| # 🧠 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). | |
| --- | |
| ## 🏛️ Architecture | |
| ```mermaid | |
| 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. | |
| --- | |
| ## 🛠️ 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**: | |
| ```bash | |
| uvicorn main:app --reload | |
| ``` | |
| 4. **Run Evaluation Audit**: | |
| ```bash | |
| python run_evals.py | |
| ``` | |
| ### Option B: Docker (Recommended) | |
| 1. **Build**: | |
| ```bash | |
| docker-compose build | |
| ``` | |
| 2. **Run**: | |
| ```bash | |
| docker-compose up | |
| ``` | |
| ### Option C: Run Tests | |
| ```bash | |
| pytest | |
| ``` | |
| --- | |
| ## 📊 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. | |
| --- | |
| ## 📜 Credits | |
| Built by **Vignesh Ladar Vidyananda**. | |
| Powered by FastAPI, LangGraph, FAISS, and Google Gemini. | |