/embeddings

name: embeddings
description: RuVector embedding engine status and operations -- ONNX, HNSW, RaBitQ quantization

Embedding engine commands:

1. Call `mcp__plugin_ruflo-core_ruflo__embeddings_status` to check the ONNX embedding engine. 2. Show: model (all-MiniLM-L6-v2), dimensions (384), HNSW index status, cache hit rate. 3. If not initialized, suggest calling `mcp__plugin_ruflo-core_ruflo__embeddings_init`. 4. For search, use `mcp__plugin_ruflo-core_ruflo__embeddings_search` with the user's query (namespace-filtered). 5. For large-corpus search with memory pressure, use the RaBitQ quantized path (32× memory reduction):

• the index builds itself on first use and rebuilds when the corpus drifts >20%; call `mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_build` to force one now
• `mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_search` (Hamming-prefilter to top-N candidates)
• `mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_status` for index health

6. For hierarchical data (taxonomies, code trees), use `mcp__plugin_ruflo-core_ruflo__embeddings_hyperbolic` (Poincare ball model). 7. The substrate-level entry point `mcp__plugin_ruflo-core_ruflo__embeddings_neural` exists; in normal use it's covered by `embeddings_init` + `embeddings_generate`.