🔧 What is LangGraph?

LangGraph is a library from LangChain that helps you build stateful, multi-step applications using graph-based workflows.

Think of it like building a flowchart of how your AI app should behave — with clear paths for different logic.

LangGraph = LangChain + Graph + Memory + Control flow.

✅ RAG with LangGraph – What's the Flow?

Here's a typical RAG pipeline using LangGraph:

🔁 The Graph Nodes

Each node is a step in the RAG process.

1. Input Node – User question comes in.

2. Retriever Node – Use a retriever (e.g., FAISS, Chroma) to fetch relevant chunks from your knowledge base.

3. LLM Generation Node – Send the retrieved documents + question to the LLM for final answer.

4. Output Node – Return the answer to the user.

Optional nodes can include:

• Condense question (for follow-ups)

• Re-ranking of documents

• Memory for previous chats

• Validation of LLM outputs

🔁 How LangGraph Helps in RAG

• You can define custom logic in how your RAG system works.

• You can add retry mechanisms, loops, or condition-based routing (e.g., “If retrieval fails, ask a clarifying question”).

• You can manage conversation memory, so it feels like a chatbot that remembers context.

🧠 Visual Flow Example

[User Input]
     ↓
[Condense Question (optional)]
     ↓
[Retrieve Documents]
     ↓
[Generate Answer using LLM]
     ↓
[Return Response to User]

You define these steps in LangGraph using @tool, @node, and connect them using edges.

📦 Example Use Case in Code (Simplified)

from langgraph.graph import StateGraph
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

retriever = vectorstore.as_retriever()

def retrieve_docs(state):
    return {"docs": retriever.get_relevant_documents(state["question"])}

def generate_answer(state):
    llm = ChatOpenAI()
    docs = state["docs"]
    question = state["question"]
    # Combine docs and question
    prompt = f"Answer based on docs: {docs}\n\nQuestion: {question}"
    answer = llm.invoke(prompt)
    return {"answer": answer}

# Define graph
graph = StateGraph()
graph.add_node("retrieve", retrieve_docs)
graph.add_node("generate", generate_answer)
graph.set_entry_point("retrieve")
graph.add_edge("retrieve", "generate")
graph.set_finish_point("generate")

# Run graph
graph_executor = graph.compile()
result = graph_executor.invoke({"question": "What is RAG?"})
print(result["answer"])

🚀 Why Use LangGraph for RAG?

• Modular and reusable.

• Great for production apps needing control flow and memory.

• Makes debugging and testing easier.

• Scales well with complex logic like agentic behavior.