rag-engine 0.1.2

A Retrieval-Augmented Generation (RAG) Engine for managing embeddings and similarity search

RAG Engine

RAG Engine is a Python package for implementing Retrieval-Augmented Generation (RAG) using OpenAI's embeddings and a SQLite database with vector search capabilities.

Installation

You can install the RAG Engine package using pip:

pip install rag_engine

Usage

Here's a quick example of how to use RAG Engine:

import asyncio
from rag_engine import RAGEngine

async def main():
    # Initialize the RAG Engine
    rag = RAGEngine("database.sqlite")

    # Add some sentences
    sentences = ["This is a test sentence.", "Another example sentence."]
    rag.add(sentences)

    # Search for similar sentences
    results = rag.search("test sentence", n=2)
    print(results)

# Run the async function
asyncio.run(main())

Features

• Supports multiple OpenAI embedding models: ADA_002, SMALL_3, and LARGE_3
• Asynchronous operations for better performance
• SQLite database with vector similarity search
• Flexible API for adding, searching, and deleting embeddings

Development

To set up the development environment:

1. Clone the repository
2. Install the package with development dependencies:

   pip install -e .[dev]
   

3. Run the tests:

   pytest
   

Note: Tests require a valid OpenAI API key. Set the OPENAI_API_KEY environment variable before running the tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.