feat: added support for azure open ai api

Author: muhammadyaseen

.env.example

@@ -14,6 +14,14 @@ TRAINEE_MODEL=gpt-4o-mini
 TRAINEE_BASE_URL=
 TRAINEE_API_KEY=
 
+# azure_openai_api
+# SYNTHESIZER_BACKEND=azure_openai_api
+# The following is the same as your "Deployment name" in Azure
+# SYNTHESIZER_MODEL=<your-deployment-name>
+# SYNTHESIZER_BASE_URL=https://<your-resource-name>.openai.azure.com/openai/deployments/<your-deployment-name>/chat/completions
+# SYNTHESIZER_API_KEY=
+# SYNTHESIZER_API_VERSION=<api-version>
+
 # # ollama_api
 # SYNTHESIZER_BACKEND=ollama_api
 # SYNTHESIZER_MODEL=gemma3

graphgen/models/llm/api/openai_client.py

@@ -2,7 +2,8 @@
 from typing import Any, Dict, List, Optional
 
 import openai
-from openai import APIConnectionError, APITimeoutError, AsyncOpenAI, RateLimitError
+from openai import APIConnectionError, APITimeoutError, AsyncOpenAI, AsyncAzureOpenAI, RateLimitError
+from pyparsing import Literal
 from tenacity import (
     retry,
     retry_if_exception_type,
@@ -35,17 +36,20 @@ def __init__(
         model: str = "gpt-4o-mini",
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
+        api_version: Optional[str] = None,
         json_mode: bool = False,
         seed: Optional[int] = None,
         topk_per_token: int = 5,  # number of topk tokens to generate for each token
         request_limit: bool = False,
         rpm: Optional[RPM] = None,
         tpm: Optional[TPM] = None,
+        backend: str = "openai_api",
         **kwargs: Any,
     ):
         super().__init__(**kwargs)
         self.model = model
         self.api_key = api_key
+        self.api_version = api_version # required for Azure OpenAI
         self.base_url = base_url
         self.json_mode = json_mode
         self.seed = seed
@@ -56,13 +60,31 @@ def __init__(
         self.rpm = rpm or RPM()
         self.tpm = tpm or TPM()
 
+        assert backend in ["openai_api", "azure_openai_api"], f"Unsupported backend {backend}. Use 'openai_api' or 'azure_openai_api'."
+        self.backend = backend
+
         self.__post_init__()
 
     def __post_init__(self):
-        assert self.api_key is not None, "Please provide api key to access openai api."
-        self.client = AsyncOpenAI(
-            api_key=self.api_key or "dummy", base_url=self.base_url
-        )
+
+        api_name = self.backend.replace("_", " ") 
+        assert self.api_key is not None, f"Please provide api key to access {api_name}."
+
+        if self.backend == "openai_api":    
+            self.client = AsyncOpenAI(
+                api_key=self.api_key or "dummy", base_url=self.base_url
+            )
+        elif self.backend == "azure_openai_api":
+            assert self.api_version is not None, f"Please provide api_version for {api_name}."
+            assert self.base_url is not None, f"Please provide base_url for {api_name}."
+            self.client = AsyncAzureOpenAI(
+                api_key=self.api_key,
+                azure_endpoint=self.base_url,
+                api_version=self.api_version,
+                azure_deployment=self.model,
+            )
+        else:
+            raise ValueError(f"Unsupported backend {self.backend}. Use 'openai_api' or 'azure_openai_api'.")
 
     def _pre_generate(self, text: str, history: List[str]) -> Dict:
         kwargs = {

graphgen/operators/init/init_llm.py

@@ -27,10 +27,11 @@ def create_llm_wrapper(backend: str, config: Dict[str, Any]) -> BaseLLMWrapper:
             from graphgen.models.llm.api.http_client import HTTPClient
 
             return HTTPClient(**config)
-        if backend == "openai_api":
+        if backend == "openai_api" or backend == "azure_openai_api":
             from graphgen.models.llm.api.openai_client import OpenAIClient
-
-            return OpenAIClient(**config)
+            
+            # pass in concrete backend to the OpenAIClient so that internally we can distinguish between OpenAI and Azure OpenAI
+            return OpenAIClient(**config, backend=backend)
         if backend == "ollama_api":
             from graphgen.models.llm.api.ollama_client import OllamaClient