Semantic Search

Semantic search is a search approach that understands the meaning and intent behind queries, rather than just matching keywords. It uses embeddings (numerical representations of meaning) and vector similarity to find content that is conceptually relevant, even when exact keywords don't match.

Traditional keyword search vs. semantic search: keyword search for "how to fix a flat tire" only finds content containing those exact words. Semantic search also finds content about "tire puncture repair," "changing a blown tire," or "roadside tire replacement" — because the meaning is similar even though the words are different.

How semantic search works: content is converted to embedding vectors (capturing meaning), queries are also converted to vectors, the system finds vectors closest to the query vector (using cosine similarity or other distance metrics), and the most similar content is returned as search results.

Semantic search is critical for AI agents because users ask questions in natural language, using their own words. They don't know the exact terminology in the knowledge base. Semantic search bridges this gap, ensuring the right information is retrieved regardless of how the question is phrased.

Modern search systems often use hybrid approaches: combining keyword search (exact matching, Boolean logic) with semantic search (meaning-based matching) and re-ranking (using AI to order results by relevance). This hybrid approach captures both exact matches and semantically similar content.

On AI agent platforms like Chipp, semantic search powers knowledge base retrieval — ensuring agents find the most relevant information to answer user questions accurately.