Source code for langchain_community.embeddings.edenai

from typing import Any, Dict, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
    BaseModel,
    Extra,
    Field,
    SecretStr,
    root_validator,
)
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_community.utilities.requests import Requests


[docs]class EdenAiEmbeddings(BaseModel, Embeddings): """EdenAI embedding. environment variable ``EDENAI_API_KEY`` set with your API key, or pass it as a named parameter. """ edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token") provider: str = "openai" """embedding provider to use (eg: openai,google etc.)""" model: Optional[str] = None """ model name for above provider (eg: 'gpt-3.5-turbo-instruct' for openai) available models are shown on https://docs.edenai.co/ under 'available providers' """ 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.""" values["edenai_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "edenai_api_key", "EDENAI_API_KEY") ) return values
[docs] @staticmethod def get_user_agent() -> str: from langchain_community import __version__ return f"langchain/{__version__}"
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]: """Compute embeddings using EdenAi api.""" url = "https://api.edenai.run/v2/text/embeddings" headers = { "accept": "application/json", "content-type": "application/json", "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } payload: Dict[str, Any] = {"texts": texts, "providers": self.provider} if self.model is not None: payload["settings"] = {self.provider: self.model} request = Requests(headers=headers) response = request.post(url=url, data=payload) if response.status_code >= 500: raise Exception(f"EdenAI Server: Error {response.status_code}") elif response.status_code >= 400: raise ValueError(f"EdenAI received an invalid payload: {response.text}") elif response.status_code != 200: raise Exception( f"EdenAI returned an unexpected response with status " f"{response.status_code}: {response.text}" ) temp = response.json() provider_response = temp[self.provider] if provider_response.get("status") == "fail": err_msg = provider_response.get("error", {}).get("message") raise Exception(err_msg) embeddings = [] for embed_item in temp[self.provider]["items"]: embedding = embed_item["embedding"] embeddings.append(embedding) return embeddings
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of documents using EdenAI. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ return self._generate_embeddings(texts)
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using EdenAI. Args: text: The text to embed. Returns: Embeddings for the text. """ return self._generate_embeddings([text])[0]