import json
import os
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from openai import AzureOpenAI
from selfmemory.llms.base import LLMBase
from selfmemory.llms.configs import AzureOpenAIConfig, BaseLlmConfig
from selfmemory.memory.utils import extract_json
SCOPE = "https://cognitiveservices.azure.com/.default"
class AzureOpenAILLM(LLMBase):
def __init__(self, config: BaseLlmConfig | AzureOpenAIConfig | dict | None = None):
# Convert to AzureOpenAIConfig if needed
if config is None:
config = AzureOpenAIConfig()
elif isinstance(config, dict):
config = AzureOpenAIConfig(**config)
elif isinstance(config, BaseLlmConfig) and not isinstance(
config, AzureOpenAIConfig
):
# Convert BaseLlmConfig to AzureOpenAIConfig
config = AzureOpenAIConfig(
model=config.model,
temperature=config.temperature,
api_key=config.api_key,
max_tokens=config.max_tokens,
top_p=config.top_p,
top_k=config.top_k,
enable_vision=config.enable_vision,
vision_details=config.vision_details,
http_client_proxies=config.http_client,
)
super().__init__(config)
# Model name should match the custom deployment name chosen for it.
if not self.config.model:
self.config.model = "gpt-4.1-nano-2025-04-14"
api_key = self.config.azure_kwargs.api_key or os.getenv(
"LLM_AZURE_OPENAI_API_KEY"
)
azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv(
"LLM_AZURE_DEPLOYMENT"
)
azure_endpoint = self.config.azure_kwargs.azure_endpoint or os.getenv(
"LLM_AZURE_ENDPOINT"
)
api_version = self.config.azure_kwargs.api_version or os.getenv(
"LLM_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 _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(
extract_json(tool_call.function.arguments)
),
}
)
return processed_response
return response.choices[0].message.content
def generate_response(
self,
messages: list[dict[str, str]],
response_format=None,
tools: list[dict] | None = None,
tool_choice: str = "auto",
**kwargs,
):
"""
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".
**kwargs: Additional Azure OpenAI-specific parameters.
Returns:
str: The generated response.
"""
user_prompt = messages[-1]["content"]
user_prompt = user_prompt.replace("assistant", "ai")
messages[-1]["content"] = user_prompt
params = self._get_supported_params(messages=messages, **kwargs)
# Add model and messages
params.update(
{
"model": self.config.model,
"messages": messages,
}
)
if tools:
params["tools"] = tools
params["tool_choice"] = tool_choice
response = self.client.chat.completions.create(**params)
return self._parse_response(response, tools)
