mirror of https://github.com/hwchase17/langchain
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"""Wrappers around embedding modules."""
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from langchain.embeddings.cohere import CohereEmbeddings
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.embeddings.openai import OpenAIEmbeddings
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__all__ = ["OpenAIEmbeddings"]
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__all__ = ["OpenAIEmbeddings", "HuggingFaceEmbeddings", "CohereEmbeddings"]
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"""Wrapper around Cohere embedding models."""
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import os
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Extra, root_validator
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from langchain.embeddings.base import Embeddings
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class CohereEmbeddings(BaseModel, Embeddings):
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"""Wrapper around Cohere embedding models.
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To use, you should have the ``cohere`` python package installed, and the
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environment variable ``COHERE_API_KEY`` set with your API key or pass it
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as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain.embeddings import CohereEmbeddings
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cohere = CohereEmbeddings(model_name="medium", cohere_api_key="my-api-key")
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"""
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client: Any #: :meta private:
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model: str = "medium"
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"""Model name to use."""
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cohere_api_key: Optional[str] = os.environ.get("COHERE_API_KEY")
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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cohere_api_key = values.get("cohere_api_key")
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if cohere_api_key is None or cohere_api_key == "":
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raise ValueError(
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"Did not find Cohere API key, please add an environment variable"
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" `COHERE_API_KEY` which contains it, or pass `cohere_api_key` as a"
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" named parameter."
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)
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try:
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import cohere
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values["client"] = cohere.Client(cohere_api_key)
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except ImportError:
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raise ValueError(
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"Could not import cohere python package. "
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"Please it install it with `pip install cohere`."
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)
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Call out to Cohere's embedding endpoint.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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embeddings = self.client.embed(model=self.model, texts=texts).embeddings
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Call out to Cohere's embedding endpoint.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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embedding = self.client.embed(model=self.model, texts=[text]).embeddings[0]
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return embedding
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"""Test cohere embeddings."""
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from langchain.embeddings.cohere import CohereEmbeddings
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def test_cohere_embedding_documents() -> None:
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"""Test cohere embeddings."""
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documents = ["foo bar"]
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embedding = CohereEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 2048
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def test_cohere_embedding_query() -> None:
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"""Test cohere embeddings."""
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document = "foo bar"
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embedding = CohereEmbeddings()
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output = embedding.embed_query(document)
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assert len(output) == 2048
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