llm-sentence-transformers 0.1.1

Use sentence-transformers for embeddings with LLM

llm-sentence-transformers

LLM plugin for embedding models using sentence-transformers

Further reading:

• LLM now provides tools for working with embeddings
• Embedding paragraphs from my blog with E5-large-v2

Installation

Install this plugin in the same environment as LLM.

llm install llm-sentence-transformers

Configuration

After installing the plugin you need to register one or more models in order to use it. The all-MiniLM-L6-v2 model is registered by default, and will be downloaded the first time you use it.

You can try that model out like this:

llm embed -m mini-l6 -c 'hello'

This will return a JSON array of floating point numbers.

You can add more models using the llm sentence-transformers register command. Here is a list of available models.

Two good models to start experimenting with are all-MiniLM-L12-v2 - a 120MB download - and all-mpnet-base-v2, which is 420MB.

To install that all-mpnet-base-v2 model, run:

llm sentence-transformers register \
  all-mpnet-base-v2 \
  --alias mpnet

The --alias is optional, but can be used to configure one or more shorter aliases for the model.

You can run llm aliases to confirm which aliases you have configured, and llm aliases set to configure further aliases.

Usage

Once you have installed an embedding model you can use it like this:

llm -m sentence-transformers/all-mpnet-base-v2 \
  -c "Hello world"

Or use its alias:

llm -m mpnet -c "Hello world"

Embeddings are more useful if you store them in a database - see the LLM documentation for instructions on doing that.

Be sure to review the documentation for the model you are using. Many models will silently truncate content beyond a certain number of tokens. all-mpnet-base-v2 says that "input text longer than 384 word pieces is truncated", for example.

Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-sentence-transformers
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

pip install -e '.[test]'

To run the tests:

pytest