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import os
import random
from functools import cache
from operator import itemgetter
import langsmith
from langchain.memory import ConversationBufferWindowMemory
from langchain.retrievers import EnsembleRetriever
from langchain_community.document_transformers import LongContextReorder
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda
from langchain_openai.chat_models import ChatOpenAI
from .prompt_template import generate_prompt_template
from .retrievers_setup import (DenseRetrieverClient, SparseRetrieverClient,
compression_retriever_setup)
# Helpers
def reorder_documents(docs: list[Document]) -> list[Document]:
"""Long-Context Reorder: No matter the architecture of the model, there is
a performance degradation when we include 10+ retrieved documents.
Args:
docs (list): List of Langchain documents
Returns:
list: Reordered list of Langchain documents
"""
reorder = LongContextReorder()
return reorder.transform_documents(docs)
def randomize_documents(documents: list[Document]) -> list[Document]:
"""Randomize the documents as an attempt to randomize the model recommendations."""
random.shuffle(documents)
return documents
def format_practitioners_docs(docs: list[Document]) -> str:
"""Format the practitioners_db Documents to markdown.
Args:
docs (list[Documents]): List of Langchain documents
Returns:
docs (str):
"""
return f"\n{'-' * 3}\n".join(
[f"- Practitioner #{i+1}:\n\n\t" +
d.page_content for i, d in enumerate(docs)]
)
def format_tall_tree_docs(docs: list[Document]) -> str:
"""Format the tall_tree_db Documents to markdown.
Args:
docs (list[Documents]): List of Langchain documents
Returns:
docs (str):
"""
return f"\n{'-' * 3}\n".join(
[f"- No. {i+1}:\n\n\t" +
d.page_content for i, d in enumerate(docs)]
)
def create_langsmith_client():
"""Create a Langsmith client."""
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "talltree-ai-assistant"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
langsmith_api_key = os.getenv("LANGCHAIN_API_KEY")
if not langsmith_api_key:
raise EnvironmentError(
"Missing environment variable: LANGCHAIN_API_KEY")
return langsmith.Client()
# Set up Runnable and Memory
@cache
def get_rag_chain(model_name: str = "gpt-4", temperature: float = 0.2) -> tuple[ChatOpenAI, ConversationBufferWindowMemory]:
"""Set up runnable and chat memory
Args:
model_name (str, optional): LLM model. Defaults to "gpt-4" 30012024.
temperature (float, optional): Model temperature. Defaults to 0.2.
Returns:
Runnable, Memory: Chain and Memory
"""
# Set up Langsmith to trace the chain
langsmith_tracing = create_langsmith_client()
# LLM and prompt template
llm = ChatOpenAI(model_name=model_name,
temperature=temperature)
prompt = generate_prompt_template()
# Set retrievers pointing to the practitioners's dataset
embeddings_model = "text-embedding-ada-002"
dense_retriever_client = DenseRetrieverClient(embeddings_model=embeddings_model,
collection_name="practitioners_db")
# Qdrant db as a retriever
practitioners_db_dense_retriever = dense_retriever_client.get_dense_retriever(search_type="similarity",
k=10)
# Testing the sparse vector retriever using Qdrant
collection_name = "practitioners_db_sparse_collection"
vector_name = "sparse_vector"
sparse_retriever_client = SparseRetrieverClient(
collection_name=collection_name,
vector_name=vector_name,
splade_model_id="naver/splade-cocondenser-ensembledistil",
k=15)
practitioners_db_sparse_retriever = sparse_retriever_client.get_sparse_retriever()
# Ensemble retriever for hyprid search (dense retriever seems to work better but the dense retriever is good for acronyms like RMT)
practitioners_ensemble_retriever = EnsembleRetriever(
retrievers=[practitioners_db_dense_retriever,
practitioners_db_sparse_retriever], weights=[0.2, 0.8]
)
# Compression retriever for practitioners db
practitioners_db_compression_retriever = compression_retriever_setup(
practitioners_ensemble_retriever,
embeddings_model="text-embedding-ada-002",
similarity_threshold=0.74
)
# Set retrievers pointing to the tall_tree_db
dense_retriever_client = DenseRetrieverClient(embeddings_model=embeddings_model,
collection_name="tall_tree_db")
tall_tree_db_dense_retriever = dense_retriever_client.get_dense_retriever(search_type="similarity",
k=8)
# Compression retriever for tall_tree_db
tall_tree_db_compression_retriever = compression_retriever_setup(
tall_tree_db_dense_retriever,
embeddings_model="text-embedding-ada-002",
similarity_threshold=0.74
)
# Set conversation history window memory. It only uses the last k interactions.
memory = ConversationBufferWindowMemory(memory_key="history",
return_messages=True,
k=6)
# Set up runnable using LCEL
setup_and_retrieval = {"practitioners_db": itemgetter("message")
| practitioners_db_compression_retriever
| format_practitioners_docs,
"tall_tree_db": itemgetter("message") | tall_tree_db_compression_retriever | format_tall_tree_docs,
"history": RunnableLambda(memory.load_memory_variables) | itemgetter("history"),
"message": itemgetter("message")
}
chain = (
setup_and_retrieval
| prompt
| llm
| StrOutputParser()
)
return chain, memory
|