Vector Database Optimization

Performance tuning for Pinecone, Weaviate, Qdrant, and pgvector including index selection, embedding dimensionality, chunking strategies, and hybrid search optimization for RAG systems.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

Decision Usefulness (15%)

8.5

Best for

▸Optimizing HNSW index parameters for Pinecone production workloads
▸Reducing pgvector query latency through embedding dimensionality tuning
▸Designing chunking strategies for legal document RAG systems
▸Implementing hybrid search with semantic and lexical ranking fusion
▸Troubleshooting recall degradation in billion-vector Qdrant deployments

What you'll get

●Detailed HNSW parameter recommendations with specific M, efConstruction, and efSearch values based on dataset characteristics and latency requirements
●Chunking strategy comparison table showing token sizes, overlap percentages, and expected recall impact for different document types
●Performance optimization roadmap with quantified improvements, cost implications, and implementation timeline for production deployment

Not designed for ↓

×Training custom embedding models from scratch
×Building vector databases from the ground up
×General machine learning model optimization
×Database schema design for traditional RDBMS

Expects

Production vector database details including system type, dataset size, embedding dimensions, current performance metrics, and specific bottlenecks or optimization goals.

Returns

Concrete optimization recommendations with specific parameter values, chunking strategies, index configurations, and performance benchmarking approaches tailored to the vector database system.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

vector-databasepineconeweaviateqdrantpgvectorhnswembedding-optimizationraghybrid-searchindex-tuningapproximate-nearest-neighborchunking-strategyquantizationretrieval-augmented-generation

Research Foundation: 8 sources (3 academic, 4 official docs, 1 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Works well with

Embedding Space ArchitectPlatinum Hybrid Search ArchitectGold RAG Architecture DesignerGold RAG Chunking StrategistGold Search Relevance EngineerPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Database Performance TunerPlatinum Real-Time Analytics ArchitectGold Kubernetes Operations AdvisorGold

Common Workflows

RAG System Optimization Pipeline

Complete RAG system optimization from content preprocessing through vector storage to retrieval evaluation

RAG Chunking Strategist→vector-database-optimization→Hybrid Search Architect→llm-evaluation-framework-designer

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