Support Azure OpenAI LLM (#1581)

mem0/llms/azure_openai.py

@@ -0,0 +1,80 @@
+import json
+from typing import Dict, List, Optional
+
+from openai import AzureOpenAI
+
+from mem0.llms.base import LLMBase
+from mem0.configs.llms.base import BaseLlmConfig
+
+class AzureOpenAILLM(LLMBase):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config)
+
+        # Model name should match the custom deployment name chosen for it.
+        if not self.config.model:
+            self.config.model="gpt-4o"
+        self.client = AzureOpenAI()
+
+    def _parse_response(self, response, tools):
+        """
+        Process the response based on whether tools are used or not.
+
+        Args:
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
+
+        Returns:
+            str or dict: The processed response.
+        """
+        if tools:
+            processed_response = {
+                "content": response.choices[0].message.content,
+                "tool_calls": []
+            }
+
+            if response.choices[0].message.tool_calls:
+                for tool_call in response.choices[0].message.tool_calls:
+                    processed_response["tool_calls"].append({
+                        "name": tool_call.function.name,
+                        "arguments": json.loads(tool_call.function.arguments)
+                    })
+
+            return processed_response
+        else:
+            return response.choices[0].message.content
+
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+    ):
+        """
+        Generate a response based on the given messages using Azure OpenAI.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response. Defaults to "text".
+            tools (list, optional): List of tools that the model can call. Defaults to None.
+            tool_choice (str, optional): Tool choice method. Defaults to "auto".
+
+        Returns:
+            str: The generated response.
+        """
+        params = {
+            "model": self.config.model, 
+            "messages": messages, 
+            "temperature": self.config.temperature, 
+            "max_tokens": self.config.max_tokens, 
+            "top_p": self.config.top_p
+        }
+        if response_format:
+            params["response_format"] = response_format
+        if tools:
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
+
+        response = self.client.chat.completions.create(**params)
+        return self._parse_response(response, tools)

mem0/llms/configs.py

@@ -14,7 +14,7 @@ class LlmConfig(BaseModel):
     @field_validator("config")
     def validate_config(cls, v, values):
         provider = values.data.get("provider")
-        if provider in ("openai", "ollama", "groq", "together", "aws_bedrock", "litellm"):
+        if provider in ("openai", "ollama", "groq", "together", "aws_bedrock", "litellm", "azure_openai"):
             return v
         else:
             raise ValueError(f"Unsupported LLM provider: {provider}")

mem0/utils/factory.py

@@ -18,6 +18,7 @@ class LlmFactory:
         "aws_bedrock": "mem0.llms.aws_bedrock.AWSBedrockLLM",
         "litellm": "mem0.llms.litellm.LiteLLM",
         "ollama": "mem0.llms.ollama.OllamaLLM",
+        "azure_openai": "mem0.llms.azure_openai.AzureOpenAILLM",
     }
 
     @classmethod