AI / MLMarch 12, 20266 min read

Applying the Stack: RAG + LangGraph in a Booking Platform

How RAG retrieval and LangGraph agent orchestration work together in a production AI booking platform.

Stack in practice

An AI booking platform uses the full stack. RAG retrieves relevant hotels, restaurants, and attractions. LangChain connects retrievers to LLMs. LangGraph orchestrates the multi-step booking workflow. Hybrid search with Elasticsearch and pgvector feeds the retrieval layer.

booking_platform_api.pyFastAPI

@router.post("/trips/plan")
async def plan_trip(request: TripPlanRequest):
    # RAG: retrieve relevant travel content
    context = await hybrid_search(
        query=request.destination,
        preferences=request.preferences,
        top_k=20,
    )

    # LangGraph: run multi-step agent workflow
    result = await booking_agent.ainvoke({
        "destination": request.destination,
        "budget": request.budget,
        "dates": request.dates,
        "context": context,
    })

    return {"itinerary": result["itinerary"], "sources": result["citations"]}

Why the layers stack cleanly

Each layer has one job. RAG finds evidence. LangChain wires components. LangGraph controls execution flow. GraphRAG enters when cross-document relationships improve recommendation quality. Keeping these roles separate makes the system testable and maintainable.