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ef90e8e672
# Description
Major PR which, among other things, introduces the possibility of easily
customizing the retrieval workflows. Workflows are based on LangGraph,
and can be customized using a [yaml configuration
file](core/tests/test_llm_endpoint.py), and adding the implementation of
the nodes logic into
[quivr_rag_langgraph.py](1a0c98437a/backend/core/quivr_core/quivr_rag_langgraph.py)
This is a first, simple implementation that will significantly evolve in
the coming weeks to enable more complex workflows (for instance, with
conditional nodes). We also plan to adopt a similar approach for the
ingestion part, i.e. to enable user to easily customize the ingestion
pipeline.
Closes CORE-195, CORE-203, CORE-204
## Checklist before requesting a review
Please delete options that are not relevant.
- [X] My code follows the style guidelines of this project
- [X] I have performed a self-review of my code
- [X] I have commented hard-to-understand areas
- [X] I have ideally added tests that prove my fix is effective or that
my feature works
- [X] New and existing unit tests pass locally with my changes
- [X] Any dependent changes have been merged
## Screenshots (if appropriate):
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import asyncio
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import json
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from uuid import uuid4
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from langchain_core.embeddings import DeterministicFakeEmbedding
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from langchain_core.messages.ai import AIMessageChunk
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from langchain_core.vectorstores import InMemoryVectorStore
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from quivr_core.chat import ChatHistory
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from quivr_core.config import LLMEndpointConfig, RetrievalConfig
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from quivr_core.llm import LLMEndpoint
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from quivr_core.quivr_rag_langgraph import QuivrQARAGLangGraph
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async def main():
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retrieval_config = RetrievalConfig(llm_config=LLMEndpointConfig(model="gpt-4o"))
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embedder = DeterministicFakeEmbedding(size=20)
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vec = InMemoryVectorStore(embedder)
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llm = LLMEndpoint.from_config(retrieval_config.llm_config)
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chat_history = ChatHistory(uuid4(), uuid4())
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rag_pipeline = QuivrQARAGLangGraph(
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retrieval_config=retrieval_config, llm=llm, vector_store=vec
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)
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conversational_qa_chain = rag_pipeline.build_chain()
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with open("response.jsonl", "w") as f:
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async for event in conversational_qa_chain.astream_events(
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{
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"messages": [
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("user", "What is NLP, give a very long detailed answer"),
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],
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"chat_history": chat_history,
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"custom_personality": None,
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},
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version="v1",
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config={"metadata": {}},
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):
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kind = event["event"]
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if (
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kind == "on_chat_model_stream"
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and event["metadata"]["langgraph_node"] == "generate"
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):
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chunk = event["data"]["chunk"]
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dict_chunk = {
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k: v.dict() if isinstance(v, AIMessageChunk) else v
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for k, v in chunk.items()
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}
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f.write(json.dumps(dict_chunk) + "\n")
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asyncio.run(main())
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