add first-class support for Azure OpenAI

core/config/__init__.py

@@ -1,6 +1,6 @@
 from enum import Enum
 from os.path import abspath, dirname, isdir, join
-from typing import Literal, Optional, Union
+from typing import Any, Literal, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 from typing_extensions import Annotated
@@ -55,6 +55,7 @@ class LLMProvider(str, Enum):
     ANTHROPIC = "anthropic"
     GROQ = "groq"
     LM_STUDIO = "lm-studio"
+    AZURE = "azure"
 
 
 class UIAdapter(str, Enum):
@@ -89,6 +90,10 @@ class ProviderConfig(_StrictModel):
         description="Timeout (in seconds) for receiving a new chunk of data from the response stream",
         ge=0.0,
     )
+    extra: Optional[dict[str, Any]] = Field(
+        None,
+        description="Extra provider-specific configuration",
+    )
 
 
 class AgentLLMConfig(_StrictModel):
@@ -140,6 +145,10 @@ class LLMConfig(_StrictModel):
         description="Timeout (in seconds) for receiving a new chunk of data from the response stream",
         ge=0.0,
     )
+    extra: Optional[dict[str, Any]] = Field(
+        None,
+        description="Extra provider-specific configuration",
+    )
 
     @classmethod
     def from_provider_and_agent_configs(cls, provider: ProviderConfig, agent: AgentLLMConfig):
@@ -151,6 +160,7 @@ def from_provider_and_agent_configs(cls, provider: ProviderConfig, agent: AgentL
             temperature=agent.temperature,
             connect_timeout=provider.connect_timeout,
             read_timeout=provider.read_timeout,
+            extra=provider.extra,
         )
 
 

core/llm/azure_client.py

@@ -0,0 +1,29 @@
+from httpx import Timeout
+from openai import AsyncAzureOpenAI
+
+from core.config import LLMProvider
+from core.llm.openai_client import OpenAIClient
+from core.log import get_logger
+
+log = get_logger(__name__)
+
+
+class AzureClient(OpenAIClient):
+    provider = LLMProvider.AZURE
+    stream_options = None
+
+    def _init_client(self):
+        azure_deployment = self.config.extra.get("azure_deployment")
+        api_version = self.config.extra.get("api_version")
+
+        self.client = AsyncAzureOpenAI(
+            api_key=self.config.api_key,
+            azure_endpoint=self.config.base_url,
+            azure_deployment=azure_deployment,
+            api_version=api_version,
+            timeout=Timeout(
+                max(self.config.connect_timeout, self.config.read_timeout),
+                connect=self.config.connect_timeout,
+                read=self.config.read_timeout,
+            ),
+        )

core/llm/base.py

@@ -316,6 +316,7 @@ def for_provider(provider: LLMProvider) -> type["BaseLLMClient"]:
         :return: Client class for the specified provider.
         """
         from .anthropic_client import AnthropicClient
+        from .azure_client import AzureClient
         from .groq_client import GroqClient
         from .openai_client import OpenAIClient
 
@@ -325,6 +326,8 @@ def for_provider(provider: LLMProvider) -> type["BaseLLMClient"]:
             return AnthropicClient
         elif provider == LLMProvider.GROQ:
             return GroqClient
+        elif provider == LLMProvider.AZURE:
+            return AzureClient
         else:
             raise ValueError(f"Unsupported LLM provider: {provider.value}")
 

core/llm/openai_client.py

@@ -17,6 +17,7 @@
 
 class OpenAIClient(BaseLLMClient):
     provider = LLMProvider.OPENAI
+    stream_options = {"include_usage": True}
 
     def _init_client(self):
         self.client = AsyncOpenAI(
@@ -40,10 +41,10 @@ async def _make_request(
             "messages": convo.messages,
             "temperature": self.config.temperature if temperature is None else temperature,
             "stream": True,
-            "stream_options": {
-                "include_usage": True,
-            },
         }
+        if self.stream_options:
+            completion_kwargs["stream_options"] = self.stream_options
+
         if json_mode:
             completion_kwargs["response_format"] = {"type": "json_object"}
 

example-config.json

@@ -1,6 +1,6 @@
 {
   // Configuration for the LLM providers that can be used. Pythagora supports
-  // OpenAI, Anthropic and Groq. Azure and OpenRouter and local LLMs (such as LM-Studio)
+  // OpenAI, Azure, Anthropic and Groq. OpenRouter and local LLMs (such as LM-Studio)
   // also work, you can use "openai" provider to define these.
   "llm": {
     "openai": {
@@ -9,6 +9,17 @@
       "api_key": null,
       "connect_timeout": 60.0,
       "read_timeout": 10.0
+    },
+    // Example config for Azure OpenAI (see https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions)
+    "azure": {
+      "base_url": "https://your-resource-name.openai.azure.com/",
+      "api_key": "your-api-key",
+      "connect_timeout": 60.0,
+      "read_timeout": 10.0,
+      "extra": {
+        "azure_deployment": "your-azure-deployment-id",
+        "api_version": "2024-02-01"
+      }
     }
   },
   // Each agent can use a different model or configuration. The default, as before, is GPT4 Turbo