vector-search
Vector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction
Profile →npx skills add ruvnet/ruflo --skill vector-search --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Vector Search Two distinct vector-search paths live in this plugin. Pick the right one — they're not interchangeable. | Path | Tool family | Backing | Capacity | Latency | |------|-------------|---------|----------|---------| | **Large-scale corpus** | `embeddings_*` | `@claude-flow/memory` HNSW (Rust/Native) | up to millions of vectors | ~1.9× at N=20k, ~3.2×–4.7× at N=5k vs brute-force (measured; recall@10 ≈ 0.99). ANN wins above the crossover | | **Hot-path router** | `ruvllm_hnsw_*` | WASM-backed router (v2.0.1) | **~11 patterns max** (`ruvllm-tools.ts:58`) | sub-ms; designed for high-priority routing, not corpus search | The "12,500×" headline applies to the large-scale `embeddings_search` path. The WASM router is **not** that path. ## When to use | Need | Path | |---|---| | Search a corpus of N ≥ 500 documents | `embeddings_search` | | Memory-constrained corpus (≥5,000 vectors) | RaBitQ quantized — see "Quantized search" below | | Compare two strings | `embeddings_compare` | | Hierarchical / taxonomic data | `embeddings_hyperbolic` (Poincare ball) | | Route a query to one of ≤11 hot patterns | `ruvllm_hnsw_route` | | Cross-namespace search | `memory_search_unified` | ## Sta
- When to use
- Standard search
- Quantized search (32× memory reduction)
- Tuning
- HNSW pattern router (WASM, ≤11 patterns)
- Hyperbolic embeddings
- CLI alternative
- Performance
npx @claude-flow/cli@latest embeddings search --query "authentication patterns" npx @claude-flow/cli@latest embeddings init npx @claude-flow/cli@latest memory search --query "your query"
What does the vector-search skill do?
Vector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction
How do I install it?
Run `npx skills add ruvnet/ruflo --skill vector-search --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.