diff --git a/.semversioner/next-release/patch-20260206194808781905.json b/.semversioner/next-release/patch-20260206194808781905.json
new file mode 100644
index 000000000..6f6638f43
--- /dev/null
+++ b/.semversioner/next-release/patch-20260206194808781905.json
@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Remove unnecessary response format check. Fixes: #2203"
+}
diff --git a/.semversioner/next-release/patch-20260206205026841660.json b/.semversioner/next-release/patch-20260206205026841660.json
new file mode 100644
index 000000000..db0635dd4
--- /dev/null
+++ b/.semversioner/next-release/patch-20260206205026841660.json
@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Add table provider factory."
+}
diff --git a/packages/graphrag-cache/graphrag_cache/cache_factory.py b/packages/graphrag-cache/graphrag_cache/cache_factory.py
index 6b1310754..33a51099f 100644
--- a/packages/graphrag-cache/graphrag_cache/cache_factory.py
+++ b/packages/graphrag-cache/graphrag_cache/cache_factory.py
@@ -5,20 +5,14 @@
 """Cache factory implementation."""
 
 from collections.abc import Callable
-from typing import TYPE_CHECKING
 
-from graphrag_common.factory import Factory
-from graphrag_storage import create_storage
+from graphrag_common.factory import Factory, ServiceScope
+from graphrag_storage import Storage, create_storage
 
+from graphrag_cache.cache import Cache
 from graphrag_cache.cache_config import CacheConfig
 from graphrag_cache.cache_type import CacheType
 
-if TYPE_CHECKING:
-    from graphrag_common.factory import ServiceScope
-    from graphrag_storage import Storage
-
-    from graphrag_cache.cache import Cache
-
 
 class CacheFactory(Factory["Cache"]):
     """A factory class for cache implementations."""
@@ -29,8 +23,8 @@ class CacheFactory(Factory["Cache"]):
 
 def register_cache(
     cache_type: str,
-    cache_initializer: Callable[..., "Cache"],
-    scope: "ServiceScope" = "transient",
+    cache_initializer: Callable[..., Cache],
+    scope: ServiceScope = "transient",
 ) -> None:
     """Register a custom cache implementation.
 
@@ -45,7 +39,7 @@ def register_cache(
 
 
 def create_cache(
-    config: CacheConfig | None = None, storage: "Storage | None" = None
+    config: CacheConfig | None = None, storage: Storage | None = None
 ) -> "Cache":
     """Create a cache implementation based on the given configuration.
 
diff --git a/packages/graphrag-llm/graphrag_llm/completion/completion.py b/packages/graphrag-llm/graphrag_llm/completion/completion.py
index 0debab273..be8ee4ae1 100644
--- a/packages/graphrag-llm/graphrag_llm/completion/completion.py
+++ b/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,
diff --git a/packages/graphrag-llm/graphrag_llm/completion/lite_llm_completion.py b/packages/graphrag-llm/graphrag_llm/completion/lite_llm_completion.py
index 794296604..dd62d459d 100644
--- a/packages/graphrag-llm/graphrag_llm/completion/lite_llm_completion.py
+++ b/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
 
diff --git a/packages/graphrag-llm/graphrag_llm/completion/mock_llm_completion.py b/packages/graphrag-llm/graphrag_llm/completion/mock_llm_completion.py
index c1e29fcfc..7cdcc319e 100644
--- a/packages/graphrag-llm/graphrag_llm/completion/mock_llm_completion.py
+++ b/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,
         /,
diff --git a/packages/graphrag-llm/notebooks/03_structured_responses.ipynb b/packages/graphrag-llm/notebooks/03_structured_responses.ipynb
index f01499d2d..80f6af037 100644
--- a/packages/graphrag-llm/notebooks/03_structured_responses.ipynb
+++ b/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",
diff --git a/packages/graphrag-llm/pyproject.toml b/packages/graphrag-llm/pyproject.toml
index 883b03f6b..62e4915c0 100644
--- a/packages/graphrag-llm/pyproject.toml
+++ b/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",
diff --git a/packages/graphrag-storage/graphrag_storage/tables/table_provider_config.py b/packages/graphrag-storage/graphrag_storage/tables/table_provider_config.py
new file mode 100644
index 000000000..1255646c2
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/tables/table_provider_config.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""Storage configuration model."""
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from graphrag_storage.tables.table_type import TableType
+
+
+class TableProviderConfig(BaseModel):
+    """The default configuration section for table providers."""
+
+    model_config = ConfigDict(extra="allow")
+    """Allow extra fields to support custom table provider implementations."""
+
+    type: str = Field(
+        description="The table type to use.",
+        default=TableType.Parquet,
+    )
diff --git a/packages/graphrag-storage/graphrag_storage/tables/table_provider_factory.py b/packages/graphrag-storage/graphrag_storage/tables/table_provider_factory.py
new file mode 100644
index 000000000..93add5d8d
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/tables/table_provider_factory.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+
+"""Storage factory implementation."""
+
+from collections.abc import Callable
+
+from graphrag_common.factory import Factory, ServiceScope
+
+from graphrag_storage.storage import Storage
+from graphrag_storage.tables.table_provider import TableProvider
+from graphrag_storage.tables.table_provider_config import TableProviderConfig
+from graphrag_storage.tables.table_type import TableType
+
+
+class TableProviderFactory(Factory[TableProvider]):
+    """A factory class for table storage implementations."""
+
+
+table_provider_factory = TableProviderFactory()
+
+
+def register_table_provider(
+    table_type: str,
+    table_initializer: Callable[..., TableProvider],
+    scope: ServiceScope = "transient",
+) -> None:
+    """Register a custom storage implementation.
+
+    Args
+    ----
+        - table_type: str
+            The table type id to register.
+        - table_initializer: Callable[..., TableProvider]
+            The table initializer to register.
+    """
+    table_provider_factory.register(table_type, table_initializer, scope)
+
+
+def create_table_provider(
+    config: TableProviderConfig, storage: Storage | None = None
+) -> TableProvider:
+    """Create a table provider implementation based on the given configuration.
+
+    Args
+    ----
+        - config: TableProviderConfig
+            The table provider configuration to use.
+        - storage: Storage | None
+            The storage implementation to use for file-based TableProviders such as Parquet and CSV.
+
+    Returns
+    -------
+        TableProvider
+            The created table provider implementation.
+    """
+    config_model = config.model_dump()
+    table_type = config.type
+
+    if table_type not in table_provider_factory:
+        match table_type:
+            case TableType.Parquet:
+                from graphrag_storage.tables.parquet_table_provider import (
+                    ParquetTableProvider,
+                )
+
+                register_table_provider(TableType.Parquet, ParquetTableProvider)
+            case _:
+                msg = f"TableProviderConfig.type '{table_type}' is not registered in the TableProviderFactory. Registered types: {', '.join(table_provider_factory.keys())}."
+                raise ValueError(msg)
+
+    if storage:
+        config_model["storage"] = storage
+
+    return table_provider_factory.create(table_type, config_model)
diff --git a/packages/graphrag-storage/graphrag_storage/tables/table_type.py b/packages/graphrag-storage/graphrag_storage/tables/table_type.py
new file mode 100644
index 000000000..ab8cdf701
--- /dev/null
+++ b/packages/graphrag-storage/graphrag_storage/tables/table_type.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+
+"""Builtin table storage implementation types."""
+
+from enum import StrEnum
+
+
+class TableType(StrEnum):
+    """Enum for table storage types."""
+
+    Parquet = "parquet"
diff --git a/packages/graphrag-storage/pyproject.toml b/packages/graphrag-storage/pyproject.toml
index e3b5388d3..2b64532f1 100644
--- a/packages/graphrag-storage/pyproject.toml
+++ b/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",
diff --git a/packages/graphrag-vectors/pyproject.toml b/packages/graphrag-vectors/pyproject.toml
index 1ff3255dd..05739af78 100644
--- a/packages/graphrag-vectors/pyproject.toml
+++ b/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",
diff --git a/packages/graphrag/graphrag/cli/query.py b/packages/graphrag/graphrag/cli/query.py
index 1f808420d..d3f710973 100644
--- a/packages/graphrag/graphrag/cli/query.py
+++ b/packages/graphrag/graphrag/cli/query.py
@@ -9,7 +9,7 @@
 from typing import TYPE_CHECKING, Any
 
 from graphrag_storage import create_storage
-from graphrag_storage.tables.parquet_table_provider import ParquetTableProvider
+from graphrag_storage.tables.table_provider_factory import create_table_provider
 
 import graphrag.api as api
 from graphrag.callbacks.noop_query_callbacks import NoopQueryCallbacks
@@ -378,7 +378,7 @@ def _resolve_output_files(
     """Read indexing output files to a dataframe dict."""
     dataframe_dict = {}
     storage_obj = create_storage(config.output_storage)
-    table_provider = ParquetTableProvider(storage_obj)
+    table_provider = create_table_provider(config.table_provider, storage=storage_obj)
     for name in output_list:
         df_value = asyncio.run(table_provider.read_dataframe(name))
         dataframe_dict[name] = df_value
diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py
index 84fb2de88..dc28da97c 100644
--- a/packages/graphrag/graphrag/config/models/graph_rag_config.py
+++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py
@@ -12,6 +12,7 @@
 from graphrag_input import InputConfig
 from graphrag_llm.config import ModelConfig
 from graphrag_storage import StorageConfig, StorageType
+from graphrag_storage.tables.table_provider_config import TableProviderConfig
 from graphrag_vectors import IndexSchema, VectorStoreConfig, VectorStoreType
 from pydantic import BaseModel, Field, model_validator
 
@@ -138,6 +139,11 @@ def _validate_update_output_storage_base_dir(self) -> None:
                 Path(self.update_output_storage.base_dir).resolve()
             )
 
+    table_provider: TableProviderConfig = Field(
+        description="The table provider configuration.", default=TableProviderConfig()
+    )
+    """The table provider configuration. By default we read/write parquet to disk. You can register custom output table storage."""
+
     cache: CacheConfig = Field(
         description="The cache configuration.",
         default=CacheConfig(**asdict(graphrag_config_defaults.cache)),
diff --git a/packages/graphrag/graphrag/index/run/run_pipeline.py b/packages/graphrag/graphrag/index/run/run_pipeline.py
index 24ff39cc0..a76b161d3 100644
--- a/packages/graphrag/graphrag/index/run/run_pipeline.py
+++ b/packages/graphrag/graphrag/index/run/run_pipeline.py
@@ -13,8 +13,8 @@
 import pandas as pd
 from graphrag_cache import create_cache
 from graphrag_storage import create_storage
-from graphrag_storage.tables.parquet_table_provider import ParquetTableProvider
 from graphrag_storage.tables.table_provider import TableProvider
+from graphrag_storage.tables.table_provider_factory import create_table_provider
 
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
 from graphrag.config.models.graph_rag_config import GraphRagConfig
@@ -36,9 +36,10 @@ async def run_pipeline(
 ) -> AsyncIterable[PipelineRunResult]:
     """Run all workflows using a simplified pipeline."""
     input_storage = create_storage(config.input_storage)
-    input_table_provider = ParquetTableProvider(input_storage)
 
     output_storage = create_storage(config.output_storage)
+    output_table_provider = create_table_provider(config.table_provider, output_storage)
+
     cache = create_cache(config.cache)
 
     # load existing state in case any workflows are stateful
@@ -56,13 +57,16 @@ async def run_pipeline(
         update_timestamp = time.strftime("%Y%m%d-%H%M%S")
         timestamped_storage = update_storage.child(update_timestamp)
         delta_storage = timestamped_storage.child("delta")
-        delta_table_provider = ParquetTableProvider(delta_storage)
+        delta_table_provider = create_table_provider(
+            config.table_provider, delta_storage
+        )
         # copy the previous output to a backup folder, so we can replace it with the update
         # we'll read from this later when we merge the old and new indexes
         previous_storage = timestamped_storage.child("previous")
-        previous_table_provider = ParquetTableProvider(previous_storage)
+        previous_table_provider = create_table_provider(
+            config.table_provider, previous_storage
+        )
 
-        output_table_provider = ParquetTableProvider(output_storage)
         await _copy_previous_output(output_table_provider, previous_table_provider)
 
         state["update_timestamp"] = update_timestamp
@@ -74,7 +78,6 @@ async def run_pipeline(
 
         context = create_run_context(
             input_storage=input_storage,
-            input_table_provider=input_table_provider,
             output_storage=delta_storage,
             output_table_provider=delta_table_provider,
             previous_table_provider=previous_table_provider,
@@ -88,15 +91,13 @@ async def run_pipeline(
 
         # if the user passes in a df directly, write directly to storage so we can skip finding/parsing later
         if input_documents is not None:
-            output_table_provider = ParquetTableProvider(output_storage)
             await output_table_provider.write_dataframe("documents", input_documents)
             pipeline.remove("load_input_documents")
 
         context = create_run_context(
             input_storage=input_storage,
-            input_table_provider=input_table_provider,
             output_storage=output_storage,
-            output_table_provider=ParquetTableProvider(storage=output_storage),
+            output_table_provider=output_table_provider,
             cache=cache,
             callbacks=callbacks,
             state=state,
diff --git a/packages/graphrag/graphrag/index/run/utils.py b/packages/graphrag/graphrag/index/run/utils.py
index 207e9561a..b85459bf7 100644
--- a/packages/graphrag/graphrag/index/run/utils.py
+++ b/packages/graphrag/graphrag/index/run/utils.py
@@ -8,6 +8,8 @@
 from graphrag_storage import Storage, create_storage
 from graphrag_storage.memory_storage import MemoryStorage
 from graphrag_storage.tables.parquet_table_provider import ParquetTableProvider
+from graphrag_storage.tables.table_provider import TableProvider
+from graphrag_storage.tables.table_provider_factory import create_table_provider
 
 from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
@@ -20,10 +22,9 @@
 
 def create_run_context(
     input_storage: Storage | None = None,
-    input_table_provider: ParquetTableProvider | None = None,
     output_storage: Storage | None = None,
-    output_table_provider: ParquetTableProvider | None = None,
-    previous_table_provider: ParquetTableProvider | None = None,
+    output_table_provider: TableProvider | None = None,
+    previous_table_provider: TableProvider | None = None,
     cache: Cache | None = None,
     callbacks: WorkflowCallbacks | None = None,
     stats: PipelineRunStats | None = None,
@@ -34,8 +35,6 @@ def create_run_context(
     output_storage = output_storage or MemoryStorage()
     return PipelineRunContext(
         input_storage=input_storage,
-        input_table_provider=input_table_provider
-        or ParquetTableProvider(storage=input_storage),
         output_storage=output_storage,
         output_table_provider=output_table_provider
         or ParquetTableProvider(storage=output_storage),
@@ -59,7 +58,7 @@ def create_callback_chain(
 
 def get_update_table_providers(
     config: GraphRagConfig, timestamp: str
-) -> tuple[ParquetTableProvider, ParquetTableProvider, ParquetTableProvider]:
+) -> tuple[TableProvider, TableProvider, TableProvider]:
     """Get table providers for the update index run."""
     output_storage = create_storage(config.output_storage)
     update_storage = create_storage(config.update_output_storage)
@@ -67,8 +66,10 @@ def get_update_table_providers(
     delta_storage = timestamped_storage.child("delta")
     previous_storage = timestamped_storage.child("previous")
 
-    output_table_provider = ParquetTableProvider(output_storage)
-    previous_table_provider = ParquetTableProvider(previous_storage)
-    delta_table_provider = ParquetTableProvider(delta_storage)
+    output_table_provider = create_table_provider(config.table_provider, output_storage)
+    previous_table_provider = create_table_provider(
+        config.table_provider, previous_storage
+    )
+    delta_table_provider = create_table_provider(config.table_provider, delta_storage)
 
     return output_table_provider, previous_table_provider, delta_table_provider
diff --git a/packages/graphrag/graphrag/index/typing/context.py b/packages/graphrag/graphrag/index/typing/context.py
index f606218dd..277e41f09 100644
--- a/packages/graphrag/graphrag/index/typing/context.py
+++ b/packages/graphrag/graphrag/index/typing/context.py
@@ -20,8 +20,6 @@ class PipelineRunContext:
     stats: PipelineRunStats
     input_storage: Storage
     "Storage for reading input documents."
-    input_table_provider: TableProvider
-    "Table provider for reading input tables."
     output_storage: Storage
     "Long-term storage for pipeline verbs to use. Items written here will be written to the storage provider."
     output_table_provider: TableProvider
diff --git a/packages/graphrag/pyproject.toml b/packages/graphrag/pyproject.toml
index cbb768fd7..a825b5b97 100644
--- a/packages/graphrag/pyproject.toml
+++ b/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",
diff --git a/tests/integration/language_model/test_factory.py b/tests/integration/language_model/test_factory.py
index 526cb3e8d..428586bf0 100644
--- a/tests/integration/language_model/test_factory.py
+++ b/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,
             /,
diff --git a/uv.lock b/uv.lock
index 1801e0eca..7bc020e7c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -266,7 +266,7 @@ wheels = [
 
 [[package]]
 name = "azure-identity"
-version = "1.19.0"
+version = "1.25.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "azure-core" },
@@ -275,9 +275,9 @@ dependencies = [
     { name = "msal-extensions" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/aa/91/cbaeff9eb0b838f0d35b4607ac1c6195c735c8eb17db235f8f60e622934c/azure_identity-1.19.0.tar.gz", hash = "sha256:500144dc18197d7019b81501165d4fa92225f03778f17d7ca8a2a180129a9c83", size = 263058, upload-time = "2024-10-08T15:41:33.554Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/06/8d/1a6c41c28a37eab26dc85ab6c86992c700cd3f4a597d9ed174b0e9c69489/azure_identity-1.25.1.tar.gz", hash = "sha256:87ca8328883de6036443e1c37b40e8dc8fb74898240f61071e09d2e369361456", size = 279826, upload-time = "2025-10-06T20:30:02.194Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/83/7b/5652771e24fff12da9dde4c20ecf4682e606b104f26419d139758cc935a6/azure_identity-1.25.1-py3-none-any.whl", hash = "sha256:e9edd720af03dff020223cd269fa3a61e8f345ea75443858273bcb44844ab651", size = 191317, upload-time = "2025-10-06T20:30:04.251Z" },
 ]
 
 [[package]]
@@ -1054,7 +1054,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "azure-identity", specifier = "~=1.19" },
+    { name = "azure-identity", specifier = "~=1.25" },
     { name = "azure-search-documents", specifier = "~=11.5" },
     { name = "azure-storage-blob", specifier = "~=12.24" },
     { name = "blis", specifier = "~=1.0" },
@@ -1162,7 +1162,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "azure-identity", specifier = "~=1.19.0" },
+    { name = "azure-identity", specifier = "~=1.25" },
     { name = "graphrag-cache", editable = "packages/graphrag-cache" },
     { name = "graphrag-common", editable = "packages/graphrag-common" },
     { name = "jinja2", specifier = "~=3.1" },
@@ -1245,7 +1245,7 @@ dependencies = [
 requires-dist = [
     { name = "aiofiles", specifier = "~=24.1" },
     { name = "azure-cosmos", specifier = "~=4.9" },
-    { name = "azure-identity", specifier = "~=1.19" },
+    { name = "azure-identity", specifier = "~=1.25" },
     { name = "azure-storage-blob", specifier = "~=12.24" },
     { name = "graphrag-common", editable = "packages/graphrag-common" },
     { name = "pandas", specifier = "~=2.3" },
@@ -1272,7 +1272,7 @@ dependencies = [
 requires-dist = [
     { name = "azure-core", specifier = "~=1.32" },
     { name = "azure-cosmos", specifier = "~=4.9" },
-    { name = "azure-identity", specifier = "~=1.19" },
+    { name = "azure-identity", specifier = "~=1.25" },
     { name = "azure-search-documents", specifier = "~=11.6" },
     { name = "graphrag-common", editable = "packages/graphrag-common" },
     { name = "lancedb", specifier = "~=0.24.1" },