🤖 RAG Chatbot – Retrieval-Augmented Generation for Smarter Responses

This project implements a powerful RAG (Retrieval-Augmented Generation) chatbot that merges traditional document search with language generation. It retrieves contextually relevant information from a knowledge base and passes it to a transformer-based model to provide highly accurate, human-like responses.

🚀 Features

• 🧠 Combines retrieval with generation for more accurate answers
• 📂 Ingests custom documents as a knowledge base
• 🔍 Uses vector embeddings to retrieve relevant context
• 🗨️ Provides GPT-style answers grounded in real data
• 🌐 Option to deploy as a local or web-based chatbot

🛠 Tech Stack

• Python 3
• Hugging Face Transformers (e.g., BERT, T5, or GPT2)
• FAISS / Chroma / Weaviate (Vector store)
• LangChain or Haystack (for RAG pipeline)
• Streamlit / Flask (UI option)
• SentenceTransformers (embeddings)

📦 Installation & Setup

1. Clone the Repository

```bash git clone https://github.com/CAPTAINCODERCOOL/rag-chatbot.git cd rag-chatbot 2. Create a Virtual Environment (Optional) bash Copy Edit python -m venv venv source venv/bin/activate # macOS/Linux venv\Scripts\activate # Windows 3. Install Dependencies bash Copy Edit pip install -r requirements.txt 4. Prepare Knowledge Base Add your .txt, .pdf, or .md files into the data/ directory.

The chatbot will parse and embed them for use in RAG.

▶️ Running the Chatbot Option 1: Console Chat bash Copy Edit python rag_chat.py Option 2: Streamlit Web Interface bash Copy Edit streamlit run app.py Open your browser and go to: http://localhost:8501

📂 Project Structure bash Copy Edit rag-chatbot/ ├── app.py # Streamlit frontend ├── rag_chat.py # CLI-based RAG chatbot ├── data/ # Your documents/knowledge base ├── retriever.py # Embedding & vector search logic ├── generator.py # Text generation module ├── requirements.txt └── README.md 🧠 How It Works Ingestion: Loads and splits documents.

Embedding: Encodes chunks using a sentence transformer.

Retrieval: Finds top-k relevant chunks using FAISS or Chroma.

Generation: Passes retrieved context + query to a language model.

Response: Returns a factual, grounded answer to the user.

📊 Example Use Cases Internal documentation Q&A bots

Academic research assistants

Technical support bots with grounding

HR/Policy query systems

💡 Future Improvements Integrate OpenAI/GPT-4 or Claude

Add memory (multi-turn support)

Summarize retrieved documents for shorter inputs

Deploy as a production API (FastAPI)

📜 License This project is licensed under the Apache License 2.0. Read more at: Apache 2.0 License

🌐 Connect with Me GitHub: CAPTAINCODERCOOL

LinkedIn: chiragpatil04

Email: chiragpatilprofessional@gmail.com