- name
- vector-search
- description
- 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
- argument-hint
- <query> [--limit N] [--quantized]
- allowed-tools
- mcp__claude-flow__embeddings_generate mcp__claude-flow__embeddings_search mcp__claude-flow__embeddings_compare mcp__claude-flow__embeddings_init mcp__claude-flow__embeddings_status mcp__claude-flow__embeddings_hyperbolic mcp__claude-flow__embeddings_neural mcp__claude-flow__embeddings_rabitq_build mcp__claude-flow__embeddings_rabitq_search mcp__claude-flow__embeddings_rabitq_status mcp__claude-flow__ruvllm_hnsw_create mcp__claude-flow__ruvllm_hnsw_add mcp__claude-flow__ruvllm_hnsw_route mcp__claude-flow__memory_search_unified Bash
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 | 150×–12,500× faster than brute-force, depending on N and parameters |
| 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 |
Standard search
- Check status —
mcp__claude-flow__embeddings_statusto verify the embedding engine. - Initialize —
mcp__claude-flow__embeddings_initif not active. - Generate —
mcp__claude-flow__embeddings_generatefor text input. - Search —
mcp__claude-flow__embeddings_searchwith the query. - Compare —
mcp__claude-flow__embeddings_compareto measure similarity. - Unified search —
mcp__claude-flow__memory_search_unifiedfor cross-namespace.
Quantized search (32× memory reduction)
For corpora ≥5,000 vectors and/or memory-constrained environments, use the RaBitQ 1-bit quantization workflow. Below 5,000 vectors the rebuild cost outweighs the savings — use the standard path instead.
| Step | Tool | Purpose |
|---|---|---|
| 1 | embeddings_init | Engine warm |
| 2 | embeddings_rabitq_build | One-time build of the 1-bit index after corpus is loaded |
| 3 | embeddings_rabitq_search | Hamming-prefilter returns top-N candidate IDs (cheap) |
| 4 | embeddings_search | Optional exact rerank on the candidate set (full-precision) |
| 5 | embeddings_rabitq_status | Index health, memory footprint, build time |
Note:embeddings_rabitq_searchreturns candidate IDs only — the rerank in step 4 is the user's responsibility (mirrors the docstring atembeddings-tools.ts:911). Without rerank, results are approximate; with rerank, you get full-precision quality at 32× lower memory.
Tuning
HNSW exposes three knobs that trade recall against latency. The "12,500×" headline assumes defaults; tune deliberately for your workload:
| Profile | efSearch | M | When to use |
|---|---|---|---|
recall-first | 200 | 32 | Pattern recall during planning; quality matters more than ms |
balanced (default) | 64 | 16 | General-purpose semantic recall |
latency-first | 16 | 8 | Hot-path routing where p99 latency matters |
efSearch is passed via ruvllm_hnsw_create (ruvllm-tools.ts:64). M is registry-level today; raise as a follow-up if it should be MCP-tunable. efConstruction defaults to 200 in the lite index (hnsw-index.ts:537).
HNSW pattern router (WASM, ≤11 patterns)
For routing a small number of high-priority patterns:
mcp__claude-flow__ruvllm_hnsw_create— create the WASM index (cap ~11)mcp__claude-flow__ruvllm_hnsw_add— add a patternmcp__claude-flow__ruvllm_hnsw_route— route an incoming query
This is not a corpus index. Treat it as a fast classifier over a curated set of patterns.
Hyperbolic embeddings
For hierarchical data (code trees, org charts), use mcp__claude-flow__embeddings_hyperbolic which maps to Poincare ball space. Distance is geodesic, not cosine.
CLI alternative
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"Performance
| Method | Speed |
|---|---|
| Brute-force scan | Baseline |
| HNSW (n=500, balanced) | ~150× faster |
| HNSW (n=10,000, balanced) | ~12,500× faster |
| RaBitQ + rerank (n=10,000) | ~12,500× search speed at 32× lower memory |
ruvllm_hnsw_route (n≤11) | sub-ms per route, fixed cost |