32 lines
967 B
Python
32 lines
967 B
Python
from typing import Optional
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from sentence_transformers import SentenceTransformer
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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class HuggingFaceEmbedding(EmbeddingBase):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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if self.config.model is None:
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self.config.model = "multi-qa-MiniLM-L6-cos-v1"
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self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)
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if self.config.embedding_dims is None:
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self.config.embedding_dims = self.model.get_sentence_embedding_dimension()
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def embed(self, text):
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"""
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Get the embedding for the given text using Hugging Face.
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Args:
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text (str): The text to embed.
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Returns:
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list: The embedding vector.
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"""
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return self.model.encode(text, convert_to_numpy = True).tolist()
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