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#1	import os
#2	from typing import Literal, Optional
#3
#4	from azure.identity import DefaultAzureCredential, get_bearer_token_provider
#5	from openai import AzureOpenAI
#6
#7	from mem0.configs.embeddings.base import BaseEmbedderConfig
#8	from mem0.embeddings.base import EmbeddingBase
#9
#10	SCOPE = "https://cognitiveservices.azure.com/.default"
#11
#12
#13	class AzureOpenAIEmbedding(EmbeddingBase):
#14	def __init__(self, config: Optional[BaseEmbedderConfig] = None):
#15	super().__init__(config)
#16
#17	api_key = self.config.azure_kwargs.api_key or os.getenv("EMBEDDING_AZURE_OPENAI_API_KEY")
#18	azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("EMBEDDING_AZURE_DEPLOYMENT")
#19	azure_endpoint = self.config.azure_kwargs.azure_endpoint or os.getenv("EMBEDDING_AZURE_ENDPOINT")
#20	api_version = self.config.azure_kwargs.api_version or os.getenv("EMBEDDING_AZURE_API_VERSION")
#21	default_headers = self.config.azure_kwargs.default_headers
#22
#23	# If the API key is not provided or is a placeholder, use DefaultAzureCredential.
#24	if api_key is None or api_key == "" or api_key == "your-api-key":
#25	self.credential = DefaultAzureCredential()
#26	azure_ad_token_provider = get_bearer_token_provider(
#27	self.credential,
#28	SCOPE,
#29	)
#30	api_key = None
#31	else:
#32	azure_ad_token_provider = None
#33
#34	self.client = AzureOpenAI(
#35	azure_deployment=azure_deployment,
#36	azure_endpoint=azure_endpoint,
#37	azure_ad_token_provider=azure_ad_token_provider,
#38	api_version=api_version,
#39	api_key=api_key,
#40	http_client=self.config.http_client,
#41	default_headers=default_headers,
#42	)
#43
#44	def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
#45	"""
#46	Get the embedding for the given text using OpenAI.
#47
#48	Args:
#49	text (str): The text to embed.
#50	memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
#51	Returns:
#52	list: The embedding vector.
#53	"""
#54	text = text.replace("\n", " ")
#55	return self.client.embeddings.create(input=[text], model=self.config.model).data[0].embedding
#56

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