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ee640d6bd3
This PR fixes a bug with the current implementation for Model2Vec embeddings where `embed_documents` does not work as expected. - **Description**: the current implementation uses `encode_as_sequence` for encoding documents. This is incorrect, as `encode_as_sequence` creates token embeddings and not mean embeddings. The normal `encode` function handles both single and batched inputs and should be used instead. The return type was also incorrect, as encode returns a NumPy array. This PR converts the embedding to a list so that the output is consistent with the Embeddings ABC.
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
"""Wrapper around model2vec embedding models."""
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from typing import List
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from langchain_core.embeddings import Embeddings
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class Model2vecEmbeddings(Embeddings):
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"""Model2Vec embedding models.
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Install model2vec first, run 'pip install -U model2vec'.
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The github repository for model2vec is : https://github.com/MinishLab/model2vec
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Example:
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.. code-block:: python
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from langchain_community.embeddings import Model2vecEmbeddings
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embedding = Model2vecEmbeddings("minishlab/potion-base-8M")
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embedding.embed_documents([
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"It's dangerous to go alone!",
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"It's a secret to everybody.",
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])
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embedding.embed_query(
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"Take this with you."
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)
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"""
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def __init__(self, model: str):
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"""Initialize embeddings.
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Args:
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model: Model name.
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"""
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try:
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from model2vec import StaticModel
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except ImportError as e:
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raise ImportError(
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"Unable to import model2vec, please install with "
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"`pip install -U model2vec`."
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) from e
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self._model = StaticModel.from_pretrained(model)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using the model2vec embeddings model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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return self._model.encode(texts).tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using the model2vec embeddings model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self._model.encode(text).tolist()
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