// Glossary · technical

Semantic Search

Also: vector search · embedding-based search · neural search

Search that retrieves by meaning rather than exact keyword match. Uses vector embeddings to compare query intent against indexed content. Foundation of modern AI copilots and internal search.

Semantic search retrieves content by meaning rather than by exact keyword match. The underlying mechanic is vector embeddings. An embedding model converts a piece of text (a question, a document chunk, a product title) into a high-dimensional vector that captures its semantic meaning. Two pieces of text that mean similar things produce vectors that sit close together in the embedding space, even if they share no overlapping words. A query about reset my password retrieves a document titled forgot login credentials because the meanings align, not because the words match. This is the breakthrough that makes search feel intelligent compared to traditional lexical search.

The architecture sits at the heart of every modern RAG system, every AI copilot, and every internal knowledge search tool funded teams deploy. The flow is straightforward. Documents are chunked into passages, each passage is embedded into a vector, and all vectors are stored in a vector database (Pinecone, Weaviate, Qdrant, pgvector). At query time, the query is embedded with the same model, and the database returns the passages whose vectors are nearest to the query vector by cosine similarity. The retrieved passages then get passed to a language model to generate a grounded answer with citations. The quality of the whole stack depends on the embedding model choice, the chunking strategy, and the ranking layer between retrieval and generation.

For production deployments, pure semantic search is rarely enough on its own. The pattern most teams converge on is hybrid search, combining semantic vector results with traditional keyword (BM25) results and a reranker model that scores the combined set. Pure semantic search struggles with proper nouns, product codes, and exact terminology that the embedding model has not seen. Pure keyword search struggles with paraphrasing and natural-language questions. The hybrid combination handles both. The AI Ops Department and AI Support Department ship semantic search backed copilots as standard delivery, against the knowledge base, support history, and product documentation specific to the customer.

// Examples
  • An internal copilot indexes 6,800 Notion pages with OpenAI embeddings and answers employee questions with citations, removing 55% of repeated questions to the ops team.
  • A support deflection layer combines semantic search and BM25 keyword search across 1,200 help articles, deflecting 38% of tier-1 tickets without human handoff.
  • A B2B e-commerce site replaces a keyword product search with hybrid semantic search, lifting search-result click-through 41% and improving conversion on long-tail queries.
// Common questions
How is semantic search different from keyword search?
Keyword search matches the literal words in the query against the words in the index. Semantic search matches the meaning of the query against the meaning of the index, using vector embeddings. Semantic search handles paraphrasing and synonyms naturally. Keyword search handles exact terms (product codes, names) more reliably. Most production systems use both in a hybrid configuration.
Do I need a vector database for semantic search?
For anything beyond a small prototype, yes. Dedicated vector databases (Pinecone, Weaviate, Qdrant) provide the indexing, filtering, and concurrent query performance needed for production. Below 10,000 documents you can run in memory with FAISS or pgvector on Postgres. Above that, the dedicated databases pay for themselves in operational simplicity.
Which embedding model should I use?
Depends on use case and data sensitivity. OpenAI text-embedding-3 models are the default high-quality choice for most teams. Cohere and Voyage offer competitive alternatives. For sensitive data, open-source models like BGE or E5 run on infrastructure you control. Match the embedding model used at index time and query time, or the math breaks.
What is hybrid search and why does it usually win?
Hybrid search combines semantic vector results with keyword BM25 results, then reranks the combined set with a cross-encoder model. Semantic search alone misses exact terms like product codes. Keyword search alone misses paraphrasing. The hybrid combination handles both failure modes and typically improves retrieval quality 15 to 30 percent over either alone.
// Related terms
// Ready to ship?

EOI runs fractional AI departments for funded teams under 50. Sales, Content, Ops, Support. Live in 14 days on a monthly retainer.

Apply for a sprint →See all departments →