https://github.com/haswinai/rag_chatbot

A Retrieval-Augmented Generation (RAG) chatbot that answers questions from uploaded PDFs. Built using LangChain, Groq LLMs (LLaMA 3), FAISS, HuggingFace Embeddings, and Streamlit.
https://github.com/haswinai/rag_chatbot

faiss langchain llama3 rag-chatbot streamlit

Last synced: 3 months ago
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A Retrieval-Augmented Generation (RAG) chatbot that answers questions from uploaded PDFs. Built using LangChain, Groq LLMs (LLaMA 3), FAISS, HuggingFace Embeddings, and Streamlit.

• Host: GitHub
• URL: https://github.com/haswinai/rag_chatbot
• Owner: HaswinAI
• Created: 2025-07-18T09:11:46.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-07-19T06:58:40.000Z (11 months ago)
• Last Synced: 2025-07-19T11:26:51.550Z (11 months ago)
• Topics: faiss, langchain, llama3, rag-chatbot, streamlit
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📚 RAG Chatbot – PDF Q&A with LangChain + Groq

# A Retrieval-Augmented Generation (RAG) chatbot that answers questions from uploaded PDFs. Built using LangChain, Groq LLMs (LLaMA 3), FAISS, HuggingFace Embeddings, and Streamlit.

# 🧠 What This Project Does

🗂 Uploads a PDF document

📖 Reads and chunks the content into manageable parts

🔢 Converts chunks into embeddings using HuggingFace models

💾 Stores embeddings in a FAISS vector store

❓ Accepts user queries and retrieves top-matching chunks

🧠 Sends the context to Groq’s LLaMA 3 model to generate an answer

💬 Displays accurate, document-based responses in real-time

# 🔁 Workflow

PDF Upload

 → Text Extraction

   → Chunking

     → Embedding (HuggingFace)

       → FAISS Vector Store

User Query

 → Embedding

   → Search FAISS for Relevant Chunks

     → Generate Answer with Groq LLM

🧰 Tech Stack

Tool	Role

🦜 LangChain	LLM and retrieval framework

🧠 Groq	LLM inference (e.g. llama3-70b-8192)

🧠 HuggingFace	Embedding model (all-MiniLM-L6-v2)

📚 FAISS	Vector search for chunk similarity

📑 PyPDF2	PDF text extraction

⚡ Streamlit	UI to upload and chat

🔐 Dotenv	Secure API key handling

📁 Project Structure

rag-chatbot/

├── app.py              # Streamlit UI

├── rag_chain.py        # Retrieval and LLM logic

├── utils.py            # PDF reading and chunking

├── requirements.txt    # Required packages

├── .env                # API keys (not pushed)

├── data/               # Uploaded PDFs (ignored)

└── README.md           # You are here

🛠 Installation

Clone this repo:

git clone https://github.com/HaswinAI/RAG-chatbot.git

cd rag-chatbot-groq

Create a virtual environment:

python -m venv chatbot

source chatbot/bin/activate  # or chatbot\Scripts\activate on Windows

Install requirements:

pip install -r requirements.txt

Set up .env:

GROQ_API_KEY=your_actual_groq_key_here

▶️ Run the App

streamlit run app.py

Go to localhost in your browser.

💬 Example Use

Upload a whitepaper or article PDF

Ask: "What are the key takeaways?"

Get an instant, AI-generated summary based on the real content

📌 Notes

PDF contents are not stored permanently

.env, .pdf files, and data/ are in .gitignore

Compatible with models like:

llama3-70b-8192

llama3-8b-8192

gemma-7b-it

mixtral-8x22b

📄 License

This project is released under the MIT License

✨ Acknowledgements

LangChain

Groq

FAISS by Meta

HuggingFace Sentence Transformers

Streamlit

# Creator - HASWIN