RAGAS Demo 🚀

A lightweight Retrieval-Augmented Generation (RAG) library built with Azure OpenAI and ChromaDB.

This package allows you to query your own vector database using natural language and generate accurate, context-aware responses.

✨ Features

• 🔍 Semantic search using vector embeddings
• ⚡ Fast retrieval with ChromaDB
• 🤖 Answer generation via Azure OpenAI
• 🔐 User-controlled API keys (no key storage)
• 📦 Easy to install via pip

📦 Installation

pip install ragas_testing

🚀 Quick Start

from ragas_test import RAG

rag = RAG(
    api_key="YOUR_AZURE_API_KEY",
    endpoint="YOUR_AZURE_ENDPOINT",
    chat_deployment="YOUR_CHAT_DEPLOYMENT",
    embedding_deployment="YOUR_EMBEDDING_DEPLOYMENT",
    db_path="./chroma_db"   # Path to your vector DB
)

response = rag.ask("What is RAG?")
print(response)

🧠 How It Works

1. User question is converted into embeddings
2. Relevant documents are retrieved from ChromaDB
3. Context is passed to Azure OpenAI
4. LLM generates a grounded response

📁 Requirements

• Python 3.8+
• Azure OpenAI account
• Pre-built ChromaDB vector store

⚠️ Important Notes

• The embedding model used for queries must match the one used to build the ChromaDB
• This package does not store API keys
• All requests are processed using user-provided credentials
• Ensure your chroma_db directory exists before running

🔐 Security

• No API keys are stored internally
• No external data is logged
• Users retain full control over their data and credentials

🛠️ Future Improvements

• CLI support
• Auto vector DB creation
• Remote vector DB integration
• Caching for faster responses

👨‍💻 Author

Mannat Sharma