osobhandClaude Opus 5
e5e087f9ab
Graph degree and the build- and query-time candidate list sizes were constants, so a deployment had no way to trade recall against memory or query speed. They are now `MemoryConfig::hnsw_m`, `hnsw_ef_construction` and `hnsw_ef_search`, persisted with the store and defaulting to exactly the previous behaviour (16, 64, and a query list that scales with `k`). Two things the straightforward version would have got wrong: `clawhdf5-ann` asserts a graph degree of at least 2, so a configured 0 — from a file, or from a caller reading 0 as "use the default" — aborted the process inside the index builder. The store clamps instead, and a test covers it: removing the clamp makes that test panic rather than fail. `ef_search` and the candidate pool handed to score fusion were the same number. Tying the pool to the new setting would mean lowering `ef` for speed also narrows what fusion sees, quietly degrading hybrid results through a knob that looks like it only costs time. They are now independent. 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