Semantic search
Overview
Semantic search is an information retrieval approach that prioritizes conceptual and contextual meaning over lexical matching. Rather than retrieving documents containing the exact words a user typed, semantic search systems map both queries and documents into a shared vector space—typically via embeddings—and measure similarity in that space. This approach enables retrieval of documents that address the user's intent even when they use different vocabulary.
The technology has become foundational to modern AI systems. Large language models and retrieval pipelines routinely rely on semantic matching to supplement or replace traditional keyword-based indexing. In generative AI contexts, semantic search underpins retrieval-augmented generation (RAG) systems that feed relevant documents into language models to ground responses in external knowledge.
Semantic search operates at different granularities: at the document level (which documents are relevant?), at the passage level (which paragraphs?), and increasingly at the claim or entity level for precise factual grounding. The effectiveness of semantic search systems depends heavily on the quality of the embedding model, the size of the searchable index, and alignment between the embedding space and the task at hand.
How it works
Semantic search follows a standard pipeline:
- Embedding generation: A neural encoder (such as a transformer-based model) converts each query and each document (or passage) into a fixed-size vector. These vectors encode semantic content in a learned representation space. Common embedding models include BERT-based encoders, contrastive models like Sentence-BERT, and specialized dense retrievers trained on passage ranking tasks.
- Similarity computation: The system computes a distance or similarity metric between the query embedding and all document embeddings. Common metrics are cosine similarity, Euclidean distance, or learned scoring functions. Cosine similarity is standard because embeddings are typically L2-normalized.
- Ranking and retrieval: Documents are ranked by their similarity scores and the top-k results are returned. In RAG systems, these results are often re-ranked using a cross-encoder or other reranking model before being passed to a language model.
- Efficient indexing: To scale to billions of documents, semantic search systems typically use vector databases (e.g., Pinecone, Weaviate, Milvus) or approximate nearest-neighbor indexes (HNSW, IVF) rather than exhaustive search.
The quality of semantic search is measured by retrieval precision and recall: whether relevant documents appear in the top results, and what fraction of all relevant documents are recovered.
| Term | Distinction |
|---|---|
| Keyword search | Keyword search matches on lexical tokens (words, phrases, n-grams) and typically requires exact or fuzzy overlap. Semantic search operates in meaning space and retrieves documents with different vocabulary that convey the same concept. |
| Embedding | An embedding is a vector representation of text; semantic search is a retrieval method that uses embeddings to find similar documents. Embeddings are the mechanism; semantic search is the application. |
| Retrieval-augmented generation (RAG) | RAG is an end-to-end system that retrieves documents (often using semantic search) and feeds them to a language model for generation. Semantic search is the retrieval component within RAG. |
| Knowledge graph | Knowledge graphs are structured databases of entities and relations, often queried via entity matching or SPARQL. Semantic search treats documents as unstructured text or passages and matches by embedding similarity. Both can complement each other. |
| Grounding | Grounding is the requirement that a system's output be supported by external evidence. Semantic search is a technical mechanism for retrieving that evidence; grounding is the principle. |
Examples
- Google's generative engine: Google's AI Overviews and search results increasingly rely on semantic matching to retrieve passages from the web, rather than only keyword-based indexing, to surface answers and relevant context.
- OpenAI's retrieval for ChatGPT Plugins: When a user query is dispatched to a plugin, semantic search over the plugin's documentation or API schema—using embeddings—determines which endpoints are most relevant to call.
- Enterprise RAG systems: Many organizations deploy semantic search over internal document repositories (wikis, help centers, policy documents) to build question-answering systems. The system embeds user queries and retrieves the most similar passages to ground LLM responses, improving groundedness and reducing hallucinations.
See also
- Embeddings — The vector representations that enable semantic search
- Retrieval-augmented generation — Systems that use semantic search to augment language model generation
- Vector database — Infrastructure for storing and searching embeddings at scale
- Retrieval precision and recall — Metrics for evaluating semantic search quality
- Grounding vs RAG — The principle of anchoring outputs in retrieved evidence
