Metadata-Version: 2.1
Name: simplerag
Version: 0.0.4
Summary: This package will help you talk to your data using Retrieval Augmented Generation (RAG).
Home-page: https://github.com/samarthsarin/simplerag/
Author: Samarth Sarin
Author-email: sarin.samarth07@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/samarthsarin/simplerag/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: transformers
Requires-Dist: accelerate
Requires-Dist: SentencePiece
Requires-Dist: bitsandbytes
Requires-Dist: accelerate (==0.26.1)
Requires-Dist: langchain (==0.0.166)
Requires-Dist: InstructorEmbedding
Requires-Dist: chromadb (==0.3.22)
Requires-Dist: typing-inspect (==0.8.0)
Requires-Dist: typing-extensions (==4.5.0)

# 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
1. Introduction
2. Installation
3. Usage
4. Creating a Vector DB
5. Retrieval Augmented Generation
6. Examples
7. Contributing
8. 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```



