first commit

embeddings/azure_openai.py

@@ -0,0 +1,55 @@
+import os
+from typing import Literal, Optional
+
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from openai import AzureOpenAI
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
+
+SCOPE = "https://cognitiveservices.azure.com/.default"
+
+
+class AzureOpenAIEmbedding(EmbeddingBase):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        api_key = self.config.azure_kwargs.api_key or os.getenv("EMBEDDING_AZURE_OPENAI_API_KEY")
+        azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("EMBEDDING_AZURE_DEPLOYMENT")
+        azure_endpoint = self.config.azure_kwargs.azure_endpoint or os.getenv("EMBEDDING_AZURE_ENDPOINT")
+        api_version = self.config.azure_kwargs.api_version or os.getenv("EMBEDDING_AZURE_API_VERSION")
+        default_headers = self.config.azure_kwargs.default_headers
+
+        # If the API key is not provided or is a placeholder, use DefaultAzureCredential.
+        if api_key is None or api_key == "" or api_key == "your-api-key":
+            self.credential = DefaultAzureCredential()
+            azure_ad_token_provider = get_bearer_token_provider(
+                self.credential,
+                SCOPE,
+            )
+            api_key = None
+        else:
+            azure_ad_token_provider = None
+
+        self.client = AzureOpenAI(
+            azure_deployment=azure_deployment,
+            azure_endpoint=azure_endpoint,
+            azure_ad_token_provider=azure_ad_token_provider,
+            api_version=api_version,
+            api_key=api_key,
+            http_client=self.config.http_client,
+            default_headers=default_headers,
+        )
+
+    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
+        """
+        Get the embedding for the given text using OpenAI.
+
+        Args:
+            text (str): The text to embed.
+            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
+        Returns:
+            list: The embedding vector.
+        """
+        text = text.replace("\n", " ")
+        return self.client.embeddings.create(input=[text], model=self.config.model).data[0].embedding