Multilingual embedding model supporting over 100 languages and ranking #1 on MTEB multilingual leaderboard. Offers flexible model sizes from 0.6B to 8B parameters with user-defined instructions.

sqlite-vec is a Rust-based SQLite extension library for vector similarity search using diskANN indexes on embeddings, enabling lightweight ANN without separate databases. Features HNSW-like graphs, quantization support, and hybrid full-text+vector queries in embedded SQLite environments. Perfect for prototyping and on-device apps; extremely lightweight compared to Milvus, more persistent than pure hnswlib.

ClickHouse is a columnar OLAP database with vector indexes (ANN via AMM, brute-force), supporting SQL queries over vectors + structured data at petabyte scale. Excels in aggregations with vectors. For analytics workloads with embeddings; faster ingestion than Postgres pgvector for big data.

Open-source embeddings database for semantic search, workflows, and AI applications with vector storage and retrieval capabilities.

Production-ready ONNX embedding generation in pure Rust using ONNX Runtime, no Python required. Supports 8+ pretrained models including all-MiniLM-L6-v2, BGE, E5, GTE with pooling strategies and GPU acceleration (CUDA, TensorRT, CoreML, WebGPU). Enables direct integration with RuVector indices for RAG pipelines and semantic similarity computation.

Multimodal embedding model with 137M parameters that outperforms OpenAI text-embedding-3-small on both short and long context tasks. Features Matryoshka Representation Learning for flexible embedding dimensions.