Semantic Search - Searching by Meaning Instead of Keyword

Semantic search is a search method that finds content by its meaning instead of by matching character strings. Searching for "car" also returns results about "vehicle" or "automobile" - even though none of these words is identical to the query. This is possible because query and documents are translated into embeddings: numerical vectors that place the meaning of a text in space. Similar meaning leads to vectors that lie close together.

Semantic search is therefore the heart of modern RAG systems: it ensures that the passages matching a question in content are found before a language model formulates an answer from them.

How It Works

1. Embedding: All document passages are converted into vectors in advance and stored in a vector database.
2. Query as vector: The search query is translated into a vector with the same method.
3. Nearest-neighbor search: The vectors closest to the query vector are sought - that is, the passages most similar in content.
4. Return: These passages are returned as results, usually sorted by similarity.

How It Differs From Classic Full-Text Search

Classic keyword or full-text search compares character strings: a document counts as a hit if the search term appears literally in it. That is fast, transparent, and precise for exact terms - but reaches its limits as soon as the user chooses different words than the document.

• Full-text search: Searches for the wording. Finds "notice period" only if exactly that word is in the text. Synonyms, paraphrases, or typos stay undetected.
• Semantic search: Searches for the meaning. Finds the matching paragraph even if it reads "period to terminate the contract" - because the meaning is similar.

Neither approach is superior in every situation. Full-text search wins with exact terms like product numbers or proper names, semantic search with freely phrased questions. That is why both are often combined in a hybrid search.

What Quality Depends On

• A good embedding model: It decides how aptly meaning is mapped into vectors - depending on language and domain.
• Clean chunking: Only thematically self-contained passages yield clear vectors. How documents are split for this is described by the term chunking.
• Suitable similarity search: For large collections, an approximate nearest-neighbor search is used that balances speed and hit quality.

Semantic Search at Elasticbrains

At Elasticbrains we build semantic search into your knowledge collections - with embeddings tuned to language and domain and, where useful, combined with keyword search and reranking. See how this becomes a searchable, verifiable internal assistant on our service page internal AI assistants / RAG knowledge base.