Remove unnecessary response format check.

.semversioner/next-release/patch-20260206194808781905.json

@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Remove unnecessary response format check. Fixes: #2203"
+}

packages/graphrag-llm/graphrag_llm/completion/completion.py

@@ -77,17 +77,6 @@ def __init__(
         """
         raise NotImplementedError
 
-    @abstractmethod
-    def supports_structured_response(self) -> bool:
-        """Whether the completion supports structured responses.
-
-        Returns
-        -------
-            bool:
-                True if structured responses are supported, False otherwise.
-        """
-        raise NotImplementedError
-
     @abstractmethod
     def completion(
         self,

packages/graphrag-llm/graphrag_llm/completion/lite_llm_completion.py

@@ -8,7 +8,7 @@
 
 import litellm
 from azure.identity import DefaultAzureCredential, get_bearer_token_provider
-from litellm import ModelResponse, supports_response_schema  # type: ignore
+from litellm import ModelResponse  # type: ignore
 
 from graphrag_llm.completion.completion import LLMCompletion
 from graphrag_llm.config.types import AuthMethod
@@ -128,10 +128,6 @@ def __init__(
             retrier=self._retrier,
         )
 
-    def supports_structured_response(self) -> bool:
-        """Check if the model supports structured response."""
-        return supports_response_schema(self._model_id)
-
     def completion(
         self,
         /,
@@ -140,9 +136,6 @@ def completion(
         """Sync completion method."""
         messages: LLMCompletionMessagesParam = kwargs.pop("messages")
         response_format = kwargs.pop("response_format", None)
-        if response_format and not self.supports_structured_response():
-            msg = f"Model '{self._model_id}' does not support response schemas."
-            raise ValueError(msg)
 
         is_streaming = kwargs.get("stream") or False
 
@@ -182,11 +175,6 @@ async def completion_async(
         """Async completion method."""
         messages: LLMCompletionMessagesParam = kwargs.pop("messages")
         response_format = kwargs.pop("response_format", None)
-        if response_format and not supports_response_schema(
-            self._model_id,
-        ):
-            msg = f"Model '{self._model_id}' does not support response schemas."
-            raise ValueError(msg)
 
         is_streaming = kwargs.get("stream") or False
 

packages/graphrag-llm/graphrag_llm/completion/mock_llm_completion.py

@@ -87,10 +87,6 @@ def __init__(
 
         self._mock_responses = mock_responses  # type: ignore
 
-    def supports_structured_response(self) -> bool:
-        """Check if the model supports structured response."""
-        return True
-
     def completion(
         self,
         /,

packages/graphrag-llm/notebooks/03_structured_responses.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "a79c242b",
    "metadata": {},
    "outputs": [
@@ -22,10 +22,10 @@
      "text": [
       "City: Seattle\n",
       "  Temperature: 11.1 °C\n",
-      "  Condition: sunny\n",
+      "  Condition: Sunny\n",
       "City: San Francisco\n",
       "  Temperature: 23.9 °C\n",
-      "  Condition: cloudy\n"
+      "  Condition: Cloudy\n"
      ]
     }
    ],
@@ -84,32 +84,6 @@
     "    print(f\"  Condition: {report.condition}\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6dcfa20c",
-   "metadata": {},
-   "source": [
-    "## Checking for support\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "aa1edadb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Supports structured responses: True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Supports structured responses: {llm_completion.supports_structured_response()}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "6360f512",

packages/graphrag-llm/pyproject.toml

@@ -32,7 +32,7 @@ classifiers = [
     "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
-    "azure-identity~=1.19.0",
+    "azure-identity~=1.25",
     "graphrag-cache==3.0.1",
     "graphrag-common==3.0.1",
     "jinja2~=3.1",

packages/graphrag-storage/pyproject.toml

@@ -32,7 +32,7 @@ classifiers = [
 dependencies = [
     "aiofiles~=24.1",
     "azure-cosmos~=4.9",
-    "azure-identity~=1.19",
+    "azure-identity~=1.25",
     "azure-storage-blob~=12.24",
     "graphrag-common==3.0.1",
     "pandas~=2.3",

packages/graphrag-vectors/pyproject.toml

@@ -32,7 +32,7 @@ classifiers = [
 dependencies = [
     "azure-core~=1.32",
     "azure-cosmos~=4.9",
-    "azure-identity~=1.19",
+    "azure-identity~=1.25",
     "azure-search-documents~=11.6",
     "graphrag-common==3.0.1",
     "lancedb~=0.24.1",

packages/graphrag/pyproject.toml

@@ -32,7 +32,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "azure-identity~=1.19",
+    "azure-identity~=1.25",
     "azure-search-documents~=11.5",
     "azure-storage-blob~=12.24",
     "devtools~=0.12",

tests/integration/language_model/test_factory.py

@@ -38,9 +38,6 @@ class CustomChatModel(LLMCompletion):
         def __init__(self, **kwargs):
             pass
 
-        def supports_structured_response(self) -> bool:
-            return True
-
         def completion(
             self,
             /,