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# Description This PR includes far too many new features: - detection of user intent (closes CORE-211) - treating multiple questions in parallel (closes CORE-212) - using the chat history when answering a question (closes CORE-213) - filtering of retrieved chunks by relevance threshold (closes CORE-217) - dynamic retrieval of chunks (closes CORE-218) - enabling web search via Tavily (closes CORE-220) - enabling agent / assistant to activate tools when relevant to complete the user task (closes CORE-224) Also closes CORE-205 ## Checklist before requesting a review Please delete options that are not relevant. - [ ] My code follows the style guidelines of this project - [ ] I have performed a self-review of my code - [ ] I have commented hard-to-understand areas - [ ] I have ideally added tests that prove my fix is effective or that my feature works - [ ] New and existing unit tests pass locally with my changes - [ ] Any dependent changes have been merged ## Screenshots (if appropriate): --------- Co-authored-by: Stan Girard <stan@quivr.app>
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.rag.entities.chat import ChatHistory
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from quivr_core.rag.entities.config import LLMEndpointConfig, RetrievalConfig
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from quivr_core.llm import LLMEndpoint
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from quivr_core.rag.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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