mirror of
https://github.com/lancedb/lancedb.git
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eb31d95fef
- Rename safe_import -> attempt_import_or_raise (closes https://github.com/lancedb/lancedb/pull/923) - Update docs - Add Notebook example (@changhiskhan you can use it for the talk. Comes with "open in colab" button) - Latency benchmark & results comparison, sanity check on real-world data - Updates the default openai model to gpt-4
132 lines
4.2 KiB
Python
132 lines
4.2 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("gte-text")
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class GteEmbeddings(TextEmbeddingFunction):
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"""
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An embedding function that uses GTE-LARGE MLX format(for Apple silicon devices only)
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as well as the standard cpu/gpu version from: https://huggingface.co/thenlper/gte-large.
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For Apple users, you will need the mlx package insalled, which can be done with:
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pip install mlx
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Parameters
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----------
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name: str, default "thenlper/gte-large"
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The name of the model to use.
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device: str, default "cpu"
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Sets the device type for the model.
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normalize: str, default "True"
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Controls normalize param in encode function for the transformer.
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mlx: bool, default False
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Controls which model to use. False for gte-large,True for the mlx version.
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Examples
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--------
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import lancedb
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import lancedb.embeddings.gte
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from lancedb.embeddings import get_registry
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from lancedb.pydantic import LanceModel, Vector
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import pandas as pd
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model = get_registry().get("gte-text").create() # mlx=True for Apple silicon
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class TextModel(LanceModel):
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text: str = model.SourceField()
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vector: Vector(model.ndims()) = model.VectorField()
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df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
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db = lancedb.connect("~/.lancedb")
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tbl = db.create_table("test", schema=TextModel, mode="overwrite")
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tbl.add(df)
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rs = tbl.search("hello").limit(1).to_pandas()
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"""
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name: str = "thenlper/gte-large"
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device: str = "cpu"
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normalize: bool = True
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mlx: bool = False
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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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if kwargs:
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self.mlx = kwargs.get("mlx", False)
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if self.mlx is True:
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self.name = "gte-mlx"
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@property
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def embedding_model(self):
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"""
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Get the embedding model specified by the flag,
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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.mlx is True:
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self._ndims = self.embedding_model.dims
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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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if self.mlx is True:
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return self.embedding_model.run(list(texts)).tolist()
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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 embedding model specified by the flag,
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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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if self.mlx is True:
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from .gte_mlx_model import Model
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return Model()
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else:
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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(
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self.name, device=self.device
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)
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