Hybrid Search
- [Canberra Deep Space Communication Complex - NASA]
- Overview
Hybrid search merges traditional keyword (lexical) search and modern AI vector (semantic) search into a single system to deliver results that are both exact and context-aware.
1. How It Works:
- Keyword Search (Sparse Vectors): Uses algorithms like BM25 to find exact word and phrase matches, offering high precision for specific terms, part numbers, or names.
- Vector Search (Dense Vectors): Uses neural embeddings to capture the conceptual meaning and intent behind a query, handling synonyms and paraphrasing even when exact words are missing.
- Result Fusion: Combines both parallel search streams using methods like Reciprocal Rank Fusion (RRF) to merge separate score scales into a single, highly relevant ranked list.
2. Why Use Hybrid Search:
- Improves Relevance: Studies show it boosts search accuracy by 25% to 40% compared to using either method by itself.
- Fixes Blind Spots: Keyword searches miss synonyms and context, while vector searches can be too fuzzy or miss specific proper nouns; hybrid search covers both weaknesses.
- Broad Application: Powers modern enterprise search, e-commerce product discovery, and Retrieval-Augmented Generation (RAG) pipelines for AI models.
[More to come ...]
