Corrective RAG Agent

A sophisticated Retrieval-Augmented Generation (RAG) system that implements a corrective multi-stage workflow using LangGraph. This system combines document retrieval, relevance grading, query transformation, and web search to provide comprehensive and accurate responses.

Features

• Smart Document Retrieval: Uses Qdrant vector store for efficient document retrieval
• Document Relevance Grading: Employs Claude 4.5 sonnet to assess document relevance
• Query Transformation: Improves search results by optimizing queries when needed
• Web Search Fallback: Uses Tavily API for web search when local documents aren't sufficient
• Multi-Model Approach: Combines OpenAI embeddings and Claude 4.5 sonnet for different tasks
• Interactive UI: Built with Streamlit for easy document upload and querying

How to Run?

1. Clone the Repository:

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd rag_tutorials/corrective_rag

2. Install Dependencies:

pip install -r requirements.txt

3. Set Up API Keys:

You'll need to obtain the following API keys:

• OpenAI API key (for embeddings)
• Anthropic API key (for Claude 4.5 sonnet as LLM)
• Tavily API key (for web search)
• Qdrant Cloud Setup

1. Visit Qdrant Cloud
2. Create an account or sign in
3. Create a new cluster
4. Get your credentials:

• Qdrant API Key: Found in API Keys section
• Qdrant URL: Your cluster URL (format: https://xxx-xxx.aws.cloud.qdrant.io)

4. Run the Application:

streamlit run corrective_rag.py

5. Use the Application:

• Upload documents or provide URLs
• Enter your questions in the query box
• View the step-by-step Corrective RAG process
• Get comprehensive answers

Tech Stack

• LangChain: For RAG orchestration and chains
• LangGraph: For workflow management
• Qdrant: Vector database for document storage
• Claude 4.5 sonnet: Main language model for analysis and generation
• OpenAI: For document embeddings
• Tavily: For web search capabilities
• Streamlit: For the user interface