Support for Agentic Retrieval in LangChain (Inspired by Azure AI Search)

[kevaldekivadiya2415]

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Feature request

Requesting support for an Agentic Retrieval pipeline in LangChain, similar to the new agentic retrieval capability in Azure AI Search. This feature leverages an LLM to decompose complex user queries into multiple parallel subqueries using chat history and context, and combines the results in a structured, ranked response optimized for RAG applications.

Motivation

LangChain already excels in building composable chains and retrieval-augmented generation (RAG) pipelines. However, it currently lacks native support for automatic query decomposition, parallel subquery execution, and structured response formats driven by LLMs — key features now offered by Azure AI Search.

This capability would significantly improve recall and answer quality in multi-turn conversational AI, copilots, and agent-based applications.

Proposal (If applicable)

Proposed Features

1. AgenticRetriever component:

   • Accepts a complex user query + optional chat history.

   • Uses an LLM to:

     • Understand the user intent.
     • Break down the input into focused subqueries.
     • Optionally correct typos and paraphrase queries.

2. Subquery Executor:

   • Executes subqueries in parallel over vector stores or hybrid retrievers (e.g., keyword + vector).

3. Semantic Merger & Ranker:

   • Ranks and merges results from all subqueries using a semantic scoring model (or embedding-based similarity + reranking).

4. Structured Output:

   • Final response includes:

     • A synthesized answer string.
     • Source/reference chunks.
     • Metadata about subqueries and execution (for debugging or traceability).

5. Integration with LangGraph for orchestrating the agentic workflow with observability.

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Benefits

• Improves accuracy for multi-part and ambiguous user queries.
• Better support for long-running conversations (multi-turn context).
• Enables powerful AI copilots and assistants out of the box.

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Implementation Ideas

• Use OpenAI’s GPT-4o, Claude, or any model as the reasoning agent.
• Offer default AgenticRetriever(...) to wrap existing retrievers (e.g., FAISS, Elastic, AstraDB, etc.).
• Plug in with LangGraph for advanced routing and retries.

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Example API Design:

retriever = AgenticRetriever(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=my_hybrid_retriever,
    reranker=ColBERT()  # optional
)

result = retriever.invoke({
    "query": "Find me open-source RAG tools with observability and LangChain compatibility",
    "chat_history": previous_messages,
})

print(result["answer"])
print(result["references"])
print(result["execution_plan"])

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Would love to collaborate or contribute on this if it aligns with LangChain’s roadmap. Let me know what you think!

—
Keval Dekivadiya
Maintainer of FastAPI GenAI Boilerplate