RAG ChatBot (Groq + LangChain)

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RAG ChatBot (Groq + LangChain)

Outline / Brief Description:
This project is a Retrieval-Augmented Generation (RAG)-based chatbot that enables interactive Q&A on uploaded PDF documents. It uses Streamlit for a stylish UI and LangChain for document chunking, vectorization with HuggingFace embeddings, and leveraging Groq’s ultra-fast LLaMA model for accurate responses. It’s deployed on Streamlit Cloud and is designed to make document analysis faster and more accessible.

📄 General Information

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• This project is a RAG-powered chatbot allowing queries on uploaded PDFs.
• It solves the problem of manually searching long documents, boosting efficiency.
• The goal is to streamline Q&A for students, professionals, and researchers.

🛠️ Technologies Used

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Programming Language:

• Python 3.11

Frameworks & Libraries:

• Streamlit — for building the web interface
• LangChain — for RAG pipeline and vectorstore management
• HuggingFace Embeddings — for embedding the document contents
• Groq API (LLaMA 3) — for super-fast LLM inference
• PyPDFLoader — for reading and splitting PDFs
• RecursiveCharacterTextSplitter — for efficient chunking of text content

Deployment & Version Control:

• Git & GitHub — version control and source code hosting
• Streamlit Cloud — one-click deployment & hosting

Configuration:

• .env files — for safe environment variable handling

✨ Features

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• 📂 PDF Upload Support — Easily upload one or multiple PDFs.
• 🧠 Context-Aware Answers — Answers derived directly from uploaded content.
• 📝 Conversation History — Keep track of queries and responses.
• ⚡ Groq LLaMA-powered Inference — Fast responses powered by Groq API.
• 🎨 Stylish UI — Modern glassmorphism interface for a polished look.
• 🔄 Reset Option — Quickly reset chat and upload new files.
• 🆕 Source References — See excerpts of source text alongside answers.

⚙️ Setup

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Requirements:

Check requirements.txt for dependencies:

streamlit
langchain
langchain-groq
langchain-huggingface
pypdf
sentence-transformers

Steps to Run Locally:

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/RAG_ChatBot_Groq.git
   cd RAG_ChatBot_Groq

2. (Optional) Create & activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file for your Groq API key:

   GROQ_API_KEY=your-groq-api-key-here

5. Run the app:

   streamlit run Chatbot.py

💡 Usage

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Once up and running, you can:

• 📂 Upload a PDF file — via the upload section.
• 🤖 Ask questions — and receive instant, accurate answers.
• 🔄 Restart session — to clear data and upload new files.

📊 Project Status

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✅ Completed and deployed on Streamlit Cloud — try the live app here 🎯

🚀 Improvements

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1. Multi-File Support — Query across multiple PDFs.
2. Conversation Persistence — Save sessions for future use.
3. Advanced Features — Voice-based querying and highlighted source excerpts.

🌟 Future Features

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1. Multi-File Chat Support
2. Voice Input & Output
3. Contextual Highlighting of Source Text

🙏 Acknowledgements

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1. This project was inspired by the need for smarter document Q&A tools.
2. Based on tutorials and official docs from LangChain, Groq, HuggingFace, and Streamlit.
3. Special thanks to the open-source community that made these tools accessible.

📬 Contact

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