autonomize-autorag 0.1.14

Description:

autonomizeautorag 0.1.14

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

Create a virtual environment, we recommend Miniconda for environment management:
conda create -n autorag python=3.12
conda activate autorag


Install the package:
pip install autonomize-autorag



To install with optional dependencies like Qdrant, Huggingface, OpenAI, Modelhub, etc., refer to the Installation Guide.
Usage
The full set of examples can be found in examples directory.
Sync Usage
import os
from autorag.language_models.openai import OpenAI

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

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

Async Usage
Simply use sync methods with a prefix and use await for each call. Example: client.generate(...) becomes await client.agenerate(...) and everything else remains the same.
import os
from autorag.language_models.openai import OpenAI

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

generation = await client.agenerate(
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.