This package will help you talk to your data using Retrieval Augmented Generation (RAG).
Project description
simplerag (Simple Retrieval Augmented Generation)
Simplerag: Retrieval Augmented Generation Library Simplerag is a Python library designed to assist users in working with custom data using Large Language Models (LLMs). It enables users to create a vector database, which is a one-time activity, and LLM models can then utilize the product outputs for retrieval augmented generation.
Table of Contents
- Introduction
- Installation
- Usage
- Creating a Vector DB
- Retrieval Augmented Generation
- Examples
- Contributing
- License
Introduction
Large Language Models (LLMs) have gained significant popularity in recent years due to their ability to generate human-like text. However, working with custom data using LLMs can be challenging, especially when dealing with large datasets or complex retrieval tasks. Simplerag aims to simplify this process by providing a convenient and efficient way to work with LLMs for retrieval augmented generation tasks.
Key features of simplerag include:
Vector Database Creation: simplerag allows users to create a vector database from their custom data. This database serves as an index for retrieval tasks and is generated as a one-time activity.
Retrieval Augmented Generation: With the vector database in place, Simplerag enables users to perform retrieval augmented generation using LLM models. This approach combines the power of retrieval and generation to produce high-quality outputs.
Installation To install simplerag, you can use pip:
pip install simplerag
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