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8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
from typing import Any, Dict, List
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from langchain_core.embeddings import Embeddings
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from langchain_core.utils import get_from_dict_or_env, pre_init
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from pydantic import BaseModel
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class NLPCloudEmbeddings(BaseModel, Embeddings):
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"""NLP Cloud embedding models.
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To use, you should have the nlpcloud python package installed
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Example:
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.. code-block:: python
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from langchain_community.embeddings import NLPCloudEmbeddings
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embeddings = NLPCloudEmbeddings()
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"""
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model_name: str # Define model_name as a class attribute
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gpu: bool # Define gpu as a class attribute
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client: Any #: :meta private:
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def __init__(
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self,
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model_name: str = "paraphrase-multilingual-mpnet-base-v2",
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gpu: bool = False,
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**kwargs: Any,
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) -> None:
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super().__init__(model_name=model_name, gpu=gpu, **kwargs)
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@pre_init
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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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nlpcloud_api_key = get_from_dict_or_env(
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values, "nlpcloud_api_key", "NLPCLOUD_API_KEY"
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)
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try:
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import nlpcloud
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values["client"] = nlpcloud.Client(
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values["model_name"], nlpcloud_api_key, gpu=values["gpu"], lang="en"
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)
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except ImportError:
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raise ImportError(
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"Could not import nlpcloud python package. "
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"Please install it with `pip install nlpcloud`."
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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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"""Embed a list of documents using NLP Cloud.
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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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return self.client.embeddings(texts)["embeddings"]
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using NLP Cloud.
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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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return self.client.embeddings([text])["embeddings"][0]
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