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Retrieval-Augmented Generation (RAG) — From Fundamentals to Production-Ready Agentic RAG Systems

Building the Agentic RAG Workflow with LangGraph

Implement the agentic workflow using LangGraph: retriever tools, ReAct agent loop, plan-and-execute, self-correction, and conditional routing for the capstone.

Learning Goals

  • Build the agentic RAG workflow with LangGraph
  • Implement self-correction and conditional routing

Building the Agentic RAG Workflow with LangGraph

Now that we have our ingestion and retrieval layers, it's time to build the "Brain" of our support agent. We will use LangGraph to create a stateful, cyclical workflow that orchestrates our retrievers. Our agent won't just retrieve and guess; it will Grade its results, Pivot to the web if needed, and Reflect on its final answer to ensure zero hallucinations.

In this section, we will implement the core agentic logic of our capstone project.

Learning Goals

  • Orchestrate a multi-node LangGraph for complex RAG tasks.
  • Implement conditional routing for "Knowledge Gaps" (Vector → Web Search).
  • Build a "Self-Correction" loop using LLM-based document grading.

Core Concepts

1. The Agent State

Our agent's memory (State) will track:

  • question: The user's input.
  • documents: The context retrieved from internal docs or the web.
  • generation: The draft and final versions of the answer.
  • web_search: A flag indicating if the internal search failed.

2. The Logic Nodes

  • retrieve: Calls our Hybrid + Re-ranker engine.
  • grade_documents: Evaluates the retrieved chunks for relevance.
  • web_search: Calls Tavily if grade_documents fails.
  • generate: Synthesizes the final answer.

3. The Self-Correction Loop

Assembling the Capstone Agent

  1. 1
    Step 1
    1from langgraph.graph import StateGraph
2
3class AgentState(TypedDict):
4    question: str
5    documents: list
6    generation: str
7
8workflow = StateGraph(AgentState)
  2. 2
    Step 2

    Use a structured LLM to perform binary classification (yes/no) on each retrieved chunk.

  3. 3
    Step 3

    If the grader returns 'no' for all documents, route the flow to a web_search node that uses the Tavily tool.

  4. 4
    Step 4
    1workflow.add_edge("web_search", "generate")
2workflow.add_conditional_edges("generate", grade_generation_v_documents)
3
4app = workflow.compile()

Example: Successful Self-Correction

User: "How do I fix error code X-501 in the latest firmware?"

  1. Retrieve: Internal docs only mention X-500.
  2. Grade: Grader flags documents as irrelevant.
  3. Web Search: Agent finds a patch note for X-501 on the official support forum.
  4. Generate: Agent provides the forum's fix, noting that it's a new update not yet in the manual. The agent successfully recognized its internal knowledge gap and filled it autonomously.

Common Mistakes

  • Vague Grading Prompts: If your grader is too "nice," it will accept irrelevant documents, leading to poor answers. Use a strict rubric: "Does this chunk contain the specific fix or fact requested?"
  • Infinite Looping: If the web search also returns junk, the agent might keep looping. Always set a max_retries counter in your State.

Recap

  • We built a robust reasoning loop with LangGraph.
  • We implemented a Vector-to-Web fallback mechanism.
  • We used reflection to ensure answer quality before the final output.

Knowledge Check

Question 1 of 3
Q1Single choice

What is the primary function of the 'Grade Docs' node in our capstone agent?

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