osobhandClaude Fable 5.1
390a2e3836
Fusion min-max normalises over every keyword match, so hybrid_search asked BM25 for a ranked list of the whole corpus: a hash insert per posting, then a sort of every match, then the merge sorted every candidate again to keep k. - BM25Index::scores returns every match unsorted, accumulated in a dense array (contributions are strictly positive, so zero means untouched). search() is built on it with the bounded heap. - merge_vector_keyword partitions out its top k (select_nth) and orders only those, with the same score-then-id order. - Both hybrid paths use scores(). Rankings are identical (equivalence tests for both changes). p50 0.24 -> 0.07 ms (1K), 2.1 -> 0.49 ms (10K), 23 -> 4.65 ms (100K). The harness gains --fusion-study, which measured the alternative — capping the keyword pool — and found it changes the top-10 for most queries (overlap 0.83-0.92, different #1 for 10-35%) for only a 2x saving. Not adopted. 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