Open source repositories tagged with #embeddings, ranked by health score.
A vector index built on TurboQuant, written in Rust with Python bindings
SeaTunnel is a multimodal, high-performance, distributed, massive data integration tool.
The Ruby-native AI framework. Chats, agents, tools, images, audio, and video through one consistent API, in plain Ruby or Rails.
AI memory and a knowledge graph in one SQLite file. Pure Go: vectors, RAG, agent memory, RDF/SPARQL, Cypher, 80+ MCP tools. Works without an embedding model.
LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.
A python library for self-supervised learning on images.
High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale
Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..
The Harmony Python library: a research tool for psychologists to harmonise data and questionnaire items. Open source.
An open-source, local-first CV & resume management platform featuring privacy-focused semantic search to instantly match your skills, projects, and experiences
