vector-search
Vector search via embeddings (large-scale HNSW) and ruvllmhnsw (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/ruvnet/ruflo --skill vector-search- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads vector-search automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 |
Standard search
- Check status —
mcp__plugin_ruflo-core_ruflo__embeddings_statusto verify the embedding engine. - Initialize —
mcp__plugin_ruflo-core_ruflo__embeddings_initif not active. - Generate —
mcp__plugin_ruflo-core_ruflo__embeddings_generatefor text input. - Search —
mcp__plugin_ruflo-core_ruflo__embeddings_searchwith the query. - Compare —
mcp__plugin_ruflo-core_ruflo__embeddings_compareto measure similarity. - Unified search —
mcp__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__ruvllm_hnsw_create— create the WASM index (cap ~11)mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add— add a patternmcp__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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
Measured numbers (source: scripts/benchmark-intelligence.mjs, ruvector NAPI backend; recall@10 ≈ 0.99). The older "150×–12,500×" figures were brute-force-fallback artifacts and have been retired — see project CLAUDE.md "V3 Performance Targets".
| Method | Measured speedup vs brute-force |
|---|---|
| Brute-force scan | Baseline |
| HNSW (N=5,000) | ~3.2×–4.7× faster |
| HNSW (N=20,000) | ~1.9× faster |
| HNSW (below crossover, small N) | ties/loses vs brute-force |
| RaBitQ quantization | 32× memory reduction; 0.60 ms/query at N≈14.7k |
ruvllm_hnsw_route (n≤11) | sub-ms per route, fixed cost |
FAQ
What does the vector-search skill do?
Vector search via embeddings (large-scale HNSW) and ruvllmhnsw (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction
How do I install vector-search?
Run npx skills add https://github.com/ruvnet/ruflo --skill vector-search in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/vector-search/ (personal) or .claude/skills/vector-search/ (project).
Which AI agents can use vector-search?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is vector-search safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains vector-search?
It is published by ruvnet in ruvnet/ruflo. It has 751 recorded installs.
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