# ANNex
> Rust-native vector search engine with hybrid dense-sparse retrieval and multivector support.

## What it is
ANNex is an embeddable vector search engine written in Rust. It implements HNSW for approximate nearest neighbor search with payload filtering, BM25/sparse retrieval, hybrid dense-sparse fusion (RRF), multivector/ColBERT-style late interaction, snapshots, and a write-ahead log.

## Capabilities
- Dense vector search (cosine, dot product, Euclidean)
- Sparse / BM25 retrieval
- Hybrid dense-sparse fusion (RRF)
- Multivector / late-interaction (ColBERT)
- Payload filters on arbitrary key-value metadata
- Embedded Rust library (no server required)
- HTTP server (annex-multivector)
- Python bindings (ANNexDB on PyPI)
- Snapshot persistence and WAL replay
- SQ8 and PQ quantization

## What it does NOT do (v0.2.0)
- No managed cloud or hosted service
- No distributed or sharded mode
- No GPU acceleration

## Install
Rust:  cargo add annex   (crate: annex v0.2.0)
Python: pip install ANNexDB

## Benchmark headline (NYT256, 290K docs, 256-D angular, EC2)
- 0.18ms p50 latency at 0.836 recall, 5424 QPS (m16+rcm+sq8, ef=32, single thread)
- Beats hnswlib on the recall-latency Pareto frontier up to ~0.97 recall
- BEIR quality: macro nDCG@10 = 0.440 (hybrid), parity with Qdrant and LanceDB

## Version
0.2.0

## License
Apache-2.0 OR MIT

## Links
- Source: https://github.com/rosharma719/ANNex
- Docs: https://docs.rs/annex
- Benchmarks: https://annexsearch.com/benchmarks
- Benchmark policy: https://github.com/rosharma719/ANNex/blob/main/BENCHMARK_POLICY.md