Combining multiple search techniques to improve information retrieval quality
Core Idea: Hybrid search integrates vector-based semantic search with traditional keyword-based methods to leverage the strengths of both approaches, achieving higher recall and precision than either method alone.
Key Elements
Component Search Methods
- Vector/Semantic Search:
- Uses embedding similarity to capture conceptual relationships
- Excels at understanding meaning and intent beyond exact terms
- Can find relevant results even with different vocabulary
- Keyword/Lexical Search:
- Based on exact or stemmed word matching (e.g., BM25, TF-IDF)
- Excels at precision for specific terminology
- Performs well for proper nouns, technical terms, and exact quotes
- Metadata Filtering:
- Narrows results based on document attributes
- Includes date ranges, authors, document types, categories
Implementation Approaches
- Sequential Hybrid:
- Apply one method first, then refine with the second
- Example: Broad semantic search followed by keyword reranking
- Parallel Hybrid:
- Run both methods simultaneously and combine results
- Requires score normalization and weighting strategy
- Weighted Fusion:
- Combine scores from both methods with adjustable weights
- Formula:
final_score = α·vector_score + (1-α)·keyword_score
- Reciprocal Rank Fusion:
- Combine based on result rankings rather than raw scores
- Less sensitive to score scale differences between methods
Optimization Techniques
- Dynamic Weighting:
- Adjust method weights based on query characteristics
- Increase keyword weight for technical/specific queries
- Favor semantic search for conceptual/exploratory queries
- Query Analysis:
- Detect query intent to select appropriate search strategy
- Identify named entities for special handling
- Result Diversification:
- Ensure coverage of different aspects of the query
- Prevent over-representation of a single document source
Performance Considerations
- Computational Cost:
- Vector search typically more resource-intensive
- Keyword search more efficient for large document sets
- Latency Management:
- Cascading approaches to balance speed and quality
- Optional deeper analysis for ambiguous queries
- Result Evaluation:
- Often yields 15-30% improvement over single-method approaches
- Higher gains for ambiguous or conceptual queries
Connections
- Related Concepts: Vector Search (semantic component), BM25 (keyword algorithm), Result Reranking (refinement technique)
- Broader Context: Information Retrieval Systems (parent field), Search Relevance (quality measurement)
- Applications: RAG Systems (context retrieval), Enterprise Search (practical implementation)
- Components: Query Understanding (intent analysis), Document Processing Pipeline (search enablement)
References
- Reddit discussion on RAG implementation mentioning hybrid search with BM25 (2025)
- Best practices for combining vector and keyword search in retrieval pipelines (2025)
#hybrid-search #information-retrieval #rag #search-algorithms #bm25
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