What is Semantic Search?
Semantic Search is information retrieval that uses learned representations to retrieve meaningfully related documents, then applies filters and ranking to answer an intent rather than relying solely on lexical token overlap.
Quick Facts
| Created | Concept from 2000s, transformer-based from 2019 |
|---|---|
| Specification | Official Specification |
How It Works
Semantic search uses embeddings to generate candidates that can match paraphrases, concepts, and intent even when query and document tokens differ. It is not a replacement for lexical retrieval: exact identifiers, names, code, and freshness constraints often require keyword search and metadata filters. A production pipeline typically defines a document and chunking policy, indexes embeddings, retrieves candidates with an ANN index, applies authorization and freshness filters, fuses lexical and semantic candidates when appropriate, reranks the shortlist, and measures results on labeled queries. Similarity scores are model- and index-specific signals, not probabilities or evidence that a result is correct. Choose embedding models, chunking, index settings, fusion, and rerankers against the workload's language, latency, cost, and relevance measurements rather than a universal ranking.
Key Characteristics
- Embedding-based candidate generation for paraphrase and conceptual matches
- Lexical retrieval and metadata filters for exact terms, permissions, and freshness
- Approximate nearest-neighbor indexes that trade latency, recall, and memory
- Hybrid fusion and reranking selected against labeled relevance data
- Query, document, language, tenant, and time-aware retrieval constraints
- Evaluation with Recall@K, MRR, nDCG, error slices, and online feedback
Common Use Cases
- RAG pipelines that retrieve sourced, authorized context before answer generation
- Product discovery that combines natural-language intent with inventory and catalog filters
- Enterprise document search with tenant, role, retention, and freshness constraints
- Customer support retrieval and routing with human-reviewed relevance evaluation
- Code search that combines symbols and exact identifiers with code embeddings
- Literature discovery where semantic candidates are verified against metadata and citations
- Cross-lingual search evaluated separately for each language and query class
Example
Loading code...Frequently Asked Questions
What is the difference between semantic search and keyword search?
Keyword retrieval ranks lexical overlap and is often best for identifiers, phrases, codes, names, and freshness-sensitive terms. Semantic retrieval ranks learned representations, so it can recall paraphrases and related concepts. Neither dominates universally: robust systems commonly retrieve from both, enforce metadata and access filters, then fuse or rerank candidates against measured relevance.
How do vector embeddings work in semantic search?
An embedding model maps a query or document to numeric coordinates designed to preserve task-relevant relationships. Search compares the query representation with precomputed document representations using the metric expected by the model and index. The score is relative to that model, normalization, and corpus; it does not establish factual correctness, authorization, or a calibrated probability of relevance.
What is hybrid search and when should I use it?
Hybrid search combines lexical and semantic candidate lists, often with rank fusion or learned ranking. It is useful when a workload mixes exact tokens with natural-language intent, but it is not an automatic default. Test pure lexical, pure semantic, and hybrid alternatives on labeled queries, then compare recall, ranking quality, latency, and failure slices before choosing weights or a fusion method.
Which embedding models are best for semantic search?
Choose a model after defining languages, domains, query types, privacy constraints, hardware or API budget, and latency targets. Use current provider documentation rather than hard-coding a historical model list, and benchmark candidate models on representative queries and relevance labels. Evaluate the full pipeline: chunking, normalization, index configuration, filtering, fusion, and reranking can matter as much as the embedding model.
How does semantic search enable RAG (Retrieval-Augmented Generation)?
RAG uses semantic search to retrieve relevant documents or passages from a knowledge base, then provides these as context to an LLM for generating accurate, grounded responses. Semantic search is crucial for finding the most contextually relevant information, even when user queries don't match document keywords exactly.
What is the difference between bi-encoder and cross-encoder in semantic search?
A bi-encoder independently encodes queries and documents into embeddings, enabling fast retrieval via precomputed document vectors. A cross-encoder jointly processes the query-document pair and produces a more accurate relevance score but is too slow for large-scale retrieval. In practice, a two-stage pipeline is common: a bi-encoder retrieves top candidates quickly, then a cross-encoder re-ranks them for higher precision.
How do I evaluate semantic search quality?
Common evaluation metrics include Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), Recall@K, and Precision@K. Build a test set of queries with labeled relevant documents, run your search pipeline, and measure how well the top-K results match the expected answers. Tools like BEIR and MTEB benchmarks provide standardized datasets for comparing embedding models.