"""Azure OpenAI client implementation.""" import os from typing import Any, Optional from langchain_openai import AzureChatOpenAI from .base_client import BaseLLMClient class AzureClient(BaseLLMClient): """Client for Azure OpenAI provider. Required environment variables: AZURE_OPENAI_API_KEY: Your Azure OpenAI API key AZURE_OPENAI_ENDPOINT: Your Azure endpoint (e.g., https://your-resource.openai.azure.com/) OPENAI_API_VERSION: API version (e.g., 2024-02-15-preview) Optional: AZURE_DEPLOYMENT_NAME: Your deployment name (can also be passed as model parameter) """ def __init__( self, model: str, base_url: Optional[str] = None, **kwargs, ): super().__init__(model, base_url, **kwargs) # In Azure, model is the deployment name self.deployment_name = model def get_llm(self) -> Any: """Return configured AzureChatOpenAI instance.""" llm_kwargs = { "azure_deployment": self.deployment_name, } # Pass through Azure-specific kwargs for key in ("timeout", "max_retries", "callbacks", "api_version", "azure_endpoint", "api_key", "http_client", "http_async_client"): if key in self.kwargs: llm_kwargs[key] = self.kwargs[key] # Environment variable fallbacks if "azure_endpoint" not in llm_kwargs: endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") if endpoint: llm_kwargs["azure_endpoint"] = endpoint if "api_key" not in llm_kwargs: api_key = os.environ.get("AZURE_OPENAI_API_KEY") if api_key: llm_kwargs["api_key"] = api_key if "api_version" not in llm_kwargs: api_version = os.environ.get("OPENAI_API_VERSION", "2024-02-15-preview") llm_kwargs["api_version"] = api_version return AzureChatOpenAI(**llm_kwargs) def validate_model(self) -> bool: """Validate Azure deployment name (always valid, Azure validates at runtime).""" return bool(self.deployment_name)