osobhandClaude Fable 5.1
c19199f3eb
Profiling the build showed 90% of all distance evaluations are in back-link pruning (40.8M of 44.9M at 10K): every overflow re-runs the diversity heuristic pairwise over ~max_conn candidates. The bulk build now inserts in batches: plan every node's neighbours against the graph as it stood when the batch began (read-only, so plans are independent), link, then prune each overflowing list once. A node gaining several back-links in a batch is pruned once rather than once per link, so this is faster even single-threaded (10K: 1676 -> 1074 ms). With `parallel`, planning and pruning use rayon (10K: 388 ms; 100K: ~21 s -> 5.9 s on 16 cores). Batches start at one node and are capped at 1/16 of the linked graph and 512 nodes; a node that raises the top layer gets a batch to itself. The result is deterministic and identical with or without the feature (one code path; test compares two builds byte for byte). Parallelising within a single insert was tried first: 1.45x on 16 cores, tasks too small. Incremental insert() stays sequential. Recall on clustered data is unchanged or slightly better; uniform random data dips slightly (10K, ef=64: 0.474 -> 0.444). clawhdf5-agent's `parallel` feature now passes through to the index. Co-Authored-By: Claude Fable 5.1 <[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