Source code for langchain_community.embeddings.llm_rails

""" This file is for LLMRails Embedding """
from typing import Dict, List, Optional

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

[docs]class LLMRailsEmbeddings(BaseModel, Embeddings): """LLMRails embedding models. To use, you should have the environment variable ``LLM_RAILS_API_KEY`` set with your API key or pass it as a named parameter to the constructor. Model can be one of ["embedding-english-v1","embedding-multi-v1"] Example: .. code-block:: python from langchain_community.embeddings import LLMRailsEmbeddings cohere = LLMRailsEmbeddings( model="embedding-english-v1", api_key="my-api-key" ) """ model: str = "embedding-english-v1" """Model name to use.""" api_key: Optional[SecretStr] = None """LLMRails API key.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key exists in environment.""" api_key = convert_to_secret_str( get_from_dict_or_env(values, "api_key", "LLM_RAILS_API_KEY") ) values["api_key"] = api_key return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to Cohere's embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ response = "", headers={"X-API-KEY": self.api_key.get_secret_value()}, # type: ignore[union-attr] json={"input": texts, "model": self.model}, timeout=60, ) return [item["embedding"] for item in response.json()["data"]]
[docs] def embed_query(self, text: str) -> List[float]: """Call out to Cohere's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.embed_documents([text])[0]