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https://github.com/lancedb/lancedb.git
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0293bbe142
Sets things up for this -> https://github.com/lancedb/lancedb/issues/579 - Just separates out the registry/ingestion code from the function implementation code - adds a `get_registry` util - package name "open-clip" -> "open-clip-torch"
88 lines
2.7 KiB
Python
88 lines
2.7 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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import os
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from typing import ClassVar, List, Union
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import numpy as np
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from .base import TextEmbeddingFunction
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from .registry import register
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from .utils import api_key_not_found_help
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@register("cohere")
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class CohereEmbeddingFunction(TextEmbeddingFunction):
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"""
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An embedding function that uses the Cohere API
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https://docs.cohere.com/docs/multilingual-language-models
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Parameters
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----------
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name: str, default "embed-multilingual-v2.0"
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The name of the model to use. See the Cohere documentation for a list of available models.
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Examples
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--------
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import lancedb
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from lancedb.pydantic import LanceModel, Vector
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from lancedb.embeddings import EmbeddingFunctionRegistry
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cohere = EmbeddingFunctionRegistry.get_instance().get("cohere").create(name="embed-multilingual-v2.0")
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class TextModel(LanceModel):
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text: str = cohere.SourceField()
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vector: Vector(cohere.ndims()) = cohere.VectorField()
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data = [ { "text": "hello world" },
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{ "text": "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(data)
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"""
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name: str = "embed-multilingual-v2.0"
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client: ClassVar = None
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def ndims(self):
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# TODO: fix hardcoding
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return 768
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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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# TODO retry, rate limit, token limit
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self._init_client()
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rs = CohereEmbeddingFunction.client.embed(texts=texts, model=self.name)
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return [emb for emb in rs.embeddings]
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def _init_client(self):
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cohere = self.safe_import("cohere")
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if CohereEmbeddingFunction.client is None:
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if os.environ.get("COHERE_API_KEY") is None:
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api_key_not_found_help("cohere")
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CohereEmbeddingFunction.client = cohere.Client(os.environ["COHERE_API_KEY"])
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