autonomize-autorag 0.1.3

AutoRAG is a flexible and scalable solution for building Retrieval-Augmented Generation (RAG) systems.

AutoRAG

Powering seamless retrieval and generation workflows for our internal AI systems

Overview

AutoRAG is a flexible and scalable solution for building Retrieval-Augmented Generation (RAG) systems.

This SDK provides out-of-the-box functionality for creating and managing retrieval-augmented generation workflows, offering a modular, highly-configurable interface. It supports multiple vector stores and leverages http clients like httpx for handling requests, ensuring seamless integration.

Features

• Modular architecture: The SDK allows you to swap, extend, or customize components like retrieval models, vector stores, and response generation strategies.
• High scalability: Built to handle large-scale data retrieval and generation, enabling robust, production-ready applications.
• Celery for dependency injection: Efficient background tasks with support for distributed task execution.
• Multi-flow support: Easily integrate various vector databases (ex: Qdrant, Azure AI Search) with various language models providers (ex: OpenAI, vLLM, Ollama) using standardized public methods for seamless development.

Installation

1. Create a virtual environment, we recommend Miniconda for environment management:

   conda create -n autorag python=3.12
   conda activate autorag
   

2. Install the package:

   pip install autonomize-autorag
   

Usage

The full set of API can be found in api.md

import os
from autorag.language_models import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
)

generation = client.generate(
    message="What is GPT?"
    model="gpt-4o"
)

Contribution

To contribute in our AutoRAG SDK, please refer to our Contribution Guidelines.

License

Copyright (C) Autonomize AI - All Rights Reserved

This file is part of this project.

This project can not be copied and/or distributed without the express permission of Autonomize AI.