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‎README.md‎

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

‎logs/2025_12_01.log‎

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[2025-12-01 12:48:49] INFO - project.utils.model_loader - Loaded FastEmbed: BAAI/bge-small-en-v1.5
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[2025-12-01 12:48:50] INFO - project.model.retriever - DocumentRetriever initialized
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[2025-12-01 12:48:58] INFO - project.model.retriever - Vector store created with 43 documents
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[2025-12-01 12:48:58] INFO - project.model.retriever - Base retriever configured with mmr search
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[2025-12-01 12:48:58] INFO - project.pipeline.rag - RAG chain built successfully
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[2025-12-01 12:48:58] INFO - project.pipeline.rag - RAG pipeline setup complete
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‎templates/index.html‎

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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Learn with Transformers</title>
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<title>RAG PROJECT PIPELINE</title>
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<link rel="stylesheet" href="/static/styles.css">
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<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
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<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>

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