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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
48 lines
1.9 KiB
Markdown
48 lines
1.9 KiB
Markdown
(embeddings-writing-plugins)=
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# Writing plugins to add new embedding models
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Read the {ref}`plugin tutorial <tutorial-model-plugin>` for details on how to develop and package a plugin.
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This page shows an example plugin that implements and registers a new embedding model.
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There are two components to an embedding model plugin:
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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.
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2. A class that extends the `llm.EmbeddingModel` abstract base class.
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The only required method on this class is `embed(text)`, which takes a string and returns a list of floating point numbers.
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The following example uses the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) package to provide access to the [MiniLM-L6](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) embedding model.
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```python
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import llm
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from sentence_transformers import SentenceTransformer
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@llm.hookimpl
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def register_embedding_models(register):
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model_id = "sentence-transformers/all-MiniLM-L6-v2"
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register(SentenceTransformerModel(model_id, model_id, 384), aliases=("all-MiniLM-L6-v2",))
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class SentenceTransformerModel(llm.EmbeddingModel):
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def __init__(self, model_id, model_name, embedding_size):
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self.model_id = model_id
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self.model_name = model_name
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self.embedding_size = embedding_size
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self._model = None
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def embed(self, text):
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if self._model is None:
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self._model = SentenceTransformer(self.model_name)
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return list(map(float, self._model.encode([text])[0]))
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```
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Once installed, the model provided by this plugin can be used with the {ref}`llm embed <embeddings-llm-embed>` command like this:
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```bash
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cat file.txt | llm embed -m sentence-transformers/all-MiniLM-L6-v2
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```
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Or via its registered alias like this:
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```bash
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cat file.txt | llm embed -m all-MiniLM-L6-v2
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```
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