戒酒的李白
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import numpy as np
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from typing import List
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from fastembed import TextEmbedding
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from bertopic.backend import BaseEmbedder
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class FastEmbedBackend(BaseEmbedder):
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"""FastEmbed embedding model.
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The FastEmbed embedding model used for generating sentence embeddings.
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Arguments:
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embedding_model: A FastEmbed embedding model
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Examples:
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To create a model, you can load in a string pointing to a supported
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FastEmbed model:
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```python
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from bertopic.backend import FastEmbedBackend
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sentence_model = FastEmbedBackend("BAAI/bge-small-en-v1.5")
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```
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"""
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def __init__(self, embedding_model: str = "BAAI/bge-small-en-v1.5"):
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super().__init__()
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supported_models = [m["model"] for m in TextEmbedding.list_supported_models()]
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if isinstance(embedding_model, str) and embedding_model in supported_models:
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self.embedding_model = TextEmbedding(model_name=embedding_model)
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else:
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raise ValueError(
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"Please select a correct FasteEmbed model: \n"
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"the model must be a string and must be supported. \n"
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"The supported TextEmbedding model list is here: https://qdrant.github.io/fastembed/examples/Supported_Models/"
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)
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def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
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"""Embed a list of n documents/words into an n-dimensional
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matrix of embeddings.
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Arguments:
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documents: A list of documents or words to be embedded
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verbose: Controls the verbosity of the process
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Returns:
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Document/words embeddings with shape (n, m) with `n` documents/words
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that each have an embeddings size of `m`
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"""
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embeddings = np.array(list(self.embedding_model.embed(documents, show_progress_bar=verbose)))
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return embeddings
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