AI comparison
Embeddings vs keyword search
Embedding search matches meaning, so it finds paraphrases; keyword search matches exact words, so it finds codes and names. Many systems use both together.
| Aspect | Embedding search | Keyword search |
|---|---|---|
| Matches | Meaning, even with different words | The words in the query |
| Strong on | Questions phrased in everyday language | Error codes, part numbers, rare names |
| Needs | An embedding model and a vector store | An inverted index |
| Explaining a hit | Harder: a similarity score | Easy: the matching words |
Choose Embedding search when
- Users describe problems in their own words
- Documents use different words for the same thing
- You search across languages
Choose Keyword search when
- Queries are exact identifiers
- You need to show why a result matched
- You want a simple, cheap baseline
Learn it hands-on
These free lessons teach it with commands to run in a lab and checked tasks. Read them whole with no account.
Related terms
- Embedding
- An embedding is a list of numbers that represents the meaning of a piece of text, so that texts with similar meanings sit close together.
- Vector search
- Vector search finds the stored items whose embeddings are closest to the embedding of a query, so it matches by meaning rather than by exact words.
- Hybrid search
- Hybrid search runs a keyword search and a vector search on the same query and merges the two result lists, so each covers the other's blind spot.