mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import (
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BaseModel,
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Extra,
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Field,
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SecretStr,
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root_validator,
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)
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_community.utilities.requests import Requests
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class EdenAiEmbeddings(BaseModel, Embeddings):
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"""EdenAI embedding.
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environment variable ``EDENAI_API_KEY`` set with your API key, or pass
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it as a named parameter.
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"""
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edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token")
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provider: str = "openai"
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"""embedding provider to use (eg: openai,google etc.)"""
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model: Optional[str] = None
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"""
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model name for above provider (eg: 'gpt-3.5-turbo-instruct' for openai)
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available models are shown on https://docs.edenai.co/ under 'available providers'
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"""
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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 exists in environment."""
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values["edenai_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "edenai_api_key", "EDENAI_API_KEY")
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)
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return values
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@staticmethod
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def get_user_agent() -> str:
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from langchain_community import __version__
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return f"langchain/{__version__}"
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def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
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"""Compute embeddings using EdenAi api."""
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url = "https://api.edenai.run/v2/text/embeddings"
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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"authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
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"User-Agent": self.get_user_agent(),
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}
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payload: Dict[str, Any] = {"texts": texts, "providers": self.provider}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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request = Requests(headers=headers)
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response = request.post(url=url, data=payload)
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if response.status_code >= 500:
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raise Exception(f"EdenAI Server: Error {response.status_code}")
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elif response.status_code >= 400:
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raise ValueError(f"EdenAI received an invalid payload: {response.text}")
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elif response.status_code != 200:
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raise Exception(
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f"EdenAI returned an unexpected response with status "
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f"{response.status_code}: {response.text}"
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)
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temp = response.json()
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provider_response = temp[self.provider]
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if provider_response.get("status") == "fail":
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err_msg = provider_response.get("error", {}).get("message")
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raise Exception(err_msg)
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embeddings = []
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for embed_item in temp[self.provider]["items"]:
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embedding = embed_item["embedding"]
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embeddings.append(embedding)
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return embeddings
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed a list of documents using EdenAI.
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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._generate_embeddings(texts)
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using EdenAI.
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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._generate_embeddings([text])[0]
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