Add README.md for project documentation

Added comprehensive documentation for the Agentic Corrective RAG project, including setup instructions, architecture, tech stack, and key features.

README.md

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+# Agentic Corrective RAG — Document Q&A
+
+![CI](https://github.com/Hitan547/agentic-corrective-rag/actions/workflows/ci.yml/badge.svg)
+![Python](https://img.shields.io/badge/python-3.10%2B-blue)
+![LLM](https://img.shields.io/badge/LLM-LLaMA%203.3%2070B-orange)
+![Framework](https://img.shields.io/badge/framework-LangGraph-green)
+
+A production-aware document Q&A system that answers questions **only from your uploaded documents** — not from the model's imagination. Built with hybrid retrieval, cross-encoder reranking, and a self-correcting LangGraph agent that automatically retries if the answer isn't grounded in the source material.
+
+---
+
+## What It Does
+
+Upload any PDF or TXT file, ask a question, and get an answer backed by:
+- The exact source chunks it used
+- A validation verdict (PASS/FAIL)
+- How many self-correction retries were needed
+
+---
+
+## Architecture
+
+```
+PDF/TXT Upload
+      │
+      ▼
+┌─────────────────────────────────┐
+│         Ingestion Pipeline      │
+│  PyMuPDF → Chunking → Embeddings│
+│  FAISS Index + BM25 Index       │
+└─────────────────────────────────┘
+      │
+      ▼
+┌─────────────────────────────────┐
+│       Hybrid Retrieval          │
+│  FAISS (dense) + BM25 (sparse)  │
+│  → RRF Fusion                   │
+│  → Cross-Encoder Reranking      │
+└─────────────────────────────────┘
+      │
+      ▼
+┌─────────────────────────────────┐
+│     Corrective RAG Agent        │
+│  LangGraph StateGraph           │
+│  Generate → Validate → Retry    │
+│  (up to 3 automatic retries)    │
+└─────────────────────────────────┘
+      │
+      ▼
+  Streamlit UI + FastAPI Backend
+```
+
+---
+
+## Tech Stack
+
+| Layer | Technology |
+|---|---|
+| LLM | LLaMA 3.3 70B via Groq API |
+| Agent Framework | LangGraph (StateGraph) |
+| Dense Retrieval | FAISS + `all-MiniLM-L6-v2` |
+| Sparse Retrieval | BM25 (rank-bm25) |
+| Reranker | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
+| PDF Parsing | PyMuPDF (fitz) |
+| Backend | FastAPI |
+| Frontend | Streamlit |
+| Testing | pytest (unit + integration) |
+| CI/CD | GitHub Actions |
+
+---
+
+## Key Features
+
+- **Hybrid Search** — combines FAISS semantic search and BM25 keyword search, fused with Reciprocal Rank Fusion (RRF)
+- **Cross-Encoder Reranking** — re-scores top candidates by reading query + chunk together for higher precision
+- **Self-Correcting Agent** — LangGraph pipeline automatically detects hallucinations and retries up to 3 times
+- **Hallucination Validation** — a second LLM call checks every answer against the source context before returning it
+- **Session Memory** — remembers last 5 turns of conversation per session
+- **Background Indexing** — document ingestion runs in the background without blocking the API
+- **CI/CD** — unit tests run automatically on every push via GitHub Actions
+
+---
+
+## Project Structure
+
+```
+agentic-corrective-rag/
+├── agent.py          # LangGraph corrective RAG agent
+├── retriever.py      # Hybrid retrieval + RRF + reranking
+├── ingestion.py      # PDF/TXT ingestion + FAISS/BM25 indexing
+├── main.py           # FastAPI backend
+├── app.py            # Streamlit frontend
+├── config.py         # Configuration and constants
+├── requirements.txt
+├── tests/
+│   ├── test_unit.py        # Unit tests (CI)
+│   └── test_integration.py # Integration tests (local only)
+└── .github/
+    └── workflows/
+        └── ci.yml          # GitHub Actions CI pipeline
+```
+
+---
+
+## Setup
+
+**1. Clone the repo**
+```bash
+git clone https://github.com/Hitan547/agentic-corrective-rag.git
+cd agentic-corrective-rag
+```
+
+**2. Install dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+**3. Set up environment**
+```bash
+# Create a .env file
+echo "GROQ_API_KEY=your_key_here" > .env
+```
+Get your free API key at [console.groq.com](https://console.groq.com)
+
+**4. Run the backend**
+```bash
+uvicorn main:app --reload --port 8080
+```
+
+**5. Run the frontend** (new terminal)
+```bash
+streamlit run app.py
+```
+
+**6. Open** `http://localhost:8501`, upload a PDF, click **Index documents**, then ask questions.
+
+---
+
+## Running Tests
+
+```bash
+# Unit tests (fast, no API needed)
+python -m pytest tests/test_unit.py -v
+
+# Integration tests (requires GROQ_API_KEY)
+python -m pytest tests/test_integration.py -v -m integration
+```
+
+---
+
+## How the Agent Works
+
+1. **Generate** — LLaMA 3.3 70B answers using only the retrieved chunks
+2. **Validate** — a second LLM call checks if every claim is supported by the context
+3. **Retry** — if validation fails, the agent retries with the failure reason as feedback
+4. **Stop** — returns the answer after PASS or after 3 retries
+
+---
+
+## Author
+
+**Hitan K** — Final-year CS undergraduate (AI specialization)
+
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-hitan--k-blue)](https://linkedin.com/in/hitan-k)
+[![GitHub](https://img.shields.io/badge/GitHub-Hitan547-black)](https://github.com/Hitan547)
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-Hitan2004-yellow)](https://huggingface.co/Hitan2004)