Build an Agentic Corrective RAG System from Scratch (With LangGraph)

Analytics Vidhya · Advanced ·🔍 RAG & Vector Search ·3w ago

Key Takeaways

Builds a Corrective RAG system from scratch using LangGraph to improve retrieval quality and reduce hallucinations

Original Description

Build a Corrective RAG (CRAG) system from scratch using LangGraph and learn how to create a production-ready Agentic RAG pipeline that reduces hallucinations and improves retrieval quality. Traditional RAG systems fail when retrieval goes wrong. Irrelevant chunks enter the context window, and the LLM confidently generates inaccurate or hallucinated answers. Adding more embeddings or a larger vector database rarely solves the problem. In this tutorial, you'll build a Corrective RAG (CRAG) system from scratch using LangGraph, based on the original CRAG research paper. This advanced RAG architecture automatically evaluates retrieved documents and triggers web search whenever the retrieved context is insufficient. By the end of this video, you'll know how to build Production RAG systems that are more accurate, reliable, and significantly harder to break. What You'll Build ✅ Retrieve documents from a Chroma vector database ✅ Grade retrieved documents using an LLM ✅ Detect retrieval failures automatically ✅ Trigger web search using Tavily when context quality is low ✅ Rewrite user queries for better retrieval ✅ Generate grounded answers with fewer hallucinations ✅ Build a complete LangGraph agent workflow Technologies Used 🟡 LangGraph 🟡 LangChain 🟡 OpenAI GPT-4.1 Mini 🟡 Chroma Vector Database 🟡 Tavily Search API 🟡 PyMuPDF 🟡 Python This tutorial covers topics including Corrective RAG, CRAG, LangGraph Tutorial, LangChain Tutorial, Agentic RAG, Advanced RAG, Production RAG, Retrieval Augmented Generation, RAG with Web Search, AI Agents, LLM Engineering, AI Engineering, Generative AI, and LLMOps. Timestamps 00:00 - Why Traditional RAG Fails 01:49 - Corrective RAG (CRAG) Architecture Explained 02:48 - Environment Setup and Dependencies 03:45 - Building the Vector Database with Chroma 08:00 - Retrieval Testing and Score Thresholds 10:03 - Building the Retrieval Grader 14:33 - Building the Question Answering Workflow 17:22 - Query Rewriting Agent 18:40 - Integrati
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Chapters (9)

Why Traditional RAG Fails
1:49 Corrective RAG (CRAG) Architecture Explained
2:48 Environment Setup and Dependencies
3:45 Building the Vector Database with Chroma
8:00 Retrieval Testing and Score Thresholds
10:03 Building the Retrieval Grader
14:33 Building the Question Answering Workflow
17:22 Query Rewriting Agent
18:40 Integrati
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