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Transform and to text parts for Google models that don't support tools #3591

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Transform and to text parts for Google models that don't support to…
sidg1215 Nov 28, 2025
aa07602
Added more testing.
sidg1215 Dec 4, 2025
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50 changes: 29 additions & 21 deletions pydantic_ai_slim/pydantic_ai/models/google.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -549,15 +549,20 @@ async def _map_messages(
elif isinstance(part, UserPromptPart):
message_parts.extend(await self._map_user_prompt(part))
elif isinstance(part, ToolReturnPart):
message_parts.append(
{
'function_response': {
'name': part.tool_name,
'response': part.model_response_object(),
'id': part.tool_call_id,
if self.profile.supports_tools:
message_parts.append(
{
'function_response': {
'name': part.tool_name,
'response': part.model_response_object(),
'id': part.tool_call_id,
}
}
}
)
)
else:
message_parts.append(
{'text': f'Tool {part.tool_name} responded with {part.model_response_object()}'}
)
elif isinstance(part, RetryPromptPart):
if part.tool_name is None:
message_parts.append({'text': part.model_response()})
Expand All @@ -577,7 +582,7 @@ async def _map_messages(
if message_parts:
contents.append({'role': 'user', 'parts': message_parts})
elif isinstance(m, ModelResponse):
maybe_content = _content_model_response(m, self.system)
maybe_content = _content_model_response(m, self.system, self.profile.supports_tools)
if maybe_content:
contents.append(maybe_content)
else:
Expand Down Expand Up @@ -786,7 +791,7 @@ def timestamp(self) -> datetime:
return self._timestamp


def _content_model_response(m: ModelResponse, provider_name: str) -> ContentDict | None: # noqa: C901
def _content_model_response(m: ModelResponse, provider_name: str, supports_tools: bool) -> ContentDict | None: # noqa: C901
parts: list[PartDict] = []
thinking_part_signature: str | None = None
function_call_requires_signature: bool = True
Expand All @@ -803,17 +808,20 @@ def _content_model_response(m: ModelResponse, provider_name: str) -> ContentDict
thinking_part_signature = None

if isinstance(item, ToolCallPart):
function_call = FunctionCallDict(name=item.tool_name, args=item.args_as_dict(), id=item.tool_call_id)
part['function_call'] = function_call
if function_call_requires_signature and not part.get('thought_signature'):
# Per https://ai.google.dev/gemini-api/docs/gemini-3?thinking=high#migrating_from_other_models:
# > If you are transferring a conversation trace from another model (e.g., Gemini 2.5) or injecting
# > a custom function call that was not generated by Gemini 3, you will not have a valid signature.
# > To bypass strict validation in these specific scenarios, populate the field with this specific
# > dummy string: "thoughtSignature": "context_engineering_is_the_way_to_go"
part['thought_signature'] = b'context_engineering_is_the_way_to_go'
# Only the first function call requires a signature
function_call_requires_signature = False
if supports_tools:
function_call = FunctionCallDict(name=item.tool_name, args=item.args_as_dict(), id=item.tool_call_id)
part['function_call'] = function_call
if function_call_requires_signature and not part.get('thought_signature'):
# Per https://ai.google.dev/gemini-api/docs/gemini-3?thinking=high#migrating_from_other_models:
# > If you are transferring a conversation trace from another model (e.g., Gemini 2.5) or injecting
# > a custom function call that was not generated by Gemini 3, you will not have a valid signature.
# > To bypass strict validation in these specific scenarios, populate the field with this specific
# > dummy string: "thoughtSignature": "context_engineering_is_the_way_to_go"
part['thought_signature'] = b'context_engineering_is_the_way_to_go'
# Only the first function call requires a signature
function_call_requires_signature = False
else:
part['text'] = f'Tool {item.tool_name} called with args {item.args_as_json_str()}'
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@DouweM DouweM Nov 28, 2025

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Because the arguments and return value can be very long, I want those on a separate line.

Also because there could be parallel tool calls with the same name, we should include the tool call ID.

I'd also want to format this in a more special way so the model understands this is not actually a user/assistant "message".

So I suggest doing something similar to how we send text files to OpenAI:

pydantic-ai/pydantic_ai_slim/pydantic_ai/models/openai.py

Lines 1006 to 1014 in c27e5e4

def _inline_text_file_part(text: str, *, media_type: str, identifier: str) -> ChatCompletionContentPartTextParam:
text = '\n'.join(
[
f'-----BEGIN FILE id="{identifier}" type="{media_type}"-----',
text,
f'-----END FILE id="{identifier}"-----',
]
)
return ChatCompletionContentPartTextParam(text=text, type='text')

And we're doing that for Google as well: #3269.

elif isinstance(item, TextPart):
part['text'] = item.content
elif isinstance(item, ThinkingPart):
Expand Down
26 changes: 26 additions & 0 deletions tests/models/test_google.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4307,6 +4307,7 @@ def test_google_thought_signature_on_thinking_part():
provider_name='google-gla',
),
'google-gla',
True,
)
new_google_response = _content_model_response(
ModelResponse(
Expand All @@ -4318,6 +4319,7 @@ def test_google_thought_signature_on_thinking_part():
provider_name='google-gla',
),
'google-gla',
True,
)
assert old_google_response == snapshot(
{
Expand All @@ -4342,6 +4344,7 @@ def test_google_thought_signature_on_thinking_part():
provider_name='google-gla',
),
'google-gla',
True,
)
new_google_response = _content_model_response(
ModelResponse(
Expand All @@ -4352,6 +4355,7 @@ def test_google_thought_signature_on_thinking_part():
provider_name='google-gla',
),
'google-gla',
True,
)
assert old_google_response == snapshot(
{
Expand All @@ -4376,6 +4380,7 @@ def test_google_thought_signature_on_thinking_part():
provider_name='google-gla',
),
'google-gla',
True,
)
new_google_response = _content_model_response(
ModelResponse(
Expand All @@ -4386,6 +4391,7 @@ def test_google_thought_signature_on_thinking_part():
provider_name='google-gla',
),
'google-gla',
True,
)
assert old_google_response == snapshot(
{
Expand All @@ -4412,6 +4418,7 @@ def test_google_missing_tool_call_thought_signature():
provider_name='openai',
),
'google-gla',
True,
)
assert google_response == snapshot(
{
Expand All @@ -4425,3 +4432,22 @@ def test_google_missing_tool_call_thought_signature():
],
}
)


def test_google_mapping_when_does_not_support_tools():
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@DouweM DouweM Nov 28, 2025

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We should also test the mapping of the tool result

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@sidg1215 sidg1215 Nov 30, 2025

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@DouweM Is there a way I can generate a casette file (I was initially attempting to right my own, but after thinking about it that feels...wrong)? I wanted to test for this behavior via Agent.run.

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@DouweM DouweM Dec 2, 2025

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@sidg1215 If you run the test with uv run pytest <path>::<test> --record-mide=recreate, vcrpy will automatically record them for you :)

google_response = _content_model_response(
ModelResponse(
parts=[
ToolCallPart(tool_name='tool', args={}, tool_call_id='tool_call_id'),
],
provider_name='openai',
),
'google-gla',
False,
)
assert google_response == snapshot(
{
'role': 'model',
'parts': [{'text': 'Tool tool called with args {}'}],
}
)
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