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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>
75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
"""This file is for LLMRails Embedding"""
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from typing import Dict, List, Optional
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import requests
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from langchain_core.embeddings import Embeddings
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
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from pydantic import BaseModel, ConfigDict, SecretStr
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class LLMRailsEmbeddings(BaseModel, Embeddings):
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"""LLMRails embedding models.
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To use, you should have the environment
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variable ``LLM_RAILS_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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Model can be one of ["embedding-english-v1","embedding-multi-v1"]
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Example:
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.. code-block:: python
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from langchain_community.embeddings import LLMRailsEmbeddings
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cohere = LLMRailsEmbeddings(
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model="embedding-english-v1", api_key="my-api-key"
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)
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"""
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model: str = "embedding-english-v1"
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"""Model name to use."""
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api_key: Optional[SecretStr] = None
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"""LLMRails API key."""
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model_config = ConfigDict(
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extra="forbid",
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)
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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 exists in environment."""
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api_key = convert_to_secret_str(
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get_from_dict_or_env(values, "api_key", "LLM_RAILS_API_KEY")
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)
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values["api_key"] = api_key
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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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response = requests.post(
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"https://api.llmrails.com/v1/embeddings",
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headers={"X-API-KEY": self.api_key.get_secret_value()}, # type: ignore[union-attr]
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json={"input": texts, "model": self.model},
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timeout=60,
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)
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return [item["embedding"] for item in response.json()["data"]]
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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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return self.embed_documents([text])[0]
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