claudegoodies
Skill

vector-search

From ruvnet

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

Facts

Repository
ruvnet/ruflo
Status
Actively maintained
Last commit
Declared tools
mcp__claude-flow__embeddings_generate mcp__claude-flow__embe
Source file
plugins/ruflo-agentdb/skills/vector-search/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# 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

1. **Check status** — `mcp__claude-flow__embeddings_status` to verify the embedding engine.
2. **Initialize** — `mcp__claude-flow__embeddings_init` if not active.
3. **Generate** —
View full source on GitHub →

Other skills