PgANN Approximate Nearest Neighbor Searches with PostgreSQL Back End

We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for…

Smarter search that gives AI agent richer data at lower cost

I made this website with my wife in mind; it makes it possible to browse for similar fashion products over many different retailers at once. The backend is written in Swift, and is hosted on a single Mac Mini. It performs nearest neighbors on the GPU over ~3M product images. No vector DB, just pure matrix multiplications. Since we aren't just doing approximate nearest neighbors but rather sorting all results by distance, it's possible to show different "variety" levels by changing the stride over the sorted search results. Nearest neighbors are computed in a latent vector space. The model…

Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too. The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our…

HNSW implementation in Rust. Reference: https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf - swapneel/hnsw-rust