Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
76
66%
Does it follow best practices?
Impact
93%
1.13xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/skills/hybrid-search-implementation/SKILL.mdRRF fusion pipeline implementation
RRF constant k=60
100%
100%
RRF formula correctness
100%
100%
Candidate over-fetching
70%
100%
Parallel search execution
100%
100%
Source field tracking
100%
100%
Both scores retained
0%
0%
Handles single-source documents
100%
100%
Includes keyword search
100%
100%
Top-k truncation after fusion
100%
100%
Descending score sort
100%
100%
PostgreSQL hybrid search schema
HNSW vector index
0%
100%
vector_cosine_ops operator
0%
100%
GIN full-text index
100%
100%
GENERATED ALWAYS AS tsvector
100%
100%
websearch_to_tsquery usage
100%
100%
FULL OUTER JOIN for result combination
100%
100%
RRF scoring in SQL
0%
100%
pgvector extension
100%
100%
JSONB metadata column
100%
100%
CTE-based query structure
100%
100%
Linear fusion and cross-encoder reranking
Min-max score normalization
100%
100%
Alpha-weighted combination
100%
100%
Cross-encoder model choice
100%
100%
Reranking candidate over-fetching
0%
0%
Query-content pairs for reranker
100%
100%
Rerank score descending sort
100%
100%
Empty results handling
100%
100%
Optional reranking toggle
100%
100%
Missing-source score defaults to zero
100%
100%
Normalization zero-range guard
100%
100%
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Table of Contents
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