Hey PH folks, this is Chirotpal, and I created SwarnDB. SwarnDB is a vector database written in Rust that does not stop at nearest-neighbor search. It computes the relationships between your vectors automatically and exposes them as a graph you can traverse. So you get vector search, graph traversal, and 15+ vector math operations (k-means, PCA, SLERP interpolation, cone search, drift detection, maximal marginal relevance, ghost-vector detection, and more) in one engine, instead of stitching three databases together. What's in this release (v1.0.3): - 2,398 queries per second at 98% recall on DBpedia 1M (1536 dim) with default HNSW parameters on a 32-core box, 8 concurrent searcher threads. Sub-7ms p99. - File-based bulk ingestion: stage a `.npy` or flat `.f32` file on disk, point the server at it, and the server memory-maps it directly. Working memory stays bounded by the index being built, not by the input file size. Loading a million vectors no longer balloons your container. - Plain HNSW collections become queryable within seconds of the server opening its ports. - Multi-collection databases load collections in parallel at startup. - Transparent crash recovery: incremental delta replay if a snapshot exists, full write-ahead log replay otherwise, both happen automatically before traffic resumes. - Operational endpoints for Kubernetes orchestrators: /healthz, /readyz, /startupz, plus a global /recovery_status and per-collection /persistence_status. - Async and sync Python clients with identical method names and return types. - Bulk inserts produce checkpoints and a resume token so interrupted loads pick up from the last committed batch. - Multi-arch Docker image (linux/amd64 + linux/arm64) on Docker Hub. - Pure-Python wheel on PyPI for Linux x86_64, Linux ARM64, macOS Apple Silicon, and Windows x86_64. Try it in 30 seconds: - `pip install swarndb` - `docker run -d -p 8080:8080 -p 50051:50051 sarthiai/swarndb` Source: github.com/SarthiAI/SwarnDB Benchmarks: github.com/SarthiAI/SwarnDB/blob/main/docs/benchmarks.md License: Elastic License 2.0 (free for production use). I will be in the thread all day. Would love to hear: what are you building with vector search today, and where does the lack of a relationship/graph layer hurt? Also happy to dive into the benchmark methodology, the persistence model, or the SDK design if anyone wants the deep end.

SwarnDB

Vector database that thinks in graphs

Vector database that thinks in graphs