Embedding with the CLI#

LLM provides command-line utilities for calculating and storing embeddings for pieces of content.

llm embed#

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.

Returning embeddings to the terminal#

The simplest way to use this command is to pass content to it using the -c/--content option, like this:

llm embed -c 'This is some content' -m 3-small

-m 3-small specifies the OpenAI text-embedding-3-small model. You will need to have set an OpenAI API key using llm keys set openai for this to work.

You can install plugins to access other models. The llm-sentence-transformers plugin can be used to run models on your own laptop, such as the MiniLM-L6 model:

llm install llm-sentence-transformers
llm embed -c 'This is some content' -m sentence-transformers/all-MiniLM-L6-v2

The llm embed command returns a JSON array of floating point numbers directly to the terminal:

[0.123, 0.456, 0.789...]

You can omit the -m/--model option if you set a default embedding model.

LLM also offers a binary storage format for embeddings, described in embeddings storage format.

You can output embeddings using that format as raw bytes using --format blob, or in hexadecimal using --format hex, or in Base64 using --format base64:

llm embed -c 'This is some content' -m 3-small --format base64

This outputs:

8NGzPFtdgTqHcZw7aUT6u+++WrwwpZo8XbSxv...

Some models such as llm-clip can run against binary data. You can pass in binary data using the -i and --binary options:

llm embed --binary -m clip -i image.jpg

Or from standard input like this:

cat image.jpg | llm embed --binary -m clip -i -

Storing embeddings in SQLite#

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.

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.

First, we’ll set a default model so we don’t have to keep repeating it:

llm embed-models default 3-small

The llm embed command can store results directly in a named collection like this:

llm embed quotations philkarlton-1 -c \
  'There are only two hard things in Computer Science: cache invalidation and naming things'

This stores the given text in the quotations collection under the key philkarlton-1.

You can also pipe content to standard input, like this:

cat one.txt | llm embed files one

This will store the embedding for the contents of one.txt in the files collection under the key one.

A collection will be created the first time you mention it.

Collections have a fixed embedding model, which is the model that was used for the first embedding stored in that collection.

In the above example this would have been the default embedding model at the time that the command was run.

The following example stores the embedding for the string “my happy hound” in a collection called phrases under the key hound and using the model 3-small:

llm embed phrases hound -m 3-small -c 'my happy hound'

By default, the SQLite database used to store embeddings is the embeddings.db in the user content directory managed by LLM.

You can see the path to this directory by running llm collections path.

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:

llm embed phrases hound -d my-embeddings.db -c 'my happy hound'

This creates a database file called my-embeddings.db in the current directory.

Storing content and metadata#

By default, only the entry ID and the embedding vector are stored in the database table.

You can store a copy of the original text in the content column by passing the --store option:

llm embed phrases hound -c 'my happy hound' --store

You can also store a JSON object containing arbitrary metadata in the metadata column by passing the --metadata option. This example uses both --store and --metadata options:

llm embed phrases hound \
  -m 3-small \
  -c 'my happy hound' \
  --metadata '{"name": "Hound"}' \
  --store

Data stored in this way will be returned by calls to llm similar, for example:

llm similar phrases -c 'hound'

{"id": "hound", "score": 0.8484683588631485, "content": "my happy hound", "metadata": {"name": "Hound"}}

llm embed-multi#

The llm embed command embeds a single string at a time.

llm embed-multi can be used to embed multiple strings at once, taking advantage of any efficiencies that the embedding model may provide when processing multiple strings.

This command can be called in one of three ways:

  1. With a CSV, TSV, JSON or newline-delimited JSON file

  2. With a SQLite database and a SQL query

  3. With one or more paths to directories, each accompanied by a glob pattern

All three mechanisms support these options:

  • -m model_id to specify the embedding model to use

  • -d database.db to specify a different database file to store the embeddings in

  • --store to store the original content in the embeddings table in addition to the embedding vector

  • --prefix to prepend a prefix to the stored ID of each item

  • --batch-size SIZE to process embeddings in batches of the specified size

Embedding data from a CSV, TSV or JSON file#

You can embed data from a CSV, TSV or JSON file by passing that file to the command as the second option, after the collection name.

Your file must contain at least two columns. The first one is expected to contain the ID of the item, and any subsequent columns will be treated as containing content to be embedded.

An example CSV file might look like this:

id,content
one,This is the first item
two,This is the second item

TSV would use tabs instead of commas.

JSON files can be structured like this:

[
  {"id": "one", "content": "This is the first item"},
  {"id": "two", "content": "This is the second item"}
]

Or as newline-delimited JSON like this:

{"id": "one", "content": "This is the first item"}
{"id": "two", "content": "This is the second item"}

In each of these cases the file can be passed to llm embed-multi like this:

llm embed-multi items mydata.csv

The first argument is the name of the collection, the second is the filename.

You can also pipe content to standard input of the tool using -:

cat mydata.json | llm embed-multi items -

LLM will attempt to detect the format of your data automatically. If this doesn’t work you can specify the format using the --format option. This is required if you are piping newline-delimited JSON to standard input.

cat mydata.json | llm embed-multi items - --format nl

Other supported --format options are csv, tsv and json.

This example embeds the data from a JSON file in a collection called items in database called docs.db using the 3-small model and stores the original content in the embeddings table as well, adding a prefix of my-items/ to each ID:

llm embed-multi items mydata.json \
  -d docs.db \
  -m 3-small \
  --prefix my-items/ \
  --store

Embedding data from a SQLite database#

You can embed data from a SQLite database using --sql, optionally combined with --attach to attach an additional database.

If you are storing embeddings in the same database as the source data, you can do this:

llm embed-multi docs \
  -d docs.db \
  --sql 'select id, title, content from documents' \
  -m 3-small

The docs.db database here contains a documents table, and we want to embed the title and content columns from that table and store the results back in the same database.

To load content from a database other than the one you are using to store embeddings, attach it with the --attach option and use alias.table in your SQLite query:

llm embed-multi docs \
  -d embeddings.db \
  --attach other other.db \
  --sql 'select id, title, content from other.documents' \
  -m 3-small

Embedding data from files in directories#

LLM can embed the content of every text file in a specified directory, using the file’s path and name as the ID.

Consider a directory structure like this:

docs/aliases.md
docs/contributing.md
docs/embeddings/binary.md
docs/embeddings/cli.md
docs/embeddings/index.md
docs/index.md
docs/logging.md
docs/plugins/directory.md
docs/plugins/index.md

To embed all of those documents, you can run the following:

llm embed-multi documentation \
  -m 3-small \
  --files docs '**/*.md' \
  -d documentation.db \
  --store

Here --files docs '**/*.md' specifies that the docs directory should be scanned for files matching the **/*.md glob pattern - which will match Markdown files in any nested directory.

The result of the above command is a embeddings table with the following IDs:

aliases.md
contributing.md
embeddings/binary.md
embeddings/cli.md
embeddings/index.md
index.md
logging.md
plugins/directory.md
plugins/index.md

Each corresponding to embedded content for the file in question.

The --prefix option can be used to add a prefix to each ID:

llm embed-multi documentation \
  -m 3-small \
  --files docs '**/*.md' \
  -d documentation.db \
  --store \
  --prefix llm-docs/

This will result in the following IDs instead:

llm-docs/aliases.md
llm-docs/contributing.md
llm-docs/embeddings/binary.md
llm-docs/embeddings/cli.md
llm-docs/embeddings/index.md
llm-docs/index.md
llm-docs/logging.md
llm-docs/plugins/directory.md
llm-docs/plugins/index.md

Files are assumed to be utf-8, but LLM will fall back to latin-1 if it encounters an encoding error. You can specify a different set of encodings using the --encoding option.

This example will try utf-16 first and then mac_roman before falling back to latin-1:

llm embed-multi documentation \
  -m 3-small \
  --files docs '**/*.md' \
  -d documentation.db \
  --encoding utf-16 \
  --encoding mac_roman \
  --encoding latin-1

If a file cannot be read it will be logged to standard error but the script will keep on running.

If you are embedding binary content such as images for use with CLIP, add the --binary option:

llm embed-multi photos \
  -m clip \
  --files photos/ '*.jpeg' --binary

llm similar#

The llm similar command searches a collection of embeddings for the items that are most similar to a given or item ID.

This currently uses a slow brute-force approach which does not scale well to large collections. See issue 216 for plans to add a more scalable approach via vector indexes provided by plugins.

To search the quotations collection for items that are semantically similar to 'computer science':

llm similar quotations -c 'computer science'

This embeds the provided string and returns a newline-delimited list of JSON objects like this:

{"id": "philkarlton-1", "score": 0.8323904531677017, "content": null, "metadata": null}

You can compare against text stored in a file using -i filename:

llm similar quotations -i one.txt

Or feed text to standard input using -i -:

echo 'computer science' | llm similar quotations -i -

When using a model like CLIP, you can find images similar to an input image using -i filename with --binary:

llm similar photos -i image.jpg --binary

llm embed-models#

To list all available embedding models, including those provided by plugins, run this command:

llm embed-models

The output should look something like this:

3-small (aliases: ada)
sentence-transformers/all-MiniLM-L6-v2 (aliases: all-MiniLM-L6-v2)

llm embed-models default#

This command can be used to get and set the default embedding model.

This will return the name of the current default model:

llm embed-models default

You can set a different default like this:

llm embed-models default 3-small

This will set the default model to OpenAI’s 3-small model.

Any of the supported aliases for a model can be passed to this command.

You can unset the default model using --remove-default:

llm embed-models default --remove-default

When no default model is set, the llm embed and llm embed-multi commands will require that a model is specified using -m/--model.

llm collections list#

To list all of the collections in the embeddings database, run this command:

llm collections list

Add --json for JSON output:

llm collections list --json

Add -d/--database to specify a different database file:

llm collections list -d my-embeddings.db

llm collections delete#

To delete a collection from the database, run this:

llm collections delete collection-name

Pass -d to specify a different database file:

llm collections delete collection-name -d my-embeddings.db