Embeddings are numerical representations of data that capture semantic meaning for AI and machine learning systems. They convert data such as text, images, or audio into numerical vectors, allowing AI systems to measure similarity, understand context, and power applications such as semantic search, recommendation systems, clustering, and Retrieval-Augmented Generation (RAG).
A vector index built on TurboQuant, written in Rust with Python bindings
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..
Postgres with GPUs for ML/AI apps.
High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale
A blazing fast inference solution for text embeddings models
Semantic search and document parsing tools for the command line
Korvus is a search SDK that unifies the entire RAG pipeline in a single database query. Built on top of Postgres with bindings for Python, JavaScript, Rust and C.
Rust library for generating vector embeddings and reranking locally!
PostgreSQL vector database extension for building AI applications
Believe in AI democratization. llama for nodejs backed by llama-rs, llama.cpp and rwkv.cpp, work locally on your laptop CPU. support llama/alpaca/gpt4all/vicuna/rwkv model.
Embedded relational database and native Rust data API.
A prior-art search for your code ideas — has this dev tool already been shipped?
GraphRAG-rs is a high-performance, state-of-the-art Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) that builds knowledge graphs from documents and enables natural language querying with configurable entity extraction and local LLM integration
Structural code intelligence for AI agents — semantic search, knowledge graphs, and a built-in MCP server in one Rust binary. Give Claude, Cursor, and any MCP client a deep understanding of your codebase.
A tiny embedding database in pure Rust.
Build production-ready backends in minutes
Rust bindings to https://github.com/k2-fsa/sherpa-onnx
🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.
A Redis-compatable key-value store. Up to 10x faster. Native vector support.
🧠 The Brain for Your AI — Local-first memory engine for AI agents. Store, recall, and search memories with semantic embeddings. Single Rust binary, zero config, fully offline.