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from typing import Literal
from llama_index.schema import NodeWithScore
from pydantic import BaseModel, Field
from app.server.ingest.schemas import IngestedDoc
class Chunk(BaseModel):
object: Literal["context.chunk"]
score: float = Field(examples=[0.023])
document: IngestedDoc
text: str = Field(examples=["Outbound sales increased 20%, driven by new leads."])
previous_texts: list[str] | None = Field(
default=None,
examples=[["SALES REPORT 2023", "Inbound didn't show major changes."]],
)
next_texts: list[str] | None = Field(
default=None,
examples=[
[
"New leads came from Google Ads campaign.",
"The campaign was run by the Marketing Department",
]
],
)
@classmethod
def from_node(cls: type["Chunk"], node: NodeWithScore) -> "Chunk":
doc_id = node.node.ref_doc_id if node.node.ref_doc_id is not None else "-"
return cls(
object="context.chunk",
score=node.score or 0.0,
document=IngestedDoc(
object="ingest.document",
doc_id=doc_id,
doc_metadata=node.metadata,
),
text=node.get_content(),
)
class Completion(BaseModel):
response: str
sources: list[Chunk] | None = None
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