Corrective RAG (CRAG) with LangGraph

This project implements a simplified version of the Corrective-RAG (CRAG) framework using LangGraph, a self-reflective Retrieval-Augmented Generation (RAG) strategy that incorporates dynamic feedback and correction mechanisms during the retrieval process.

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🧠 What is CRAG?

CRAG enhances the standard RAG pipeline by:

• Grading retrieved documents for relevance before generation.
• Supplementing retrieval with web search if documents are irrelevant or uncertain.
• (Optionally) refining knowledge through partitioning into "knowledge strips" — skipped in this version for simplicity.

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✅ Key Features in This Implementation

• Relevance Grading: Retrieved documents are graded for relevance.
• Conditional Generation: Generation only proceeds if relevant context is available.
• Fallback to Web Search: If retrieved content is irrelevant or insufficient, the query is rewritten and a web search is performed using Tavily Search.
• Query Rewriting: Enhances the original user query for better supplemental search results.
• FastAPI Integration: Provides an API for programmatic access to CRAG.
  • /query endpoint for normal query-response interaction
  • /stream endpoint for real-time streaming of generated answers
• Frontend UI: Minimal web interface for submitting questions and viewing answers/documents.

Note: Knowledge refinement (splitting into "knowledge strips" and further grading) is omitted in this version but can be added as a separate node in LangGraph later.

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🛠️ Tech Stack

• LangGraph – for building a dynamic, self-reflective RAG workflow
• LangSmith – for tracing, debugging, and monitoring the RAG pipeline
• DuckDuckGo Search (via tool) – for real-time supplemental web data
• LLama 3.2 3B (via Ollama) – local language model for grading, query rewriting, and generation
• Hugging Face Embeddings – for dense retrieval of relevant documents
• FAISS – vector store for efficient similarity search over embeddings
• FastAPI – for building the API and streaming endpoint
• HTML/JS Frontend – simple user interface for queries and streaming answers

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⚡ FastAPI Features

• POST /query – send a question, receive documents and generated answer.
• GET /stream – receive the generated answer in real-time chunks for improved UX.
• GET / – minimal web UI to submit questions and display streaming answers alongside retrieved documents.

Streaming Example:

const evtSource = new EventSource(`/stream?question=Your question here`);
evtSource.onmessage = function(event) {
    if(event.data !== "[DONE]") {
        console.log(event.data); // partial answer
    }
};