📄 DocumentSearch RAG Chatbot

A Retrieval-Augmented Generation (RAG) chatbot built using LangChain, LangGraph, and FAISS for intelligent document search and question answering.

This project enables users to query documents (PDFs, URLs, etc.) and receive context-aware, accurate responses powered by LLMs.

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🚀 Features

• 🔍 Semantic document search using FAISS
• 🧠 Context-aware responses with RAG pipeline
• 🔗 Graph-based orchestration using LangGraph
• 📄 Supports PDF and URL ingestion
• ⚡ Modular and scalable architecture
• 🧩 Easy to extend with new data sources

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🏗️ Project Structure

.
├── data/
│   ├── attention.pdf
│   └── url.txt
├── src/
│   ├── config/               # Configuration files
│   ├── document_ingestion/   # Data loading & preprocessing
│   ├── graph_builder/        # LangGraph workflow setup
│   ├── node/                 # Graph nodes (LLM, retrieval, etc.)
│   ├── state/                # State management
│   ├── vectorstore/          # FAISS vector DB logic
│   └── __init__.py
├── .python-version
└── README.md

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⚙️ Tech Stack

• LLM Framework: LangChain
• Workflow Orchestration: LangGraph
• Vector Database: FAISS
• Language: Python

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🔄 How It Works

1. Document Ingestion

• Load PDFs or URLs
• Split into chunks
• Generate embeddings

2. Vector Storage

• Store embeddings in FAISS
• Enable fast similarity search

3. Query Processing

• User query is embedded
• Relevant chunks retrieved

4. RAG Pipeline

• Retrieved context + query sent to LLM
• Generates grounded response

5. LangGraph Workflow

• Nodes handle each step (retrieval, generation, etc.)
• State flows through graph

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🛠️ Installation

# Clone the repository
git clone https://github.com/namansinghal111/DocumentSearch_RAG_Chatboat.git

# Navigate to project
cd DocumentSearch_RAG_Chatboat

# Create virtual environment
python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

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▶️ Usage

python main.py

• Add your documents to the data/ folder
• Configure settings in src/config
• Run the chatbot and start querying

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🔧 Configuration

Set your environment variables:

OPENAI_API_KEY=your_api_key

You can also modify:

• Chunk size
• Embedding model
• Retrieval strategy

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🧠 Example Query

Q: What is attention mechanism?
A: The attention mechanism allows models to focus on relevant parts...

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📌 Future Improvements

• 🌐 Web UI (Streamlit / React)
• 📊 Better evaluation metrics
• 🔐 Authentication & user sessions
• 🗂️ Multi-document collections
• 🧩 Plug-and-play LLM support

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