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

Refine high-quality datasets and visual AI models

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

Semantic cache for LLMs. Fully integrated with LangChain and llama_index.

Resume Matcher is an open source, free tool to improve your resume. It works by using language models to compare and rank resumes with job descriptions.

Superduper: Build end-to-end AI applications and agent workflows on your existing data infrastructure and preferred tools - without migrating your data.

Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai

🧠 AI-powered enterprise search engine 🔎

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

Production ready AI agent framework

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

Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️

Python client for Qdrant vector search engine

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.

NucliaDB, The AI Search database for RAG

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

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

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite

Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577

Neural Search