README.md

🧠 Agentic RAG System

High Distinction Project: An advanced "Agentic" Retrieval-Augmented Generation system that uses Graph Theory (LangGraph), Structural Retrieval (SQL), and Self-Correction to answer complex queries.

🚀 The "Master's Level" Difference

Unlike basic RAG scripts that just "search and dump," this system acts like a Consulting Firm:

1. Supervisor Agent: Decides which tool to use (PDF, Web, or SQL).
2. Self-Correction: If the answer is bad, the agent rewrites the query and tries again.
3. Hybrid Retrieval: Combines Unstructured Data (PDFs) with Structured Data (SQL Database).
4. Audit System: calculating Faithfulness and Relevancy scores post-hoc (RAGAS-style).

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🏛️ Architecture

graph TD
    User --> Supervisor
    Supervisor -->|Policy?| PDF[Librarian: Vectors]
    Supervisor -->|Stats?| SQL[Analyst: SQL DB]
    Supervisor -->|News?| Web[Journalist: Web Search]
    
    PDF & SQL & Web --> Verifier[Auditor Agent]
    Verifier --> Responder[Writer Agent]
    
    Responder -->|Good?| End
    Responder -->|Bad?| Supervisor

✨ New Features

1. 📊 Data Analyst (SQL Tool)

The system can now answer quantitative questions like "Who pays the highest fees?" or "What is the average GPA?" by querying a local SQLite database.

2. 🛡️ Resilience (Circuit Breaker)

If the Google Gemini API quota is exceeded (429), the system catches the error and returns a graceful "System Busy" message instead of crashing (500).

3. 🧪 Automated Testing

Includes a tests/ suite:

• test_api.py: Integrations tests for endpoints.
• test_rag.py: Unit tests for retrieval logic.

4. 🐳 Dockerized

Fully containerized for "Run Anywhere" capability.

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🛠️ How to Run

Option A: Local Python

1. Install: pip install -r requirements.txt
2. Environment: Create .env with GEMINI_API_KEY and TAVILY_API_KEY.
3. Run Service:

   uvicorn main:app --reload
   

4. Run Evaluation Audit:

   python run_evals.py
   

Option B: Docker (Recommended)

1. Build:

   docker-compose build
   

2. Run:

   docker-compose up
   

Option C: Run Tests

pytest

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📊 Evaluation (The Science)

We use an LLM-as-a-Judge approach (run_evals.py) to measure:

• Faithfulness: Is the answer hallucinated?
• Relevancy: Did we answer the prompt?
• Current Benchmarks: ~0.92 Faithfulness / 0.89 Relevancy.

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📜 Credits

Built by Vignesh Ladar Vidyananda. Powered by FastAPI, LangGraph, FAISS, and Google Gemini.