Vector Search
Skill Verified ActiveVector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction
To provide highly optimized and memory-efficient vector search capabilities for large datasets and high-priority routing scenarios.
Features
- Large-scale HNSW vector search
- WASM router for hot-path patterns
- RaBitQ 1-bit quantization for memory reduction
- Support for hierarchical data via hyperbolic embeddings
Use Cases
- Searching large document corpora
- Implementing fast routing for specific query patterns
- Memory-constrained vector search applications
- Comparing string similarity using embeddings
Non-Goals
- General-purpose data storage or retrieval
- Replacing full-text search engines for unstructured text
- Executing arbitrary code or commands
Documentation
- info:Configuration & parameter referenceWhile tools are listed, detailed documentation on specific parameters and their defaults for each tool is not explicitly laid out in the SKILL.md, though some tuning parameters are mentioned in prose.
Errors
- info:Actionable error messagesThe SKILL.md details tool purposes but does not explicitly describe error paths, root causes, or remediation steps for potential failures.
Practical Utility
- info:Edge casesWhile failure modes of specific tools aren't detailed, the distinction between standard and quantized search, and the mention of corpus size thresholds, implicitly addresses some limitations.
Installation
First, add the marketplace
/plugin marketplace add ruvnet/ruflo/plugin install ruflo-agentdb@rufloQuality Score
VerifiedTrust Signals
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