What is Semantic Search?
Semantic search is an information retrieval approach that finds results based on the meaning of a query rather than exact keyword matches. It uses AI embeddings to understand what the user intends, returning relevant results even when the query and the document use completely different words.
Why It Matters
Semantic search is the foundation of RAG (Retrieval-Augmented Generation) — the technique that gives LLMs access to external knowledge. It's also transforming traditional search: Google's search increasingly uses semantic understanding, and enterprise search tools use embeddings to find relevant documents. For AI applications, semantic search is how you connect user questions to the right data.
How It Works
- Embedding — convert both documents and queries into dense vector representations (embeddings) using a model like OpenAI's text-embedding-3 or Cohere's embed
- Indexing — store document embeddings in a vector database (Pinecone, Weaviate, Qdrant, pgvector)
- Query — when a user searches, embed the query using the same model
- Similarity matching — find the documents whose embeddings are closest to the query embedding using cosine similarity or other distance metrics
- Ranking — return the most semantically similar documents
Why it beats keyword search:
- Query: "How do I fix a broken screen?" matches a document about "display repair guide" — no keyword overlap but same meaning