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* Embeddings plugin hook + OpenAI implementation * llm.get_embedding_model(name) function * llm embed command, for returning embeddings or saving them to SQLite * Tests using an EmbedDemo embedding model * llm embed-models list and emeb-models default commands * llm embed-db path and llm embed-db collections commands
97 lines
3.3 KiB
Markdown
97 lines
3.3 KiB
Markdown
(embeddings-cli)=
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# Embedding with the CLI
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LLM provides command-line utilities for calculating and storing embeddings for pieces of content.
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(embeddings-llm-embed)=
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## llm embed
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The `llm embed` command can be used to calculate embedding vectors for a string of content. These can be returned directly to the terminal, stored in a SQLite database, or both.
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### Returning embeddings to the terminal
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The simplest way to use this command is to pass content to it using the `-c/--content` option, like this:
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```bash
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llm embed -c 'This is some content'
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```
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The command will return a JSON array of floating point numbers directly to the terminal:
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```json
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[0.123, 0.456, 0.789...]
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```
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By default it uses the {ref}`default embedding model <embeddings-cli-embed-models-default>`.
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Use the `-m/--model` option to specify a different model:
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```bash
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llm -m sentence-transformers/all-MiniLM-L6-v2 \
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-c 'This is some content'
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```
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See {ref}`embeddings-binary` for options to get back embeddings in formats other than JSON.
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### Storing embeddings in SQLite
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Embeddings are much more useful if you store them somewhere, so you can calculate similarity scores between different embeddings later on.
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LLM includes a concept of a "collection" of embeddings. This is a named object where multiple pieces of content can be stored, each with a unique ID.
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The `llm embed` command can store results directly in a named collection like this:
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```bash
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cat one.txt | llm embed my-files one
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```
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This will store the embedding for the contents of `one.txt` in the `my-files` collection under the key `one`.
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A collection will be created the first time you mention it.
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Collections have a fixed embedding model, which is the model that was used for the first embedding stored in that collection.
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In the above example this would have been the default embedding model at the time that the command was run.
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This example stores the embedding of the string "my happy hound" in a collection called `phrases` under the key `hound` and using the model `ada-002`:
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```bash
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llm embed -m ada-002 -c 'my happy hound' phrases hound
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```
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By default, the SQLite database used to store embeddings is the `embeddings.db` in the user content directory managed by LLM.
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You can see the path to this directory by running `llm embed-db path`.
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You can store embeddings in a different SQLite database by passing a path to it using the `-d/--database` option to `llm embed`. If this file does not exist yet the command will create it:
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```bash
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llm embed -d my-embeddings.db -c 'my happy hound' phrases hound
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```
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This creates a database file called `my-embeddings.db` in the current directory.
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(embeddings-cli-embed-models-default)=
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## llm embed-models default
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This command can be used to get and set the default embedding model.
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This will return the name of the current default model:
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```bash
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llm embed-models default
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```
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You can set a different default like this:
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```
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llm embed-models default name-of-other-model
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```
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Any of the supported aliases for a model can be passed to this command.
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## llm embed-db collections
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To list all of the collections in the embeddings database, run this command:
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```bash
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llm embed-db collections
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```
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Add `--json` for JSON output:
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```bash
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llm embed-db collections --json
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```
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Add `-d/--database` to specify a different database file:
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```bash
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llm embed-db collections -d my-embeddings.db
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```
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