Let's say your dataset has the words "Oceans are blue" in it.
With keyword search, if someone searches for "Ocean", they'll see that record, since it's a close match. But if they search for "sea" then that record won't be returned.
This is where semantic search comes in. It can automatically deduce semantic / conceptual relationships between words and return a record with "Ocean" even if the search term is "sea", because the two words are conceptually related.
The way semantic search works under the hood is using these things called embeddings, which are just a big array of floating point numbers for each record. It's an alternate way to represent words, in an N-dimensional space created by a machine learning model. Here's more information about embeddings: https://typesense.org/docs/0.25.0/api/vector-search.html#wha...
With the latest release, you essentially don't have to worry about embeddings (except may be picking one of the model names to use and experiment) and Typesense will do the semantic search for you by generating embeddings automatically.
Let's say your dataset has the words "Oceans are blue" in it.
With keyword search, if someone searches for "Ocean", they'll see that record, since it's a close match. But if they search for "sea" then that record won't be returned.
This is where semantic search comes in. It can automatically deduce semantic / conceptual relationships between words and return a record with "Ocean" even if the search term is "sea", because the two words are conceptually related.
The way semantic search works under the hood is using these things called embeddings, which are just a big array of floating point numbers for each record. It's an alternate way to represent words, in an N-dimensional space created by a machine learning model. Here's more information about embeddings: https://typesense.org/docs/0.25.0/api/vector-search.html#wha...
With the latest release, you essentially don't have to worry about embeddings (except may be picking one of the model names to use and experiment) and Typesense will do the semantic search for you by generating embeddings automatically.