📘 Retrieval-Augmented Generation (RAG) Chatbot

A modular Retrieval-Augmented Generation (RAG) system built with LangChain, HuggingFace, and FAISS.
This chatbot ingests documents, stores them in a vector database, retrieves the most relevant chunks, and generates context-aware answers.

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🚀 Features

• Document ingestion pipeline (load, split, embed, store)
• FAISS vector store for efficient similarity search
• Retriever module to fetch relevant chunks
• Generator module powered by HuggingFace models (e.g., flan-t5-base)
• Interactive CLI chatbot with persistent FAISS index
• Modular design for easy extension and scaling

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🗂️ Project Structure

rag-chatbot/
│
├── ingestion/
│   ├── loader.py          # Load documents
│   ├── splitter.py        # Split into chunks
│   ├── embedder.py        # Embedding manager
│   └── vector_store.py    # FAISS vector store wrapper
│
├── rag/
│   ├── retriever.py       # Retrieve chunks & build prompt
│   └── generator.py       # Generate answers with HuggingFace
│
├── data/                  # Sample documents
├── main.py                # Entry point (chatbot loop)
├── requirements.txt       # Dependencies
├── test_rag.py            # Quick validation script
└── README.md              # Project overview

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⚙️ Installation

Clone the repo and install dependencies:

git clone https://github.com/<your-username>/rag-chatbot.git
cd rag-chatbot
pip install -r requirements.txt

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▶️ Usage

1. Place your documents in the data/ folder.
2. Run the chatbot:

   python main.py

3. Ask questions interactively:

   Hi how may I help you? : What is retrieval-augmented generation?
   

Type <quit> to exit.

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🧪 Example

Query:

What is FAISS used for?

Response:

FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. In this RAG pipeline, it stores embeddings of document chunks and retrieves the most relevant ones for a given query.

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📊 Demo

• Add a short terminal screenshot or GIF here
• (Optional) Record a 1–2 min demo video and link it

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🔮 Future Improvements

• Web UI with Gradio/Streamlit
• Support for larger context models
• Evaluation metrics for retrieval quality
• Dockerized deployment

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🛠️ Tech Stack

• LangChain – orchestration
• HuggingFace Transformers – embeddings & generation
• FAISS – vector similarity search
• Python 3.13

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✅ Validation

To prove this works, run:

python test_rag.py

This script will:

• Ingest sample data
• Run a few queries
• Print responses

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👤 Author

Built by Amin — passionate about AI, NLP, and building intelligent systems.
Connect with me on LinkedIn or check out my other projects on GitHub.