osobhandClaude Opus 5.5
c470244a6f
`HDF5Memory::search(query_embedding, query_text, &SearchOptions)` is the store's full search path. `SearchOptions::new(k)` is plain hybrid search with the tuned default fusion; each further stage is opt-in: - `with_sources([..])`: only records from these source channels. The filter applies before ranking, so a filtered search still returns up to k results, normalised over what it can return. The HNSW pool is over-fetched in proportion to what the filter removes, and the allowed records are scanned exactly whenever that costs fewer distance evaluations than the index would (~pool x M) — and as the fallback if the pool comes back short. Keyword matches are filtered too. - `with_rerank(ReRankConfig)` re-ranks a max(3k, 10) candidate pool by relevance, recency, source authority and activation; `with_confidence(ConfidenceConfig)` drops low-confidence results; `at_time(now)` pins the recency clock. These were reachable only through the OpenClaw backend, which is now `search` with both on. Its Hebbian boost now goes to the k results it returns rather than the whole 3k candidate pool. `hybrid_search` and `hybrid_search_with` are wrappers and unchanged (tested bit for bit). Measured on tank (search_harness --options-study --full, 3 runs): at 100K every filter — 50%, 10%, 1% of the store, and records far from the query — returns the exact filtered top 10, and none is slower than an unfiltered search (1%: 2.3 ms vs 4.6 ms). Re-rank + confidence costs about 3%. A first version decided between index and exact scan by pool size vs store size; it measured 0.976 recall at 12.3 ms on the far-from-query filter, which is why the rule compares costs instead. Tests: tests/search_options.rs (filter correctness and full pages via both paths, far-from-query fallback, edge cases, equality with hybrid_search_with, re-rank recency, confidence, boost scope). Co-Authored-By: Claude Opus 5.5 (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