osobhandClaude Opus 5
c0a9206703
`MemoryConfig::quantized_index` stores the HNSW index's own copy of the embeddings as i8 rather than f32. At 100k x 384 that takes the index from 266 to 123 MiB and the whole reopened store from 399 to 256 MiB — 2.72x to 1.74x the raw vectors, the largest remaining item in the footprint. Quantised distances are approximate and `ef` cannot compensate, because the loss is in the distances rather than in the graph: recall@10 tops out at 0.967 against f32's 0.9995 and does not move between ef=128 and ef=256. The store already holds the exact embeddings, though, so when the index is quantised the query path re-scores the candidate pool against them before fusion. That restores recall (0.9940 vs 0.9945 at ef=64) and costs about 13% of QPS. Off by default: it trades query speed for memory and which side is worth more depends on the deployment. The flag is persisted in `/meta`, so a reopened store does not silently revert to four times the index memory, and the sidecar graph is rehydrated into the configured storage. Also on the CLI as `create --quantized-index`. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
clawhdf5-agent
HDF5-backed persistent memory store for on-device AI agents.
Built on clawhdf5, clawhdf5-agent provides a vector-searchable memory backend optimized for edge AI workloads. Store embeddings, text chunks, and metadata in a single HDF5 file with SIMD-accelerated similarity search.
Features
- Persistent vector store in HDF5 format
- Cosine similarity and L2 distance search
- SIMD-accelerated via clawhdf5-accel (AVX2, NEON)
- Optional GPU acceleration via clawhdf5-gpu
- Memory-mapped access for large stores
- f16 storage support for compact embeddings
Usage
[dependencies]
clawhdf5-agent = "2.1.0"
License
MIT