🤖 RAG-Chatbot

RAG-Chatbot is an intelligent, LLM-powered chatbot with integrated Retrieval-Augmented Generation (RAG) functionality. It enables contextual, knowledge-grounded conversations powered by FastAPI, Hugging Face Transformers, Mistral-7B, and ChromaDB.

---

🚀 Features

• ✅ RAG Integration: Combines document retrieval via ChromaDB with language generation from Mistral-7B.
• 💬 Chat API: Interact with the bot via a simple RESTful API.
• ⚡ Mistral-7B: Generates coherent, high-quality language responses.
• 🔧 FastAPI Backend: High-performance API server for LLM inference.
• 🧠 Transformers: Uses Hugging Face Transformers for model management.
• 📚 ChromaDB: Embedded vector store for fast and efficient retrieval.

---

🛠️ Tech Stack

---

🧪 Getting Started

Prerequisites

• Python 3.8+
• Docker & Docker Compose (for containerized deployment)
• GPU (recommended) or CPU with sufficient memory
• Ollama (recommended for faster responses)

Run Directly

git clone https://github.com/your-username/RAG-Chatbot.git
cd RAG-Chatbot
pip install -r requirements.txt
uvicorn app:app --reload

Run using Docker

git clone https://github.com/your-username/RAG-Chatbot.git
cd RAG-Chatbot
docker build -t rag_chatbot .
docker run -p 7860:7860 rag_chatbot

Note

The deployed version of this app does not use ollama and Mistral-7B instead microsoft phi-2 model is used via llama-cpp-python library due to platform constraints.
The deployed code is given with file name run_model_hf.py. Migration to Microsoft Azure for better performance and less constraints is underway.