mirror of
https://github.com/lancedb/lancedb.git
synced 2026-08-31 10:38:31 +00:00
Will Jones
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
|
|
|
|
|
|
from typing import List, Union
|
|
|
|
import numpy as np
|
|
|
|
from ..util import attempt_import_or_raise
|
|
from .base import TextEmbeddingFunction
|
|
from .registry import register
|
|
from .utils import weak_lru
|
|
|
|
|
|
@register("sentence-transformers")
|
|
class SentenceTransformerEmbeddings(TextEmbeddingFunction):
|
|
"""
|
|
An embedding function that uses the sentence-transformers library
|
|
|
|
https://huggingface.co/sentence-transformers
|
|
|
|
Parameters
|
|
----------
|
|
name: str, default "all-MiniLM-L6-v2"
|
|
The name of the model to use.
|
|
device: str, default "cpu"
|
|
The device to use for the model
|
|
normalize: bool, default True
|
|
Whether to normalize the embeddings
|
|
trust_remote_code: bool, default True
|
|
Whether to trust the remote code
|
|
"""
|
|
|
|
name: str = "all-MiniLM-L6-v2"
|
|
device: str = "cpu"
|
|
normalize: bool = True
|
|
trust_remote_code: bool = True
|
|
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self._ndims = None
|
|
|
|
@property
|
|
def embedding_model(self):
|
|
"""
|
|
Get the sentence-transformers embedding model specified by the
|
|
name, device, and trust_remote_code. This is cached so that the
|
|
model is only loaded once per process.
|
|
"""
|
|
return self.get_embedding_model()
|
|
|
|
def ndims(self):
|
|
if self._ndims is None:
|
|
self._ndims = len(self.generate_embeddings("foo")[0])
|
|
return self._ndims
|
|
|
|
def generate_embeddings(
|
|
self, texts: Union[List[str], np.ndarray]
|
|
) -> List[np.array]:
|
|
"""
|
|
Get the embeddings for the given texts
|
|
|
|
Parameters
|
|
----------
|
|
texts: list[str] or np.ndarray (of str)
|
|
The texts to embed
|
|
"""
|
|
return self.embedding_model.encode(
|
|
list(texts),
|
|
convert_to_numpy=True,
|
|
normalize_embeddings=self.normalize,
|
|
).tolist()
|
|
|
|
@weak_lru(maxsize=1)
|
|
def get_embedding_model(self):
|
|
"""
|
|
Get the sentence-transformers embedding model specified by the
|
|
name, device, and trust_remote_code. This is cached so that the
|
|
model is only loaded once per process.
|
|
|
|
TODO: use lru_cache instead with a reasonable/configurable maxsize
|
|
"""
|
|
sentence_transformers = attempt_import_or_raise(
|
|
"sentence_transformers", "sentence-transformers"
|
|
)
|
|
return sentence_transformers.SentenceTransformer(
|
|
self.name, device=self.device, trust_remote_code=self.trust_remote_code
|
|
)
|