Hybrid Search
A search approach that combines keyword-based (lexical) search with semantic (vector) search to get the benefits of both — exact matching for specific terms and meaning-based matching for conceptual queries.
Why It Matters
Hybrid search consistently outperforms either approach alone. It catches both exact keyword matches and semantically related content that pure keyword or vector search would miss.
Example
Searching for 'Python 3.12 release notes' using keyword search to match the exact version number AND semantic search to find content about 'latest Python features and changes.'
Think of it like...
Like searching for a restaurant by both name (keyword) and cuisine type (semantic) — sometimes you know exactly what you want, sometimes you want something similar.
Related Terms
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords. It uses embeddings to find results that are conceptually related even if they use different words.
Vector Search
The process of finding the most similar vectors in a vector database to a given query vector. It enables retrieving semantically similar content at scale.
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
BM25
Best Matching 25 — a widely used ranking function for keyword-based information retrieval. BM25 scores documents based on query term frequency, document length, and corpus statistics.