World's fastest and most compact embedded vector database: exact by default, multimodal, local-first, and GPU-accelerated
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Updated
Aug 13, 2026 - Python
World's fastest and most compact embedded vector database: exact by default, multimodal, local-first, and GPU-accelerated
⚡ Super fast clustering for high-dimensional vectors on CPUs (x86, ARM) and GPUs — for Python and C++. Faster clustering of vector embeddings than FAISS
Browser only AV1, HEVC Parser GUI
Pixel-native visual RAG ported to Rust on the ruvector ANN substrate (HNSW + IVF-Flat) — screenshot/document retrieval over visual embeddings, a Rust port of PixelRAG, with a metaharness benchmark CLI: npx rupixel
visualization tool for vector search index
A tiny approximate K-Nearest Neighbour library in Python based on Fast Product Quantization and IVF
PostgreSQL TurboQuant Index for PGVector
Vector search library built from scratch with exact and approximate nearest-neighbor indexes. Includes Flat, IVF, HNSW, Product Quantization, and K-Means implementations.
A semantic search indexing system designed to efficiently retrieve top matching results from a database of 20 million documents. Given the embedding of a search query, it quickly identifies and returns the most relevant documents
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
A machine learning system for identification of ovarian response and deployment of ovarian stimulation strategies in ART
Compact LLM domain adaptation benchmark: LoRA vs RAG with custom IVF retrieval in PostgreSQL/pgvector.
High-performance embedded vector database for Rust. HNSW, IVF, and flat indexes with first-class metadata filtering. In-process, persistent, zero-network similarity search.
Meta-analysis of DNA methylation in ART
High-performance ANN vector search engine in modern C++ implementing HNSW, IVF, PQ, SQ8, IVF-SQ, and IVF-PQ with SIFT1M benchmarking.
A fully dynamic approximate nearest neighbor index built on Inverted File Index
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