fix(python/google): preserve thought_signature in Gemini function call parts

When using Gemini models with thinking enabled, the API returns a
thought_signature on function_call Part objects that must be included
in subsequent requests. Without this, follow-up turns with tool results
fail with HTTP 400: 'Function call is missing a thought_signature'.

Changes:
- Google AI: Capture thought_signature from response parts into
  FunctionCallContent.metadata during response parsing (both
  non-streaming and streaming paths)
- Google AI: Restore thought_signature when formatting assistant
  messages back to Gemini Part objects
- Vertex AI: Same capture and restore logic for Vertex AI connector
- Tests: Added 4 new tests covering thought_signature round-trip
  for both Google AI and Vertex AI

Fixes #13480

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Author: giulio-leone

python/semantic_kernel/connectors/ai/google/google_ai/services/google_ai_chat_completion.py

@@ -303,13 +303,17 @@ def _create_chat_message_content(
                 if part.text:
                     items.append(TextContent(text=part.text, inner_content=response, metadata=response_metadata))
                 elif part.function_call:
+                    fc_metadata: dict[str, Any] = {}
+                    if part.thought_signature:
+                        fc_metadata["thought_signature"] = part.thought_signature
                     items.append(
                         FunctionCallContent(
                             id=f"{part.function_call.name}_{idx!s}",
                             name=format_gemini_function_name_to_kernel_function_fully_qualified_name(
                                 part.function_call.name  # type: ignore[arg-type]
                             ),
                             arguments={k: v for k, v in part.function_call.args.items()},  # type: ignore
+                            metadata=fc_metadata if fc_metadata else None,
                         )
                     )
 
@@ -360,13 +364,17 @@ def _create_streaming_chat_message_content(
                         )
                     )
                 elif part.function_call:
+                    fc_metadata: dict[str, Any] = {}
+                    if part.thought_signature:
+                        fc_metadata["thought_signature"] = part.thought_signature
                     items.append(
                         FunctionCallContent(
                             id=f"{part.function_call.name}_{idx!s}",
                             name=format_gemini_function_name_to_kernel_function_fully_qualified_name(
                                 part.function_call.name  # type: ignore[arg-type]
                             ),
                             arguments={k: v for k, v in part.function_call.args.items()},  # type: ignore
+                            metadata=fc_metadata if fc_metadata else None,
                         )
                     )
 

python/semantic_kernel/connectors/ai/google/google_ai/services/utils.py

@@ -91,12 +91,26 @@ def format_assistant_message(message: ChatMessageContent) -> list[Part]:
             if item.text:
                 parts.append(Part.from_text(text=item.text))
         elif isinstance(item, FunctionCallContent):
-            parts.append(
-                Part.from_function_call(
-                    name=item.name,  # type: ignore[arg-type]
-                    args=json.loads(item.arguments) if isinstance(item.arguments, str) else item.arguments,  # type: ignore[arg-type]
+            thought_signature = item.metadata.get("thought_signature") if item.metadata else None
+            if thought_signature:
+                parts.append(
+                    Part(
+                        function_call={
+                            "name": item.name,
+                            "args": json.loads(item.arguments)
+                            if isinstance(item.arguments, str)
+                            else item.arguments,
+                        },
+                        thought_signature=thought_signature,
+                    )
+                )
+            else:
+                parts.append(
+                    Part.from_function_call(
+                        name=item.name,  # type: ignore[arg-type]
+                        args=json.loads(item.arguments) if isinstance(item.arguments, str) else item.arguments,  # type: ignore[arg-type]
+                    )
                 )
-            )
         elif isinstance(item, ImageContent):
             parts.append(_create_image_part(item))
         else:

python/semantic_kernel/connectors/ai/google/vertex_ai/services/utils.py

@@ -2,7 +2,7 @@
 
 import json
 import logging
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from google.cloud.aiplatform_v1beta1.types.content import Candidate
 from vertexai.generative_models import FunctionDeclaration, Part, Tool, ToolConfig
@@ -89,13 +89,17 @@ def format_assistant_message(message: ChatMessageContent) -> list[Part]:
             if item.text:
                 parts.append(Part.from_text(item.text))
         elif isinstance(item, FunctionCallContent):
+            part_dict: dict[str, Any] = {
+                "function_call": {
+                    "name": item.name,
+                    "args": json.loads(item.arguments) if isinstance(item.arguments, str) else item.arguments,
+                }
+            }
+            thought_signature = item.metadata.get("thought_signature") if item.metadata else None
+            if thought_signature:
+                part_dict["thought_signature"] = thought_signature
             parts.append(
-                Part.from_dict({
-                    "function_call": {
-                        "name": item.name,
-                        "args": json.loads(item.arguments) if isinstance(item.arguments, str) else item.arguments,
-                    }
-                })
+                Part.from_dict(part_dict)
             )
         elif isinstance(item, ImageContent):
             parts.append(_create_image_part(item))

python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_chat_completion.py

@@ -252,13 +252,18 @@ def _create_chat_message_content(self, response: GenerationResponse, candidate:
             if "text" in part_dict:
                 items.append(TextContent(text=part.text, inner_content=response, metadata=response_metadata))
             elif "function_call" in part_dict:
+                fc_metadata: dict[str, Any] = {}
+                thought_sig = part_dict.get("thought_signature")
+                if thought_sig:
+                    fc_metadata["thought_signature"] = thought_sig
                 items.append(
                     FunctionCallContent(
                         id=f"{part.function_call.name}_{idx!s}",
                         name=format_gemini_function_name_to_kernel_function_fully_qualified_name(
                             part.function_call.name
                         ),
                         arguments={k: v for k, v in part.function_call.args.items()},
+                        metadata=fc_metadata if fc_metadata else None,
                     )
                 )
 
@@ -309,13 +314,18 @@ def _create_streaming_chat_message_content(
                     )
                 )
             elif "function_call" in part_dict:
+                fc_metadata_s: dict[str, Any] = {}
+                thought_sig_s = part_dict.get("thought_signature")
+                if thought_sig_s:
+                    fc_metadata_s["thought_signature"] = thought_sig_s
                 items.append(
                     FunctionCallContent(
                         id=f"{part.function_call.name}_{idx!s}",
                         name=format_gemini_function_name_to_kernel_function_fully_qualified_name(
                             part.function_call.name
                         ),
                         arguments={k: v for k, v in part.function_call.args.items()},
+                        metadata=fc_metadata_s if fc_metadata_s else None,
                     )
                 )
 

python/tests/unit/connectors/ai/google/google_ai/services/test_google_ai_utils.py

@@ -113,3 +113,47 @@ def test_format_assistant_message_with_unsupported_items() -> None:
 
     with pytest.raises(ServiceInvalidRequestError):
         format_assistant_message(assistant_message)
+
+
+def test_format_assistant_message_with_thought_signature() -> None:
+    """Test that thought_signature is preserved in function call parts."""
+    import base64
+
+    thought_sig = base64.b64encode(b"test_thought_signature_data")
+    assistant_message = ChatMessageContent(
+        role=AuthorRole.ASSISTANT,
+        items=[
+            FunctionCallContent(
+                name="test_function",
+                arguments={"arg1": "value1"},
+                metadata={"thought_signature": thought_sig},
+            ),
+        ],
+    )
+
+    formatted = format_assistant_message(assistant_message)
+    assert len(formatted) == 1
+    assert isinstance(formatted[0], Part)
+    assert formatted[0].function_call.name == "test_function"
+    assert formatted[0].function_call.args == {"arg1": "value1"}
+    assert formatted[0].thought_signature == thought_sig
+
+
+def test_format_assistant_message_without_thought_signature() -> None:
+    """Test that function calls without thought_signature still work."""
+    assistant_message = ChatMessageContent(
+        role=AuthorRole.ASSISTANT,
+        items=[
+            FunctionCallContent(
+                name="test_function",
+                arguments={"arg1": "value1"},
+            ),
+        ],
+    )
+
+    formatted = format_assistant_message(assistant_message)
+    assert len(formatted) == 1
+    assert isinstance(formatted[0], Part)
+    assert formatted[0].function_call.name == "test_function"
+    assert formatted[0].function_call.args == {"arg1": "value1"}
+    assert not formatted[0].thought_signature

python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_utils.py

@@ -110,3 +110,50 @@ def test_format_assistant_message_with_unsupported_items() -> None:
 
     with pytest.raises(ServiceInvalidRequestError):
         format_assistant_message(assistant_message)
+
+
+def test_format_assistant_message_with_thought_signature() -> None:
+    """Test that thought_signature is preserved in function call parts for Vertex AI."""
+    import base64
+
+    thought_sig = base64.b64encode(b"test_thought_signature_data").decode("utf-8")
+    assistant_message = ChatMessageContent(
+        role=AuthorRole.ASSISTANT,
+        items=[
+            FunctionCallContent(
+                name="test_function",
+                arguments={"arg1": "value1"},
+                metadata={"thought_signature": thought_sig},
+            ),
+        ],
+    )
+
+    formatted = format_assistant_message(assistant_message)
+    assert len(formatted) == 1
+    assert isinstance(formatted[0], Part)
+    assert formatted[0].function_call.name == "test_function"
+    assert formatted[0].function_call.args == {"arg1": "value1"}
+    part_dict = formatted[0].to_dict()
+    assert "thought_signature" in part_dict
+    assert part_dict["thought_signature"] == thought_sig
+
+
+def test_format_assistant_message_without_thought_signature() -> None:
+    """Test that function calls without thought_signature still work for Vertex AI."""
+    assistant_message = ChatMessageContent(
+        role=AuthorRole.ASSISTANT,
+        items=[
+            FunctionCallContent(
+                name="test_function",
+                arguments={"arg1": "value1"},
+            ),
+        ],
+    )
+
+    formatted = format_assistant_message(assistant_message)
+    assert len(formatted) == 1
+    assert isinstance(formatted[0], Part)
+    assert formatted[0].function_call.name == "test_function"
+    assert formatted[0].function_call.args == {"arg1": "value1"}
+    part_dict = formatted[0].to_dict()
+    assert "thought_signature" not in part_dict