🤖 RAG Chatbot (Multi-PDF AI Assistant)

A production-ready Retrieval-Augmented Generation (RAG) chatbot that allows users to upload multiple PDFs and ask questions based on their content.

Built using LangChain, Groq LLM, HuggingFace embeddings, and Streamlit, this app provides fast, accurate, and context-aware answers with source attribution.

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🚀 Live Demo

🔗 https://shivamgravity-rag-chatbot.streamlit.app/

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✨ Features

• 📂 Multi-PDF Upload — Query multiple documents at once
• 🧠 RAG Pipeline — Context-aware answers using retrieved document chunks
• ⚡ Fast LLM Inference — Powered by Groq (LLaMA 3)
• 🔍 Source Attribution — See which documents were used for answers
• 💬 Conversational Memory — Supports follow-up questions
• 🎨 ChatGPT-style UI — Clean and interactive interface
• ☁️ Deployed on Streamlit Cloud

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🏗️ Tech Stack

• Frontend: Streamlit
• LLM: Groq (LLaMA 3)
• Framework: LangChain (modular)
• Embeddings: HuggingFace (sentence-transformers)
• Vector Database: ChromaDB
• Document Loader: PyPDF

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🧠 Architecture

User Query
   ↓
Retriever (ChromaDB)
   ↓
Relevant Chunks
   ↓
Prompt + Context
   ↓
Groq LLM
   ↓
Answer + Sources

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⚙️ Installation & Setup

1. Clone the repository

git clone https://github.com/shivamgravity/langchain-rag-chatbot.git
cd rag-chatbot

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2. Create virtual environment

python -m venv venv
venv\Scripts\activate   # Windows
# or
source venv/bin/activate  # Mac/Linux

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3. Install dependencies

pip install -r requirements.txt

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4. Add API Key

Create a .env file:

GROQ_API_KEY=your_api_key_here

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5. Run the app

streamlit run app.py

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🌐 Deployment

The app is deployed using Streamlit Community Cloud.

To deploy:

1. Push code to GitHub
2. Connect repo to Streamlit Cloud
3. Add GROQ_API_KEY in Secrets
4. Deploy 🚀

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📂 Project Structure

rag-chatbot/
│
├── app.py                # Streamlit UI
├── rag_pipeline.py       # RAG logic
├── requirements.txt      # Dependencies
├── .env                  # API keys (not committed)
└── temp_docs/            # Uploaded PDFs (ignored)

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🎯 Key Learnings

• Implemented Retrieval-Augmented Generation (RAG) from scratch
• Worked with LangChain’s modular architecture
• Integrated Groq LLM for fast inference
• Built multi-document querying system
• Designed chat-based UI with memory and sources

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🏆 Future Improvements

• 🔍 Highlight exact text used for answers (Explainability)
• 📊 Confidence scoring
• ⚡ Faster retrieval optimization
• 👤 User authentication
• 🌐 Full-stack deployment (FastAPI + React)

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👨‍💻 Author

Shivam

• GitHub: https://github.com/shivamgravity
• LinkedIn: https://www.linkedin.com/in/shivam-gravity

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⭐ If you like this project

Give it a star ⭐ on GitHub!

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