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Simon Willison 2023-09-04 08:30:45 -07:00 committed by GitHub
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docs/embeddings/cli.md
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@ -38,7 +38,7 @@ See {ref}`embeddings-binary` for options to get back embeddings in formats other
Embeddings are much more useful if you store them somewhere, so you can calculate similarity scores between different embeddings later on.
LLM includes the concept of a "collection" of embeddings. A collection groups together a set of stored embeddings created using the same model, each with a unique ID within that collection.
LLM includes the concept of a **collection** of embeddings. A collection groups together a set of stored embeddings created using the same model, each with a unique ID within that collection.
Embeddings also store a hash of the content that was embedded. This hash is later used to avoid calculating duplicate embeddings for the same content.
@ -367,4 +367,4 @@ llm embed-db delete-collection collection-name
Pass `-d` to specify a different database file:
```bash
llm embed-db delete-collection collection-name -d my-embeddings.db
```
```