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147 lines
5.2 KiB
Python
147 lines
5.2 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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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 ..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 api_key_not_found_help, TEXT
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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. List of acceptable models:
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* embed-english-v3.0
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* embed-multilingual-v3.0
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* embed-english-light-v3.0
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* embed-multilingual-light-v3.0
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* embed-english-v2.0
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* embed-english-light-v2.0
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* embed-multilingual-v2.0
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source_input_type: str, default "search_document"
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The input type for the source column in the database
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query_input_type: str, default "search_query"
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The input type for the query column in the database
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Cohere supports following input types:
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| Input Type | Description |
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|-------------------------|---------------------------------------|
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| "`search_document`" | Used for embeddings stored in a vector|
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| | database for search use-cases. |
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| "`search_query`" | Used for embeddings of search queries |
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| | run against a vector DB |
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| "`semantic_similarity`" | Specifies the given text will be used |
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| | for Semantic Textual Similarity (STS) |
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| "`classification`" | Used for embeddings passed through a |
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| | text classifier. |
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| "`clustering`" | Used for the embeddings run through a |
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| | clustering algorithm |
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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
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.get_instance()
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.get("cohere")
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.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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source_input_type: str = "search_document"
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query_input_type: str = "search_query"
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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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if self.name in [
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"embed-english-v3.0",
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"embed-multilingual-v3.0",
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"embed-english-light-v2.0",
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]:
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return 1024
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elif self.name in ["embed-english-light-v3.0", "embed-multilingual-light-v3.0"]:
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return 384
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elif self.name == "embed-english-v2.0":
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return 4096
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elif self.name == "embed-multilingual-v2.0":
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return 768
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else:
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raise ValueError(f"Model {self.name} not supported")
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def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
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return self.compute_source_embeddings(query, input_type=self.query_input_type)
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def compute_source_embeddings(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
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texts = self.sanitize_input(texts)
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input_type = (
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kwargs.get("input_type") or self.source_input_type
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) # assume source input type if not passed by `compute_query_embeddings`
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return self.generate_embeddings(texts, input_type=input_type)
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def generate_embeddings(
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self, texts: Union[List[str], np.ndarray], *args, **kwargs
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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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self._init_client()
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rs = CohereEmbeddingFunction.client.embed(
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texts=texts, model=self.name, **kwargs
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)
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return [emb for emb in rs.embeddings]
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def _init_client(self):
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cohere = attempt_import_or_raise("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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