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a6bcbd007b
This changes `lancedb` from a "pure python" setuptools project to a maturin project and adds a rust lancedb dependency. The async python client is extremely minimal (only `connect` and `Connection.table_names` are supported). The purpose of this PR is to get the infrastructure in place for building out the rest of the async client. Although this is not technically a breaking change (no APIs are changing) it is still a considerable change in the way the wheels are built because they now include the native shared library.
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
# Copyright (c) 2023. LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Union
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import numpy as np
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from ..util import attempt_import_or_raise
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from .base import TextEmbeddingFunction
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from .registry import register
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from .utils import weak_lru
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@register("sentence-transformers")
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class SentenceTransformerEmbeddings(TextEmbeddingFunction):
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"""
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An embedding function that uses the sentence-transformers library
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https://huggingface.co/sentence-transformers
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"""
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name: str = "all-MiniLM-L6-v2"
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device: str = "cpu"
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normalize: bool = True
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self._ndims = None
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@property
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def embedding_model(self):
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"""
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Get the sentence-transformers embedding model specified by the
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name and device. This is cached so that the model is only loaded
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once per process.
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"""
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return self.get_embedding_model()
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def ndims(self):
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if self._ndims is None:
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self._ndims = len(self.generate_embeddings("foo")[0])
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return self._ndims
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def generate_embeddings(
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self, texts: Union[List[str], np.ndarray]
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) -> List[np.array]:
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"""
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Get the embeddings for the given texts
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Parameters
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----------
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texts: list[str] or np.ndarray (of str)
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The texts to embed
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"""
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return self.embedding_model.encode(
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list(texts),
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convert_to_numpy=True,
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normalize_embeddings=self.normalize,
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).tolist()
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@weak_lru(maxsize=1)
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def get_embedding_model(self):
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"""
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Get the sentence-transformers embedding model specified by the
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name and device. This is cached so that the model is only loaded
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once per process.
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TODO: use lru_cache instead with a reasonable/configurable maxsize
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"""
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sentence_transformers = attempt_import_or_raise(
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"sentence_transformers", "sentence-transformers"
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)
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return sentence_transformers.SentenceTransformer(self.name, device=self.device)
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