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README.md

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-# RAG Project - Learn About Transformers
-
-A production-ready **Corrective Retrieval-Augmented Generation (CRAG)** system built with LangChain, LangGraph, and FastAPI. This project implements an intelligent RAG pipeline that not only retrieves relevant documents but also **validates, corrects, and improves** retrieval quality through an agent-based workflow.
-
-## What Makes This Different from Traditional RAG?
-
-### Traditional RAG:
-```
-Query → Retrieve Documents → Generate Answer
-```
-**Problem**: If retrieved documents are irrelevant or low-quality, the answer will be poor.
-
-### This Project (Corrective RAG):
-```
-Query → Retrieve → Grade Quality → Transform Query if Needed → Web Search if Necessary → Generate
-```
-**Solution**: Intelligent agent workflow that **self-corrects** by grading document relevance and taking corrective actions.
-
-## Architecture
-
-```mermaid
-graph LR
-    A[User Query] --> B[Retrieve]
-    B --> C[FAISS+MMR]
-    C --> D[Rerank]
-    D --> E{Grade}
-    E -->|Relevant| F[Generate]
-    E -->|Partial| G[Filter]
-    E -->|Poor| H[Transform]
-    G --> F
-    H --> I[Web Search]
-    I --> F
-    F --> J[Groq LLM]
-    J --> K[Answer]
-```
-
-## Key Features
-
-### 1. **Intelligent Document Grading**
-- LLM evaluates retrieved documents for relevance
-- Filters out low-quality results automatically
-- Ensures only useful context reaches generation
-
-### 2. **Query Transformation**
-- Rewrites ambiguous or poor queries
-- Improves retrieval on second attempt
-- Adaptive query refinement
-
-### 3. **Web Search Fallback**
-- Tavily API integration for external knowledge
-- Activates when local documents insufficient
-- Combines local + web results
-
-### 4. **Advanced Retrieval Stack**
-- **FAISS** vector store with MMR search
-- **FastEmbed** (BAAI/bge-small-en-v1.5) embeddings
-- **FlashRank** (rank-T5-flan) reranking
-- Self-query retriever support
-
-### 5. **LangGraph Agent Workflow**
-- State machine orchestration
-- Conditional routing logic
-- Transparent decision-making
-
-## Tech Stack
-
-| Component | Technology |
-|-----------|------------|
-| **LLM** | Groq (openai/gpt-oss-120b) |
-| **Embeddings** | FastEmbed (BAAI/bge-small-en-v1.5) |
-| **Vector Store** | FAISS |
-| **Reranker** | FlashRank (rank-T5-flan) |
-| **Agent Framework** | LangGraph |
-| **RAG Framework** | LangChain 0.3.x |
-| **Web Search** | Tavily API |
-| **Web Framework** | FastAPI + Uvicorn |
-| **Observability** | LangSmith (optional) |
-| **Document Source** | "Attention Is All You Need" (Transformer paper) |
-
-## Project Structure
-
-```
-RAG Project/
-├── project/
-│   ├── config/
-│   │   └── config.yaml              # Model & pipeline configuration
-│   ├── logger/
-│   │   └── logging.py               # Centralized logging
-│   ├── exception/
-│   │   └── except.py                # Custom exception handling
-│   ├── utils/
-│   │   ├── config_loader.py         # YAML config loader
-│   │   └── model_loader.py          # LLM & embedding initialization
-│   ├── source/
-│   │   └── data_preparation.py      # PDF/ArXiv document loading
-│   ├── model/
-│   │   ├── retriever.py             # FAISS retriever with MMR
-│   │   └── reranking.py             # FlashRank reranking
-│   ├── prompts/
-│   │   └── prompt_template.py       # RAG, Router, WebSearch prompts
-│   └── pipeline/
-│       ├── rag.py                   # Core RAG pipeline
-│       └── agents.py                # CRAG agent workflow
-├── templates/
-│   └── index.html                   # Web UI template
-├── static/
-│   └── styles.css                   # Purple gradient theme
-├── data/
-│   └── attention-is-all-you-need.pdf
-├── app.py                           # FastAPI application
-├── main.py                          # CLI entry point
-├── Dockerfile                       # Docker containerization
-└── requirements.txt                 # Dependencies
-
-```
-
-## Quick Start
-
-### 1. Clone & Install
-```bash
-git clone https://github.com/Abeshith/RAG-Project-PipeLine.git
-cd RAG-Project-PipeLine
-pip install -r requirements.txt
-```
-
-### 2. Set Environment Variables
-Create `.env` file:
-```env
-GROQ_API_KEY=your_groq_api_key
-GOOGLE_API_KEY=your_google_api_key
-LANGSMITH_API_KEY=your_langsmith_key  
-TAVILY_API_KEY=your_tavily_key        
-```
-
-### 3. Run Web Interface
-```bash
-python app.py
-```
-Visit: http://localhost:8000
-
-### 4. Run CLI
-```bash
-python main.py
-```
-
-## Docker Deployment
-
-### Build & Run
-```bash
-docker build -t rag-project .
-docker run -d -p 8000:8000 --env-file .env rag-project
-```
-
-## How It Works
-
-### Workflow Example
-
-**Query**: "What is the attention mechanism in transformers?"
-
-1. **Retrieval**: FAISS finds top 3 most similar chunks from "Attention Is All You Need" paper
-2. **Reranking**: FlashRank reorders by relevance (top 3 kept)
-3. **Grading**: LLM evaluates each document: 
-   - ✅ Doc 1: Relevant (explains attention)
-   - ✅ Doc 2: Relevant (shows formula)
-   - ❌ Doc 3: Not relevant (talks about training data)
-4. **Decision**: 2/3 relevant → Use filtered docs
-5. **Generation**: Groq LLM synthesizes answer from relevant docs
-6. **Output**: Comprehensive answer with LaTeX formulas (rendered via MathJax)
-
-### When Retrieval Fails
-
-**Query**: "What are the latest improvements to transformers in 2024?"
-
-1. **Retrieval**: Finds documents from 2017 paper
-2. **Grading**: ❌ All documents marked "not relevant" (outdated info)
-3. **Transform**: Rewrites query → "Recent transformer architecture improvements 2024"
-4. **Web Search**: Tavily searches current web content
-5. **Generation**: Answer combines paper fundamentals + recent developments
-
-## Web Interface Features
-
-- **Modern UI**: Purple gradient design with responsive layout
-- **MathJax Integration**: Renders LaTeX formulas beautifully
-- **Transformer Visualization**: Architecture diagram in header
-- **Real-time Search**: Fast async FastAPI backend
-- **Error Handling**: Graceful degradation with user-friendly messages
+---
+title: RAG Project - Learn with Transformers
+emoji: 🤖
+colorFrom: purple
+colorTo: blue
+sdk: docker
+pinned: false
+---