Vector Index Tuning
Skill Verified ActiveOptimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Optimize vector index performance for latency, recall, and memory by providing practical guidance and code templates for tuning HNSW parameters, selecting quantization strategies, and scaling vector search infrastructure.
Features
- Tune HNSW parameters (M, efConstruction, efSearch)
- Implement quantization strategies (INT8, Product, Binary)
- Estimate memory usage for different configurations
- Create optimized Qdrant collections
- Benchmark search performance and recall
Use Cases
- Tuning HNSW parameters for specific recall/latency needs
- Selecting appropriate quantization strategies to reduce memory footprint
- Optimizing Qdrant collection configurations for balanced performance
- Scaling vector search infrastructure for large datasets
Non-Goals
- Providing a fully managed vector database service
- Automating the entire data ingestion and indexing pipeline
- Handling the creation and management of raw vector data
Installation
First, add the marketplace
/plugin marketplace add wshobson/agents/plugin install llm-application-dev@claude-code-workflowsQuality Score
VerifiedTrust Signals
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