Hybrid Search

AI concepts
About 1 min read

A technology that combines traditional keyword matching (BM25) with AI-based semantic vector search, performing both exact keyword matching and contextual understanding to optimize search precision and recall.

Also known as
Hybrid SearchCombined Search

Detailed explanation

Hybrid search integrates the strengths of keyword-based search (sparse retrieval), which finds surface-level text matches, and vector search (dense retrieval), which captures the semantic similarity of data. It identifies precise keywords like proper nouns or product numbers using the BM25 algorithm, while capturing the user's intent and abstract context using embedding models. The separate search results are then consolidated into a single ranked list using Reciprocal Rank Fusion (RRF) or weight-based summation (Alpha Tuning). This technology serves as key infrastructure particularly in RAG (Retrieval-Augmented Generation) systems, preventing search failures on technical terms or abbreviations and improving response reliability by reducing hallucinations. It delivers optimal retrieval performance that misses neither proper nouns nor context, even across large-scale datasets.

Why It Matters in Tool Selection

Using vector search alone can produce critical errors when handling data that requires 'exact matches,' such as product serial numbers, legal article numbers, or names of people. Solutions that support hybrid search prevent this loss of keywords, securing the accuracy required in business environments.

What to Check

  • Can keyword (BM25) and vector weights be adjusted manually or automatically (Alpha Tuning)?
  • Does it support proven rank consolidation algorithms like RRF (Reciprocal Rank Fusion)?
  • Is keyword search performance for agglutinative languages like Korean guaranteed through morphological analyzer support?
  • Does the latency generated during hybrid search meet service requirements?

Example

When a user searches for 'iPhone 15 Pro lowest price,' keyword search filters out the exact model name 'iPhone 15 Pro,' while vector search captures the intent of 'cheap price' or 'discount policy.' By appropriately combining both results, the most relevant product information is placed at the top.

Related terms

RAGSemantic Search