\n', (3560, 3562), False, 'from llama_index.callbacks import CallbackManager\n'), ((3858, 3921), 'autollm.callbacks.cost_calculating.CostCalculatingHandler', 'CostCalculatingHandler', ([], {'model_name': 'llm_model_name', 'verbose': '(True)'}), '(model_name=llm_model_name, verbose=True)\n', (3880, 3921), False, 'from autollm.callbacks.cost_calculating import CostCalculatingHandler\n'), ((4138, 4155), 'llama_index.extractors.EntityExtractor', 'EntityExtractor', ([], {}), '()\n', (4153, 4155), False, 'from llama_index.extractors import EntityExtractor, KeywordExtractor, QuestionsAnsweredExtractor, SummaryExtractor, TitleExtractor\n'), ((4229, 4266), 'llama_index.extractors.KeywordExtractor', 'KeywordExtractor', ([], {'llm': 'llm', 'keywords': '(5)'}), '(llm=llm, keywords=5)\n', (4245, 4266), False, 'from llama_index.extractors import EntityExtractor, KeywordExtractor, QuestionsAnsweredExtractor, SummaryExtractor, TitleExtractor\n'), ((4340, 4393), 'llama_index.extractors.SummaryExtractor', 'SummaryExtractor', ([], {'llm': 'llm', 'summaries': "['prev', 'self']"}), "(llm=llm, summaries=['prev', 'self'])\n", (4356, 4393), False, 'from llama_index.extractors import EntityExtractor, KeywordExtractor, QuestionsAnsweredExtractor, SummaryExtractor, TitleExtractor\n'), ((4465, 4497), 'llama_index.extractors.TitleExtractor', 'TitleExtractor', ([], {'llm': 'llm', 'nodes': '(5)'}), '(llm=llm, nodes=5)\n', (4479, 4497), False, 'from llama_index.extractors import EntityExtractor, KeywordExtractor, QuestionsAnsweredExtractor, SummaryExtractor, TitleExtractor\n'), ((4566, 4614), 'llama_index.extractors.QuestionsAnsweredExtractor', 'QuestionsAnsweredExtractor', ([], {'llm': 'llm', 'questions': '(5)'}), '(llm=llm, questions=5)\n', (4592, 4614), False, 'from llama_index.extractors import EntityExtractor, KeywordExtractor, QuestionsAnsweredExtractor, SummaryExtractor, TitleExtractor\n')] |
from rag.agents.interface import Pipeline
from llama_index.core.program import LLMTextCompletionProgram
import json
from llama_index.llms.ollama import Ollama
from typing import List
from pydantic import create_model
from rich.progress import Progress, SpinnerColumn, TextColumn
import requests
import warnings
import box
import yaml
import timeit
from rich import print
from typing import Any
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
# Import config vars
with open('config.yml', 'r', encoding='utf8') as ymlfile:
cfg = box.Box(yaml.safe_load(ymlfile))
class VProcessorPipeline(Pipeline):
def run_pipeline(self,
payload: str,
query_inputs: [str],
query_types: [str],
query: str,
file_path: str,
index_name: str,
debug: bool = False,
local: bool = True) -> Any:
print(f"\nRunning pipeline with {payload}\n")
start = timeit.default_timer()
if file_path is None:
raise ValueError("File path is required for vprocessor pipeline")
with open(file_path, "rb") as file:
files = {'file': (file_path, file, 'image/jpeg')}
data = {
'image_url': ''
}
response = self.invoke_pipeline_step(lambda: requests.post(cfg.VPROCESSOR_OCR_ENDPOINT,
data=data,
files=files,
timeout=180),
"Running OCR...",
local)
if response.status_code != 200:
print('Request failed with status code:', response.status_code)
print('Response:', response.text)
return "Failed to process file. Please try again."
end = timeit.default_timer()
print(f"Time to run OCR: {end - start}")
start = timeit.default_timer()
data = response.json()
ResponseModel = self.invoke_pipeline_step(lambda: self.build_response_class(query_inputs, query_types),
"Building dynamic response class...",
local)
prompt_template_str = """\
""" + query + """\
using this structured data, coming from OCR {document_data}.\
"""
llm_ollama = self.invoke_pipeline_step(lambda: Ollama(model=cfg.LLM_VPROCESSOR,
base_url=cfg.OLLAMA_BASE_URL_VPROCESSOR,
temperature=0,
request_timeout=900),
"Loading Ollama...",
local)
program = LLMTextCompletionProgram.from_defaults(
output_cls=ResponseModel,
prompt_template_str=prompt_template_str,
llm=llm_ollama,
verbose=True,
)
output = self.invoke_pipeline_step(lambda: program(document_data=data),
"Running inference...",
local)
answer = self.beautify_json(output.model_dump_json())
end = timeit.default_timer()
print(f"\nJSON response:\n")
print(answer + '\n')
print('=' * 50)
print(f"Time to retrieve answer: {end - start}")
return answer
def prepare_files(self, file_path, file):
if file_path is not None:
with open(file_path, "rb") as file:
files = {'file': (file_path, file, 'image/jpeg')}
data = {
'image_url': ''
}
else:
files = {'file': (file.filename, file.file, file.content_type)}
data = {
'image_url': ''
}
return data, files
# Function to safely evaluate type strings
def safe_eval_type(self, type_str, context):
try:
return eval(type_str, {}, context)
except NameError:
raise ValueError(f"Type '{type_str}' is not recognized")
def build_response_class(self, query_inputs, query_types_as_strings):
# Controlled context for eval
context = {
'List': List,
'str': str,
'int': int,
'float': float
# Include other necessary types or typing constructs here
}
# Convert string representations to actual types
query_types = [self.safe_eval_type(type_str, context) for type_str in query_types_as_strings]
# Create fields dictionary
fields = {name: (type_, ...) for name, type_ in zip(query_inputs, query_types)}
DynamicModel = create_model('DynamicModel', **fields)
return DynamicModel
def invoke_pipeline_step(self, task_call, task_description, local):
if local:
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
transient=False,
) as progress:
progress.add_task(description=task_description, total=None)
ret = task_call()
else:
print(task_description)
ret = task_call()
return ret
def beautify_json(self, result):
try:
# Convert and pretty print
data = json.loads(str(result))
data = json.dumps(data, indent=4)
return data
except (json.decoder.JSONDecodeError, TypeError):
print("The response is not in JSON format:\n")
print(result)
return {} | [
"llama_index.core.program.LLMTextCompletionProgram.from_defaults",
"llama_index.llms.ollama.Ollama"
] | [((396, 458), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'DeprecationWarning'}), "('ignore', category=DeprecationWarning)\n", (419, 458), False, 'import warnings\n'), ((459, 514), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'UserWarning'}), "('ignore', category=UserWarning)\n", (482, 514), False, 'import warnings\n'), ((614, 637), 'yaml.safe_load', 'yaml.safe_load', (['ymlfile'], {}), '(ymlfile)\n', (628, 637), False, 'import yaml\n'), ((1029, 1076), 'rich.print', 'print', (['f"""\nRunning pipeline with {payload}\n"""'], {}), '(f"""\nRunning pipeline with {payload}\n""")\n', (1034, 1076), False, 'from rich import print\n'), ((1092, 1114), 'timeit.default_timer', 'timeit.default_timer', ([], {}), '()\n', (1112, 1114), False, 'import timeit\n'), ((2116, 2138), 'timeit.default_timer', 'timeit.default_timer', ([], {}), '()\n', (2136, 2138), False, 'import timeit\n'), ((2147, 2187), 'rich.print', 'print', (['f"""Time to run OCR: {end - start}"""'], {}), "(f'Time to run OCR: {end - start}')\n", (2152, 2187), False, 'from rich import print\n'), ((2205, 2227), 'timeit.default_timer', 'timeit.default_timer', ([], {}), '()\n', (2225, 2227), False, 'import timeit\n'), ((3157, 3296), 'llama_index.core.program.LLMTextCompletionProgram.from_defaults', 'LLMTextCompletionProgram.from_defaults', ([], {'output_cls': 'ResponseModel', 'prompt_template_str': 'prompt_template_str', 'llm': 'llm_ollama', 'verbose': '(True)'}), '(output_cls=ResponseModel,\n prompt_template_str=prompt_template_str, llm=llm_ollama, verbose=True)\n', (3195, 3296), False, 'from llama_index.core.program import LLMTextCompletionProgram\n'), ((3628, 3650), 'timeit.default_timer', 'timeit.default_timer', ([], {}), '()\n', (3648, 3650), False, 'import timeit\n'), ((3660, 3690), 'rich.print', 'print', (['f"""\nJSON response:\n"""'], {}), '(f"""\nJSON response:\n""")\n', (3665, 3690), False, 'from rich import print\n'), ((3697, 3717), 'rich.print', 'print', (["(answer + '\\n')"], {}), "(answer + '\\n')\n", (3702, 3717), False, 'from rich import print\n'), ((3726, 3741), 'rich.print', 'print', (["('=' * 50)"], {}), "('=' * 50)\n", (3731, 3741), False, 'from rich import print\n'), ((3751, 3799), 'rich.print', 'print', (['f"""Time to retrieve answer: {end - start}"""'], {}), "(f'Time to retrieve answer: {end - start}')\n", (3756, 3799), False, 'from rich import print\n'), ((5158, 5196), 'pydantic.create_model', 'create_model', (['"""DynamicModel"""'], {}), "('DynamicModel', **fields)\n", (5170, 5196), False, 'from pydantic import create_model\n'), ((1927, 1990), 'rich.print', 'print', (['"""Request failed with status code:"""', 'response.status_code'], {}), "('Request failed with status code:', response.status_code)\n", (1932, 1990), False, 'from rich import print\n'), ((2003, 2036), 'rich.print', 'print', (['"""Response:"""', 'response.text'], {}), "('Response:', response.text)\n", (2008, 2036), False, 'from rich import print\n'), ((5657, 5680), 'rich.print', 'print', (['task_description'], {}), '(task_description)\n', (5662, 5680), False, 'from rich import print\n'), ((5884, 5910), 'json.dumps', 'json.dumps', (['data'], {'indent': '(4)'}), '(data, indent=4)\n', (5894, 5910), False, 'import json\n'), ((2719, 2832), 'llama_index.llms.ollama.Ollama', 'Ollama', ([], {'model': 'cfg.LLM_VPROCESSOR', 'base_url': 'cfg.OLLAMA_BASE_URL_VPROCESSOR', 'temperature': '(0)', 'request_timeout': '(900)'}), '(model=cfg.LLM_VPROCESSOR, base_url=cfg.OLLAMA_BASE_URL_VPROCESSOR,\n temperature=0, request_timeout=900)\n', (2725, 2832), False, 'from llama_index.llms.ollama import Ollama\n'), ((6005, 6051), 'rich.print', 'print', (['"""The response is not in JSON format:\n"""'], {}), "('The response is not in JSON format:\\n')\n", (6010, 6051), False, 'from rich import print\n'), ((6064, 6077), 'rich.print', 'print', (['result'], {}), '(result)\n', (6069, 6077), False, 'from rich import print\n'), ((1457, 1536), 'requests.post', 'requests.post', (['cfg.VPROCESSOR_OCR_ENDPOINT'], {'data': 'data', 'files': 'files', 'timeout': '(180)'}), '(cfg.VPROCESSOR_OCR_ENDPOINT, data=data, files=files, timeout=180)\n', (1470, 1536), False, 'import requests\n'), ((5364, 5379), 'rich.progress.SpinnerColumn', 'SpinnerColumn', ([], {}), '()\n', (5377, 5379), False, 'from rich.progress import Progress, SpinnerColumn, TextColumn\n'), ((5401, 5455), 'rich.progress.TextColumn', 'TextColumn', (['"""[progress.description]{task.description}"""'], {}), "('[progress.description]{task.description}')\n", (5411, 5455), False, 'from rich.progress import Progress, SpinnerColumn, TextColumn\n')] |
import asyncio
import chromadb
import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from traceloop.sdk import Traceloop
os.environ["TOKENIZERS_PARALLELISM"] = "false"
Traceloop.init(app_name="llama_index_example")
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
# define embedding function
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
# set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, embed_model=embed_model
)
async def main():
# Query Data
query_engine = index.as_query_engine()
response = await query_engine.aquery("What did the author do growing up?")
print(response)
if __name__ == "__main__":
asyncio.run(main())
| [
"llama_index.core.StorageContext.from_defaults",
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.core.VectorStoreIndex.from_documents",
"llama_index.vector_stores.chroma.ChromaVectorStore",
"llama_index.core.SimpleDirectoryReader"
] | [((344, 390), 'traceloop.sdk.Traceloop.init', 'Traceloop.init', ([], {'app_name': '"""llama_index_example"""'}), "(app_name='llama_index_example')\n", (358, 390), False, 'from traceloop.sdk import Traceloop\n'), ((408, 434), 'chromadb.EphemeralClient', 'chromadb.EphemeralClient', ([], {}), '()\n', (432, 434), False, 'import chromadb\n'), ((544, 600), 'llama_index.embeddings.huggingface.HuggingFaceEmbedding', 'HuggingFaceEmbedding', ([], {'model_name': '"""BAAI/bge-base-en-v1.5"""'}), "(model_name='BAAI/bge-base-en-v1.5')\n", (564, 600), False, 'from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n'), ((748, 802), 'llama_index.vector_stores.chroma.ChromaVectorStore', 'ChromaVectorStore', ([], {'chroma_collection': 'chroma_collection'}), '(chroma_collection=chroma_collection)\n', (765, 802), False, 'from llama_index.vector_stores.chroma import ChromaVectorStore\n'), ((821, 876), 'llama_index.core.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'vector_store': 'vector_store'}), '(vector_store=vector_store)\n', (849, 876), False, 'from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext\n'), ((885, 989), 'llama_index.core.VectorStoreIndex.from_documents', 'VectorStoreIndex.from_documents', (['documents'], {'storage_context': 'storage_context', 'embed_model': 'embed_model'}), '(documents, storage_context=storage_context,\n embed_model=embed_model)\n', (916, 989), False, 'from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext\n'), ((631, 675), 'llama_index.core.SimpleDirectoryReader', 'SimpleDirectoryReader', (['"""./data/paul_graham/"""'], {}), "('./data/paul_graham/')\n", (652, 675), False, 'from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext\n')] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ================================================== #
# This file is a part of PYGPT package #
# Website: https://pygpt.net #
# GitHub: https://github.com/szczyglis-dev/py-gpt #
# MIT License #
# Created By : Marcin Szczygliński #
# Updated Date: 2024.03.11 01:00:00 #
# ================================================== #
import os.path
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.core.indices.base import BaseIndex
from llama_index.core.indices.service_context import ServiceContext
from .base import BaseStore
class TempProvider(BaseStore):
def __init__(self, *args, **kwargs):
super(TempProvider, self).__init__(*args, **kwargs)
"""
Temporary vector store provider
:param args: args
:param kwargs: kwargs
"""
self.window = kwargs.get('window', None)
self.id = "TempVectorStore"
self.prefix = "" # prefix for index directory
self.indexes = {}
self.persist = False
def count(self) -> int:
"""
Count indexes
:return: number of indexes
"""
return len(self.indexes)
def get_path(self, id: str) -> str:
"""
Get database path
:param id: index name
:return: database path
"""
if not self.persist:
return ""
tmp_dir = os.path.join(
self.window.core.config.get_user_dir('idx'),
"_tmp", # temp directory
)
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir, exist_ok=True)
path = os.path.join(
self.window.core.config.get_user_dir('idx'),
"_tmp", # temp directory
self.prefix + id,
)
return path
def exists(self, id: str = None) -> bool:
"""
Check if index with id exists
:param id: index name
:return: True if exists
"""
if not self.persist:
if id in self.indexes:
return True
return False
path = self.get_path(id)
if os.path.exists(path):
store = os.path.join(path, "docstore.json")
if os.path.exists(store):
return True
return False
def create(self, id: str):
"""
Create empty index
:param id: index name
"""
if self.persist:
path = self.get_path(id)
if not os.path.exists(path):
index = self.index_from_empty() # create empty index
self.store(
id=id,
index=index,
)
else:
self.indexes[id] = self.index_from_empty()
def get(self, id: str, service_context: ServiceContext = None) -> BaseIndex:
"""
Get index
:param id: tmp idx id
:param service_context: Service context
:return: index instance
"""
if not self.exists(id):
self.create(id)
path = self.get_path(id)
if self.persist:
storage_context = StorageContext.from_defaults(
persist_dir=path,
)
self.indexes[id] = load_index_from_storage(
storage_context,
service_context=service_context,
)
return self.indexes[id]
def store(self, id: str, index: BaseIndex = None):
"""
Store index
:param id: index name
:param index: index instance
"""
if not self.persist:
self.indexes[id] = index
return
if index is None:
index = self.indexes[id]
path = self.get_path(id)
index.storage_context.persist(
persist_dir=path,
)
self.indexes[id] = index
def clean(self, id: str):
"""
Clean index
:param id: index name
"""
if not self.persist:
if id in self.indexes:
del self.indexes[id]
return
path = self.get_path(id)
if os.path.exists(path):
os.remove(path)
| [
"llama_index.core.StorageContext.from_defaults",
"llama_index.core.load_index_from_storage"
] | [((3275, 3321), 'llama_index.core.StorageContext.from_defaults', 'StorageContext.from_defaults', ([], {'persist_dir': 'path'}), '(persist_dir=path)\n', (3303, 3321), False, 'from llama_index.core import StorageContext, load_index_from_storage\n'), ((3384, 3457), 'llama_index.core.load_index_from_storage', 'load_index_from_storage', (['storage_context'], {'service_context': 'service_context'}), '(storage_context, service_context=service_context)\n', (3407, 3457), False, 'from llama_index.core import StorageContext, load_index_from_storage\n')] |
import streamlit as st
from sqlalchemy import create_engine, inspect, text
from typing import Dict, Any
from llama_index import (
VectorStoreIndex,
ServiceContext,
download_loader,
)
from llama_index.llama_pack.base import BaseLlamaPack
from llama_index.llms import OpenAI
import openai
import os
import pandas as pd
from llama_index.llms.palm import PaLM
from llama_index import (
SimpleDirectoryReader,
ServiceContext,
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
import sqlite3
from llama_index import SQLDatabase, ServiceContext
from llama_index.indices.struct_store import NLSQLTableQueryEngine
os.environ['OPENAI_API_KEY'] = st.secrets['OPENAI_API_KEY']
class StreamlitChatPack(BaseLlamaPack):
def __init__(
self,
page: str = "Natural Language to SQL Query",
run_from_main: bool = False,
**kwargs: Any,
) -> None:
"""Init params."""
self.page = page
def get_modules(self) -> Dict[str, Any]:
"""Get modules."""
return {}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
import streamlit as st
st.set_page_config(
page_title=f"{self.page}",
layout="centered",
initial_sidebar_state="auto",
menu_items=None,
)
if "messages" not in st.session_state: # Initialize the chat messages history
st.session_state["messages"] = [
{"role": "assistant", "content": f"Hello. Ask me anything related to the database."}
]
st.title(
f"{self.page}💬"
)
st.info(
f"Explore Snowflake views with this AI-powered app. Pose any question and receive exact SQL queries.",
icon="ℹ️",
)
def add_to_message_history(role, content):
message = {"role": role, "content": str(content)}
st.session_state["messages"].append(
message
) # Add response to message history
def get_table_data(table_name, conn):
query = f"SELECT * FROM {table_name}"
df = pd.read_sql_query(query, conn)
return df
@st.cache_resource
def load_db_llm():
# Load the SQLite database
#engine = create_engine("sqlite:///ecommerce_platform1.db")
engine = create_engine("sqlite:///ecommerce_platform1.db?mode=ro", connect_args={"uri": True})
sql_database = SQLDatabase(engine) #include all tables
# Initialize LLM
#llm2 = PaLM(api_key=os.environ["GOOGLE_API_KEY"]) # Replace with your API key
llm2 = OpenAI(temperature=0.1, model="gpt-3.5-turbo-1106")
service_context = ServiceContext.from_defaults(llm=llm2, embed_model="local")
return sql_database, service_context, engine
sql_database, service_context, engine = load_db_llm()
# Sidebar for database schema viewer
st.sidebar.markdown("## Database Schema Viewer")
# Create an inspector object
inspector = inspect(engine)
# Get list of tables in the database
table_names = inspector.get_table_names()
# Sidebar selection for tables
selected_table = st.sidebar.selectbox("Select a Table", table_names)
db_file = 'ecommerce_platform1.db'
conn = sqlite3.connect(db_file)
# Display the selected table
if selected_table:
df = get_table_data(selected_table, conn)
st.sidebar.text(f"Data for table '{selected_table}':")
st.sidebar.dataframe(df)
# Close the connection
conn.close()
# Sidebar Intro
st.sidebar.markdown('## App Created By')
st.sidebar.markdown("""
Harshad Suryawanshi:
[Linkedin](https://www.linkedin.com/in/harshadsuryawanshi/), [Medium](https://harshadsuryawanshi.medium.com/), [X](https://twitter.com/HarshadSurya1c)
""")
st.sidebar.markdown('## Other Projects')
st.sidebar.markdown("""
- [Pokemon Go! Inspired AInimal GO! - Multimodal RAG App](https://www.linkedin.com/posts/harshadsuryawanshi_llamaindex-ai-deeplearning-activity-7134632983495327744-M7yy)
- [Building My Own GPT4-V with PaLM and Kosmos](https://lnkd.in/dawgKZBP)
- [AI Equity Research Analyst](https://ai-eqty-rsrch-anlyst.streamlit.app/)
- [Recasting "The Office" Scene](https://blackmirroroffice.streamlit.app/)
- [Story Generator](https://appstorycombined-agaf9j4ceit.streamlit.app/)
""")
st.sidebar.markdown('## Disclaimer')
st.sidebar.markdown("""This application is for demonstration purposes only and may not cover all aspects of real-world data complexities. Please use it as a guide and not as a definitive source for decision-making.""")
if "query_engine" not in st.session_state: # Initialize the query engine
st.session_state["query_engine"] = NLSQLTableQueryEngine(
sql_database=sql_database,
synthesize_response=True,
service_context=service_context
)
for message in st.session_state["messages"]: # Display the prior chat messages
with st.chat_message(message["role"]):
st.write(message["content"])
if prompt := st.chat_input(
"Enter your natural language query about the database"
): # Prompt for user input and save to chat history
with st.chat_message("user"):
st.write(prompt)
add_to_message_history("user", prompt)
# If last message is not from assistant, generate a new response
if st.session_state["messages"][-1]["role"] != "assistant":
with st.spinner():
with st.chat_message("assistant"):
response = st.session_state["query_engine"].query("User Question:"+prompt+". ")
sql_query = f"```sql\n{response.metadata['sql_query']}\n```\n**Response:**\n{response.response}\n"
response_container = st.empty()
response_container.write(sql_query)
# st.write(response.response)
add_to_message_history("assistant", sql_query)
if __name__ == "__main__":
StreamlitChatPack(run_from_main=True).run()
| [
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"llama_index.ServiceContext.from_defaults",
"llama_index.indices.struct_store.NLSQLTableQueryEngine",
"llama_index.SQLDatabase"
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Inspired AInimal GO! - Multimodal RAG App](https://www.linkedin.com/posts/harshadsuryawanshi_llamaindex-ai-deeplearning-activity-7134632983495327744-M7yy)\n - [Building My Own GPT4-V with PaLM and Kosmos](https://lnkd.in/dawgKZBP)\n - [AI Equity Research Analyst](https://ai-eqty-rsrch-anlyst.streamlit.app/)\n - [Recasting "The Office" Scene](https://blackmirroroffice.streamlit.app/)\n - [Story Generator](https://appstorycombined-agaf9j4ceit.streamlit.app/)\n """'], {}), '(\n """\n - [Pokemon Go! Inspired AInimal GO! - Multimodal RAG App](https://www.linkedin.com/posts/harshadsuryawanshi_llamaindex-ai-deeplearning-activity-7134632983495327744-M7yy)\n - [Building My Own GPT4-V with PaLM and Kosmos](https://lnkd.in/dawgKZBP)\n - [AI Equity Research Analyst](https://ai-eqty-rsrch-anlyst.streamlit.app/)\n - [Recasting "The Office" Scene](https://blackmirroroffice.streamlit.app/)\n - [Story Generator](https://appstorycombined-agaf9j4ceit.streamlit.app/)\n """\n )\n', (4172, 4707), True, 'import streamlit as st\n'), ((4715, 4751), 'streamlit.sidebar.markdown', 'st.sidebar.markdown', (['"""## Disclaimer"""'], {}), "('## Disclaimer')\n", (4734, 4751), True, 'import streamlit as st\n'), ((4760, 4984), 'streamlit.sidebar.markdown', 'st.sidebar.markdown', (['"""This application is for demonstration purposes only and may not cover all aspects of real-world data complexities. 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