Hybrid Search Implementation
Skill Verifiziert AktivCombine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
To enable developers to build more effective retrieval systems by combining the strengths of semantic vector search with exact keyword matching, improving recall and precision in RAG systems and search engines.
Funktionen
- Hybrid search implementation patterns
- Vector and keyword search fusion (RRF, Linear)
- Cross-encoder reranking for quality improvement
- Code templates for Python, PostgreSQL, and Elasticsearch
- Guidance on best practices for hybrid search tuning
Anwendungsfälle
- Implementing RAG systems with improved recall
- Building domain-specific search engines
- Handling queries requiring exact term matching alongside semantic understanding
- Enhancing search accuracy for technical vocabulary or codes
Nicht-Ziele
- Providing a managed search service
- Abstracting away all database-specific syntax
- Implementing full-text search indexing itself (relies on existing DB features)
Installation
Zuerst Marketplace hinzufügen
/plugin marketplace add wshobson/agents/plugin install llm-application-dev@claude-code-workflowsQualitätspunktzahl
VerifiziertVertrauenssignale
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