Writing plugins to add new embedding models

Read the plugin tutorial for details on how to develop and package a plugin.

This page shows an example plugin that implements and registers a new embedding model.

There are two components to an embedding model plugin:

1. An implementation of the register_embedding_models() hook, which takes a register callback function and calls it to register the new model with the LLM plugin system.

2. A class that extends the llm.EmbeddingModel abstract base class.

   The only required method on this class is embed_batch(texts), which takes an iterable of strings and returns an iterator over lists of floating point numbers.

The following example uses the sentence-transformers package to provide access to the MiniLM-L6 embedding model.

import llm
from sentence_transformers import SentenceTransformer


@llm.hookimpl
def register_embedding_models(register):
    model_id = "sentence-transformers/all-MiniLM-L6-v2"
    register(SentenceTransformerModel(model_id, model_id), aliases=("all-MiniLM-L6-v2",))


class SentenceTransformerModel(llm.EmbeddingModel):
    def __init__(self, model_id, model_name):
        self.model_id = model_id
        self.model_name = model_name
        self._model = None

    def embed_batch(self, texts):
        if self._model is None:
            self._model = SentenceTransformer(self.model_name)
        results = self._model.encode(texts)
        return (list(map(float, result)) for result in results)

Once installed, the model provided by this plugin can be used with the llm embed command like this:

cat file.txt | llm embed -m sentence-transformers/all-MiniLM-L6-v2

Or via its registered alias like this:

cat file.txt | llm embed -m all-MiniLM-L6-v2

llm-sentence-transformers is a complete example of a plugin that provides an embedding model.

Execute Jina embeddings with a CLI using llm-embed-jina talks through a similar process to add support for the Jina embeddings models.

Embedding binary content

If your model can embed binary content, use the supports_binary property to indicate that:

class ClipEmbeddingModel(llm.EmbeddingModel):
    model_id = "clip"
    supports_binary = True
    supports_text= True

supports_text defaults to True and so is not necessary here. You can set it to False if your model only supports binary data.

If your model accepts binary, your .embed_batch() model may be called with a list of Python bytestrings. These may be mixed with regular strings if the model accepts both types of input.

llm-clip is an example of a model that can embed both binary and text content.