LlamaIndex is a data framework for your LLM applications

Personalizing LLM Responses

Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

Ship RAG based LLM web apps in seconds.

local-first semantic code search engine

Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone

ChatWeb can crawl web pages, read PDF, DOCX, TXT, and extract the main content, then answer your questions based on the content, or summarize the key points.

Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.

An LLM-powered advanced RAG pipeline built from scratch

Python client for Qdrant vector search engine

LLMFlows - Simple, Explicit and Transparent LLM Apps

Use ArXiv ChatGuru to talk to research papers. This app uses LangChain, OpenAI, Streamlit, and Redis as a vector database/semantic cache.

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

A Python vector database you just need - no more, no less.

A lightweight library that leverages Language Models (LLMs) to enable natural language interactions, allowing you to source and converse with data.

AI Chatbot for analyzing/extracting information from data in conversational format.

Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.

Embedding Studio is a framework which allows you transform your Vector Database into a feature-rich Search Engine.