Fast, Accurate, Lightweight Python library to make State of the Art Embedding

⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡

The framework for fast development and deployment of RAG systems.

MTEB: Massive Text Embedding Benchmark

A scalable realtime and continuous indexing and structured extraction engine for Unstructured Data to build Generative AI Applications

Text Embedding for Retrieval, Rerank and RAG

The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Atmospheric data Community Toolkit - A python based toolkit for exploring and analyzing time series atmospheric datasets

Palladian is a Java-based toolkit with functionality for text processing, classification, information extraction, and data retrieval from the Web.

A Bayesian Nested-Sampling Retrieval Code

Adaptive RAG with self-reflection and external tools

Library to generate vector embeddings, reranking. Based on Qdrant's FastEmbed.

Epsilla is a high performance Vector Database Management System. Try out hosted Epsilla at https://cloud.epsilla.com/

Customizable Case-Based Reasoning (CBR) toolkit for Python with a built-in API and CLI.

Cottontail DB is a column store vector database aimed at multimedia retrieval. It allows for classical boolean as well as vector-space retrieval (nearest neighbour search) used in similarity search using a unified data and query model.

OpenAI 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 Python OpenAI API 를 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.

advanced concepts of data, storage, organization, and retrieval. Topics include multiple-linked lists, balanced trees, graphs, abstract data types, classes and methods, object-oriented programming, searching and sorting.

Apache Lucene.NET

Work in progress. An llm util to work as an evaluation step in RAG applications

GRAG is a simple python package that provides an easy end-to-end solution for implementing Retrieval Augmented Generation (RAG). The package offers an easy way for running various LLMs locally, Thanks to LlamaCpp and also supports vector stores like Chroma and DeepLake.