pydantic_ai.models.google

Interface that uses the google-genai package under the hood to access Google's Gemini models via both the Generative Language API and Vertex AI.

Setup

For details on how to set up authentication with this model, see model configuration for Google.

LatestGoogleModelNames module-attribute

LatestGoogleModelNames = Literal[
    "gemini-flash-latest",
    "gemini-flash-lite-latest",
    "gemini-2.0-flash",
    "gemini-2.0-flash-lite",
    "gemini-2.5-flash",
    "gemini-2.5-flash-preview-09-2025",
    "gemini-2.5-flash-image",
    "gemini-2.5-flash-lite",
    "gemini-2.5-flash-lite-preview-09-2025",
    "gemini-2.5-pro",
    "gemini-3-flash-preview",
    "gemini-3-pro-preview",
    "gemini-3-pro-image-preview",
    "gemini-3.1-pro-preview",
]

Latest Gemini models.

GoogleModelName module-attribute

GoogleModelName = str | LatestGoogleModelNames

Possible Gemini model names.

Since Gemini supports a variety of date-stamped models, we explicitly list the latest models but allow any name in the type hints. See the Gemini API docs for a full list.

GoogleModelSettings

Bases: ModelSettings

Settings used for a Gemini model request.

Source code in pydantic_ai_slim/pydantic_ai/models/google.py

class GoogleModelSettings(ModelSettings, total=False):
    """Settings used for a Gemini model request."""

    # ALL FIELDS MUST BE `gemini_` PREFIXED SO YOU CAN MERGE THEM WITH OTHER MODELS.

    google_safety_settings: list[SafetySettingDict]
    """The safety settings to use for the model.

    See <https://ai.google.dev/gemini-api/docs/safety-settings> for more information.
    """

    google_thinking_config: ThinkingConfigDict
    """The thinking configuration to use for the model.

    See <https://ai.google.dev/gemini-api/docs/thinking> for more information.
    """

    google_labels: dict[str, str]
    """User-defined metadata to break down billed charges. Only supported by the Vertex AI API.

    See the [Gemini API docs](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/add-labels-to-api-calls) for use cases and limitations.
    """

    google_video_resolution: MediaResolution
    """The video resolution to use for the model.

    See <https://ai.google.dev/api/generate-content#MediaResolution> for more information.
    """

    google_cached_content: str
    """The name of the cached content to use for the model.

    See <https://ai.google.dev/gemini-api/docs/caching> for more information.
    """

    google_logprobs: bool
    """Include log probabilities in the response.

    See <https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/content-generation-parameters#log-probabilities-output-tokens> for more information.

    Note: Only supported for Vertex AI and non-streaming requests.

    These will be included in `ModelResponse.provider_details['logprobs']`.
    """

    google_top_logprobs: int
    """Include log probabilities of the top n tokens in the response.

    See <https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/content-generation-parameters#log-probabilities-output-tokens> for more information.

    Note: Only supported for Vertex AI and non-streaming requests.

    These will be included in `ModelResponse.provider_details['logprobs']`.
    """

google_safety_settings instance-attribute

google_safety_settings: list[SafetySettingDict]

The safety settings to use for the model.

See https://ai.google.dev/gemini-api/docs/safety-settings for more information.

google_thinking_config instance-attribute

google_thinking_config: ThinkingConfigDict

The thinking configuration to use for the model.

See https://ai.google.dev/gemini-api/docs/thinking for more information.

google_labels instance-attribute

google_labels: dict[str, str]

User-defined metadata to break down billed charges. Only supported by the Vertex AI API.

See the Gemini API docs for use cases and limitations.

google_video_resolution instance-attribute

google_video_resolution: MediaResolution

The video resolution to use for the model.

See https://ai.google.dev/api/generate-content#MediaResolution for more information.

google_cached_content instance-attribute

google_cached_content: str

The name of the cached content to use for the model.

See https://ai.google.dev/gemini-api/docs/caching for more information.

google_logprobs instance-attribute

google_logprobs: bool

Include log probabilities in the response.

See https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/content-generation-parameters#log-probabilities-output-tokens for more information.

Note: Only supported for Vertex AI and non-streaming requests.

These will be included in ModelResponse.provider_details['logprobs'].

google_top_logprobs instance-attribute

google_top_logprobs: int

Include log probabilities of the top n tokens in the response.

See https://docs.cloud.google.com/vertex-ai/generative-ai/docs/multimodal/content-generation-parameters#log-probabilities-output-tokens for more information.

Note: Only supported for Vertex AI and non-streaming requests.

These will be included in ModelResponse.provider_details['logprobs'].

GoogleModel dataclass

Bases: Model

A model that uses Gemini via generativelanguage.googleapis.com API.

This is implemented from scratch rather than using a dedicated SDK, good API documentation is available here.

Apart from __init__, all methods are private or match those of the base class.

Source code in pydantic_ai_slim/pydantic_ai/models/google.py

@dataclass(init=False)
class GoogleModel(Model):
    """A model that uses Gemini via `generativelanguage.googleapis.com` API.

    This is implemented from scratch rather than using a dedicated SDK, good API documentation is
    available [here](https://ai.google.dev/api).

    Apart from `__init__`, all methods are private or match those of the base class.
    """

    client: Client = field(repr=False)

    _model_name: GoogleModelName = field(repr=False)
    _provider: Provider[Client] = field(repr=False)

    def __init__(
        self,
        model_name: GoogleModelName,
        *,
        provider: Literal['google-gla', 'google-vertex', 'gateway'] | Provider[Client] = 'google-gla',
        profile: ModelProfileSpec | None = None,
        settings: ModelSettings | None = None,
    ):
        """Initialize a Gemini model.

        Args:
            model_name: The name of the model to use.
            provider: The provider to use for authentication and API access. Can be either the string
                'google-gla' or 'google-vertex' or an instance of `Provider[google.genai.AsyncClient]`.
                Defaults to 'google-gla'.
            profile: The model profile to use. Defaults to a profile picked by the provider based on the model name.
            settings: The model settings to use. Defaults to None.
        """
        self._model_name = model_name

        if isinstance(provider, str):
            provider = infer_provider('gateway/google-vertex' if provider == 'gateway' else provider)
        self._provider = provider
        self.client = provider.client

        super().__init__(settings=settings, profile=profile or provider.model_profile)

    @property
    def base_url(self) -> str:
        return self._provider.base_url

    @property
    def model_name(self) -> GoogleModelName:
        """The model name."""
        return self._model_name

    @property
    def system(self) -> str:
        """The model provider."""
        return self._provider.name

    @classmethod
    def supported_builtin_tools(cls) -> frozenset[type[AbstractBuiltinTool]]:
        """Return the set of builtin tool types this model can handle."""
        return frozenset({WebSearchTool, CodeExecutionTool, FileSearchTool, WebFetchTool, ImageGenerationTool})

    def prepare_request(
        self, model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters
    ) -> tuple[ModelSettings | None, ModelRequestParameters]:
        supports_native_output_with_builtin_tools = GoogleModelProfile.from_profile(
            self.profile
        ).google_supports_native_output_with_builtin_tools
        if model_request_parameters.builtin_tools and model_request_parameters.output_tools:
            if model_request_parameters.output_mode == 'auto':
                output_mode = 'native' if supports_native_output_with_builtin_tools else 'prompted'
                model_request_parameters = replace(model_request_parameters, output_mode=output_mode)
            else:
                output_mode = 'NativeOutput' if supports_native_output_with_builtin_tools else 'PromptedOutput'
                raise UserError(
                    f'Google does not support output tools and built-in tools at the same time. Use `output_type={output_mode}(...)` instead.'
                )
        return super().prepare_request(model_settings, model_request_parameters)

    async def request(
        self,
        messages: list[ModelMessage],
        model_settings: ModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> ModelResponse:
        check_allow_model_requests()
        model_settings, model_request_parameters = self.prepare_request(
            model_settings,
            model_request_parameters,
        )
        model_settings = cast(GoogleModelSettings, model_settings or {})
        response = await self._generate_content(messages, False, model_settings, model_request_parameters)
        return self._process_response(response)

    async def count_tokens(
        self,
        messages: list[ModelMessage],
        model_settings: ModelSettings | None,
        model_request_parameters: ModelRequestParameters,
    ) -> usage.RequestUsage:
        check_allow_model_requests()
        model_settings, model_request_parameters = self.prepare_request(
            model_settings,
            model_request_parameters,
        )
        model_settings = cast(GoogleModelSettings, model_settings or {})
        contents, generation_config = await self._build_content_and_config(
            messages, model_settings, model_request_parameters
        )

        # Annoyingly, the type of `GenerateContentConfigDict.get` is "partially `Unknown`" because `response_schema` includes `typing._UnionGenericAlias`,
        # so without this we'd need `pyright: ignore[reportUnknownMemberType]` on every line and wouldn't get type checking anyway.
        generation_config = cast(dict[str, Any], generation_config)

        config = CountTokensConfigDict(
            http_options=generation_config.get('http_options'),
        )
        if self._provider.name != 'google-gla':
            # The fields are not supported by the Gemini API per https://github.com/googleapis/python-genai/blob/7e4ec284dc6e521949626f3ed54028163ef9121d/google/genai/models.py#L1195-L1214
            config.update(  # pragma: lax no cover
                system_instruction=generation_config.get('system_instruction'),
                tools=cast(list[ToolDict], generation_config.get('tools')),
                # Annoyingly, GenerationConfigDict has fewer fields than GenerateContentConfigDict, and no extra fields are allowed.
                generation_config=GenerationConfigDict(
                    temperature=generation_config.get('temperature'),
                    top_p=generation_config.get('top_p'),
                    max_output_tokens=generation_config.get('max_output_tokens'),
                    stop_sequences=generation_config.get('stop_sequences'),
                    presence_penalty=generation_config.get('presence_penalty'),
                    frequency_penalty=generation_config.get('frequency_penalty'),
                    seed=generation_config.get('seed'),
                    thinking_config=generation_config.get('thinking_config'),
                    media_resolution=generation_config.get('media_resolution'),
                    response_mime_type=generation_config.get('response_mime_type'),
                    response_json_schema=generation_config.get('response_json_schema'),
                ),
            )

        response = await self.client.aio.models.count_tokens(
            model=self._model_name,
            contents=contents,
            config=config,
        )
        if response.total_tokens is None:
            raise UnexpectedModelBehavior(  # pragma: no cover
                'Total tokens missing from Gemini response', str(response)
            )
        return usage.RequestUsage(
            input_tokens=response.total_tokens,
        )

    @asynccontextmanager
    async def request_stream(
        self,
        messages: list[ModelMessage],
        model_settings: ModelSettings | None,
        model_request_parameters: ModelRequestParameters,
        run_context: RunContext[Any] | None = None,
    ) -> AsyncIterator[StreamedResponse]:
        check_allow_model_requests()
        model_settings, model_request_parameters = self.prepare_request(
            model_settings,
            model_request_parameters,
        )
        model_settings = cast(GoogleModelSettings, model_settings or {})
        response = await self._generate_content(messages, True, model_settings, model_request_parameters)
        yield await self._process_streamed_response(response, model_request_parameters)  # type: ignore

    def _build_image_config(self, tool: ImageGenerationTool) -> ImageConfigDict:
        """Build ImageConfigDict from ImageGenerationTool with validation."""
        image_config = ImageConfigDict()

        if tool.aspect_ratio is not None:
            image_config['aspect_ratio'] = tool.aspect_ratio

        if tool.size is not None:
            if tool.size not in _GOOGLE_IMAGE_SIZES:
                raise UserError(
                    f'Google image generation only supports `size` values: {_GOOGLE_IMAGE_SIZES}. '
                    f'Got: {tool.size!r}. Omit `size` to use the default (1K).'
                )
            image_config['image_size'] = tool.size

        if self.system == 'google-vertex':
            if tool.output_format is not None:
                if tool.output_format not in _GOOGLE_IMAGE_OUTPUT_FORMATS:
                    raise UserError(
                        f'Google image generation only supports `output_format` values: {_GOOGLE_IMAGE_OUTPUT_FORMATS}. '
                        f'Got: {tool.output_format!r}.'
                    )
                image_config['output_mime_type'] = f'image/{tool.output_format}'

            output_compression = tool.output_compression
            if output_compression is not None:
                if not (0 <= output_compression <= 100):
                    raise UserError(
                        f'Google image generation `output_compression` must be between 0 and 100. '
                        f'Got: {output_compression}.'
                    )
                if tool.output_format not in (None, 'jpeg'):
                    raise UserError(
                        f'Google image generation `output_compression` is only supported for JPEG format. '
                        f'Got format: {tool.output_format!r}. Either set `output_format="jpeg"` or remove `output_compression`.'
                    )
                image_config['output_compression_quality'] = output_compression
                if tool.output_format is None:
                    image_config['output_mime_type'] = 'image/jpeg'

        return image_config

    def _get_tools(
        self, model_request_parameters: ModelRequestParameters
    ) -> tuple[list[ToolDict] | None, ImageConfigDict | None]:
        tools: list[ToolDict] = [
            ToolDict(function_declarations=[_function_declaration_from_tool(t)])
            for t in model_request_parameters.tool_defs.values()
        ]

        image_config: ImageConfigDict | None = None

        if model_request_parameters.builtin_tools:
            if model_request_parameters.function_tools:
                raise UserError('Google does not support function tools and built-in tools at the same time.')

            for tool in model_request_parameters.builtin_tools:
                if isinstance(tool, WebSearchTool):
                    tools.append(ToolDict(google_search=GoogleSearchDict()))
                elif isinstance(tool, WebFetchTool):
                    tools.append(ToolDict(url_context=UrlContextDict()))
                elif isinstance(tool, CodeExecutionTool):
                    tools.append(ToolDict(code_execution=ToolCodeExecutionDict()))
                elif isinstance(tool, FileSearchTool):
                    file_search_config = FileSearchDict(file_search_store_names=list(tool.file_store_ids))
                    tools.append(ToolDict(file_search=file_search_config))
                elif isinstance(tool, ImageGenerationTool):  # pragma: no branch
                    if not self.profile.supports_image_output:
                        raise UserError(
                            "`ImageGenerationTool` is not supported by this model. Use a model with 'image' in the name instead."
                        )
                    image_config = self._build_image_config(tool)
                else:  # pragma: no cover
                    raise UserError(
                        f'`{tool.__class__.__name__}` is not supported by `GoogleModel`. If it should be, please file an issue.'
                    )
        return tools or None, image_config

    def _get_tool_config(
        self, model_request_parameters: ModelRequestParameters, tools: list[ToolDict] | None
    ) -> ToolConfigDict | None:
        if not model_request_parameters.allow_text_output and tools:
            names: list[str] = []
            for tool in tools:
                for function_declaration in tool.get('function_declarations') or []:
                    if name := function_declaration.get('name'):  # pragma: no branch
                        names.append(name)
            return _tool_config(names)
        else:
            return None

    @overload
    async def _generate_content(
        self,
        messages: list[ModelMessage],
        stream: Literal[False],
        model_settings: GoogleModelSettings,
        model_request_parameters: ModelRequestParameters,
    ) -> GenerateContentResponse: ...

    @overload
    async def _generate_content(
        self,
        messages: list[ModelMessage],
        stream: Literal[True],
        model_settings: GoogleModelSettings,
        model_request_parameters: ModelRequestParameters,
    ) -> Awaitable[AsyncIterator[GenerateContentResponse]]: ...

    async def _generate_content(
        self,
        messages: list[ModelMessage],
        stream: bool,
        model_settings: GoogleModelSettings,
        model_request_parameters: ModelRequestParameters,
    ) -> GenerateContentResponse | Awaitable[AsyncIterator[GenerateContentResponse]]:
        contents, config = await self._build_content_and_config(
            messages,
            model_settings,
            model_request_parameters,
        )
        func = self.client.aio.models.generate_content_stream if stream else self.client.aio.models.generate_content
        try:
            return await func(model=self._model_name, contents=contents, config=config)  # type: ignore
        except errors.APIError as e:
            if (status_code := e.code) >= 400:
                raise ModelHTTPError(
                    status_code=status_code,
                    model_name=self._model_name,
                    body=cast(Any, e.details),  # pyright: ignore[reportUnknownMemberType]
                ) from e
            raise ModelAPIError(model_name=self._model_name, message=str(e)) from e

    async def _build_content_and_config(
        self,
        messages: list[ModelMessage],
        model_settings: GoogleModelSettings,
        model_request_parameters: ModelRequestParameters,
    ) -> tuple[list[ContentUnionDict], GenerateContentConfigDict]:
        tools, image_config = self._get_tools(model_request_parameters)
        if model_request_parameters.function_tools and not self.profile.supports_tools:
            raise UserError('Tools are not supported by this model.')

        response_mime_type = None
        response_schema = None
        if model_request_parameters.output_mode == 'native':
            if model_request_parameters.function_tools:
                raise UserError(
                    'Google does not support `NativeOutput` and function tools at the same time. Use `output_type=ToolOutput(...)` instead.'
                )
            response_mime_type = 'application/json'
            output_object = model_request_parameters.output_object
            assert output_object is not None
            response_schema = self._map_response_schema(output_object)
        elif model_request_parameters.output_mode == 'prompted' and not tools:
            if not self.profile.supports_json_object_output:
                raise UserError('JSON output is not supported by this model.')
            response_mime_type = 'application/json'

        tool_config = self._get_tool_config(model_request_parameters, tools)
        system_instruction, contents = await self._map_messages(messages, model_request_parameters)

        modalities = [Modality.TEXT.value]
        if self.profile.supports_image_output:
            modalities.append(Modality.IMAGE.value)

        headers: dict[str, str] = {'Content-Type': 'application/json', 'User-Agent': get_user_agent()}
        if extra_headers := model_settings.get('extra_headers'):
            headers.update(extra_headers)
        http_options: HttpOptionsDict = {'headers': headers}
        if timeout := model_settings.get('timeout'):
            if isinstance(timeout, int | float):
                http_options['timeout'] = int(1000 * timeout)
            else:
                raise UserError('Google does not support setting ModelSettings.timeout to a httpx.Timeout')

        config = GenerateContentConfigDict(
            http_options=http_options,
            system_instruction=system_instruction,
            temperature=model_settings.get('temperature'),
            top_p=model_settings.get('top_p'),
            max_output_tokens=model_settings.get('max_tokens'),
            stop_sequences=model_settings.get('stop_sequences'),
            presence_penalty=model_settings.get('presence_penalty'),
            frequency_penalty=model_settings.get('frequency_penalty'),
            seed=model_settings.get('seed'),
            safety_settings=model_settings.get('google_safety_settings'),
            thinking_config=model_settings.get('google_thinking_config'),
            labels=model_settings.get('google_labels'),
            media_resolution=model_settings.get('google_video_resolution'),
            cached_content=model_settings.get('google_cached_content'),
            tools=cast(ToolListUnionDict, tools),
            tool_config=tool_config,
            response_mime_type=response_mime_type,
            response_json_schema=response_schema,
            response_modalities=modalities,
            image_config=image_config,
        )

        # Validate logprobs settings
        logprobs_requested = model_settings.get('google_logprobs')
        if logprobs_requested:
            config['response_logprobs'] = True

            if 'google_top_logprobs' in model_settings:
                config['logprobs'] = model_settings.get('google_top_logprobs')

        return contents, config

    def _process_response(self, response: GenerateContentResponse) -> ModelResponse:
        candidate = response.candidates[0] if response.candidates else None

        vendor_id = response.response_id
        finish_reason: FinishReason | None = None
        vendor_details: dict[str, Any] = {}

        raw_finish_reason = candidate.finish_reason if candidate else None
        if raw_finish_reason and candidate:  # pragma: no branch
            vendor_details = {'finish_reason': raw_finish_reason.value}
            # Add safety ratings to provider details
            if candidate.safety_ratings:
                vendor_details['safety_ratings'] = [r.model_dump(by_alias=True) for r in candidate.safety_ratings]
            finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason)
        elif candidate is None and response.prompt_feedback and response.prompt_feedback.block_reason:
            block_reason = response.prompt_feedback.block_reason
            vendor_details['block_reason'] = block_reason.value
            if response.prompt_feedback.block_reason_message:
                vendor_details['block_reason_message'] = response.prompt_feedback.block_reason_message
            if response.prompt_feedback.safety_ratings:
                vendor_details['safety_ratings'] = [
                    r.model_dump(by_alias=True) for r in response.prompt_feedback.safety_ratings
                ]
            finish_reason = 'content_filter'

        if response.create_time is not None:  # pragma: no branch
            vendor_details['timestamp'] = response.create_time

        if candidate is None or candidate.content is None or candidate.content.parts is None:
            parts = []
        else:
            parts = candidate.content.parts or []

        if candidate and (logprob_results := candidate.logprobs_result):
            vendor_details['logprobs'] = logprob_results.model_dump(mode='json')
            vendor_details['avg_logprobs'] = candidate.avg_logprobs

        usage = _metadata_as_usage(response, provider=self._provider.name, provider_url=self._provider.base_url)
        grounding_metadata = candidate.grounding_metadata if candidate else None
        url_context_metadata = candidate.url_context_metadata if candidate else None

        return _process_response_from_parts(
            parts,
            grounding_metadata,
            response.model_version or self._model_name,
            self._provider.name,
            self._provider.base_url,
            usage,
            vendor_id=vendor_id,
            vendor_details=vendor_details or None,
            finish_reason=finish_reason,
            url_context_metadata=url_context_metadata,
        )

    async def _process_streamed_response(
        self, response: AsyncIterator[GenerateContentResponse], model_request_parameters: ModelRequestParameters
    ) -> StreamedResponse:
        """Process a streamed response, and prepare a streaming response to return."""
        peekable_response = _utils.PeekableAsyncStream(response)
        first_chunk = await peekable_response.peek()
        if isinstance(first_chunk, _utils.Unset):
            raise UnexpectedModelBehavior('Streamed response ended without content or tool calls')  # pragma: no cover

        return GeminiStreamedResponse(
            model_request_parameters=model_request_parameters,
            _model_name=first_chunk.model_version or self._model_name,
            _response=peekable_response,
            _provider_name=self._provider.name,
            _provider_url=self._provider.base_url,
            _provider_timestamp=first_chunk.create_time,
        )

    async def _map_messages(  # noqa: C901
        self, messages: list[ModelMessage], model_request_parameters: ModelRequestParameters
    ) -> tuple[ContentDict | None, list[ContentUnionDict]]:
        contents: list[ContentUnionDict] = []
        system_parts: list[PartDict] = []

        for m in messages:
            if isinstance(m, ModelRequest):
                message_parts: list[PartDict] = []

                for part in m.parts:
                    if isinstance(part, SystemPromptPart):
                        system_parts.append({'text': part.content})
                    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,
                                }
                            }
                        )
                    elif isinstance(part, RetryPromptPart):
                        if part.tool_name is None:
                            message_parts.append({'text': part.model_response()})
                        else:
                            message_parts.append(
                                {
                                    'function_response': {
                                        'name': part.tool_name,
                                        'response': {'error': part.model_response()},
                                        'id': part.tool_call_id,
                                    }
                                }
                            )
                    else:
                        assert_never(part)

                # Work around a Gemini bug where content objects containing functionResponse parts are treated as
                # role=model even when role=user is explicitly specified.
                #
                # We build `message_parts` first, then split into multiple content objects whenever we transition
                # between function_response and non-function_response parts.
                #
                # TODO: Remove workaround when https://github.com/pydantic/pydantic-ai/issues/4210 is resolved
                if message_parts:
                    content_parts: list[PartDict] = []

                    for part in message_parts:
                        if (
                            content_parts
                            and 'function_response' in content_parts[-1]
                            and 'function_response' not in part
                        ):
                            contents.append({'role': 'user', 'parts': content_parts})
                            content_parts = []

                        content_parts.append(part)

                    contents.append({'role': 'user', 'parts': content_parts})
            elif isinstance(m, ModelResponse):
                maybe_content = _content_model_response(m, self.system)
                if maybe_content:
                    contents.append(maybe_content)
            else:
                assert_never(m)

        # Google GenAI requires at least one user part in the message, and that function call turns
        # come immediately after a user turn or after a function response turn.
        if not contents or contents[0].get('role') == 'model':  # pyright: ignore[reportAttributeAccessIssue, reportUnknownMemberType]
            contents.insert(0, {'role': 'user', 'parts': [{'text': ''}]})

        if instructions := self._get_instructions(messages, model_request_parameters):
            system_parts.append({'text': instructions})
        system_instruction = ContentDict(role='user', parts=system_parts) if system_parts else None

        return system_instruction, contents

    async def _map_user_prompt(self, part: UserPromptPart) -> list[PartDict]:
        if isinstance(part.content, str):
            return [{'text': part.content}]
        else:
            content: list[PartDict] = []
            for item in part.content:
                if isinstance(item, str):
                    content.append({'text': item})
                elif isinstance(item, BinaryContent):
                    inline_data_dict: BlobDict = {'data': item.data, 'mime_type': item.media_type}
                    part_dict: PartDict = {'inline_data': inline_data_dict}
                    if item.vendor_metadata:
                        part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
                    content.append(part_dict)
                elif isinstance(item, VideoUrl) and (
                    item.is_youtube or (item.url.startswith('gs://') and self.system == 'google-vertex')
                ):
                    # YouTube URLs work on both google-gla and google-vertex
                    # GCS URIs (gs://...) only work on google-vertex (Vertex AI can access GCS buckets)
                    # GCS on google-gla falls through to FileUrl which raises clear error on download attempt
                    # Other URLs fall through to FileUrl handling (download for google-gla)
                    # Note: force_download is not checked here, mirroring the original YouTube behavior.
                    # GCS URIs cannot be downloaded anyway ("gs://" protocol not supported for download).
                    file_data_dict: FileDataDict = {'file_uri': item.url, 'mime_type': item.media_type}
                    part_dict: PartDict = {'file_data': file_data_dict}
                    if item.vendor_metadata:
                        part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
                    content.append(part_dict)
                elif isinstance(item, FileUrl):
                    if item.force_download or (
                        # google-gla does not support passing file urls directly, except for youtube videos
                        # (see above) and files uploaded to the file API (which cannot be downloaded anyway)
                        self.system == 'google-gla'
                        and not item.url.startswith(r'https://generativelanguage.googleapis.com/v1beta/files')
                    ):
                        downloaded_item = await download_item(item, data_format='bytes')
                        inline_data: BlobDict = {
                            'data': downloaded_item['data'],
                            'mime_type': downloaded_item['data_type'],
                        }
                        part_dict: PartDict = {'inline_data': inline_data}
                        # VideoUrl is a subclass of FileUrl - include video_metadata if present
                        if isinstance(item, VideoUrl) and item.vendor_metadata:
                            part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
                        content.append(part_dict)
                    else:
                        file_data_dict: FileDataDict = {'file_uri': item.url, 'mime_type': item.media_type}
                        part_dict: PartDict = {'file_data': file_data_dict}
                        # VideoUrl is a subclass of FileUrl - include video_metadata if present
                        if isinstance(item, VideoUrl) and item.vendor_metadata:
                            part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
                        content.append(part_dict)  # pragma: lax no cover
                elif isinstance(item, CachePoint):
                    # Google doesn't support inline CachePoint markers. Google's caching requires
                    # pre-creating cache objects via the API, then referencing them by name using
                    # `GoogleModelSettings.google_cached_content`. See https://ai.google.dev/gemini-api/docs/caching
                    pass
                else:
                    assert_never(item)
        return content

    def _map_response_schema(self, o: OutputObjectDefinition) -> dict[str, Any]:
        response_schema = o.json_schema.copy()
        if o.name:
            response_schema['title'] = o.name
        if o.description:
            response_schema['description'] = o.description

        return response_schema

__init__

__init__(
    model_name: GoogleModelName,
    *,
    provider: (
        Literal["google-gla", "google-vertex", "gateway"]
        | Provider[Client]
    ) = "google-gla",
    profile: ModelProfileSpec | None = None,
    settings: ModelSettings | None = None
)

Initialize a Gemini model.

Parameters:

Name	Type	Description	Default
model_name	GoogleModelName	The name of the model to use.	required
provider	Literal['google-gla', 'google-vertex', 'gateway'] | Provider[Client]	The provider to use for authentication and API access. Can be either the string 'google-gla' or 'google-vertex' or an instance of Provider[google.genai.AsyncClient]. Defaults to 'google-gla'.	'google-gla'
profile	ModelProfileSpec | None	The model profile to use. Defaults to a profile picked by the provider based on the model name.	None
settings	ModelSettings | None	The model settings to use. Defaults to None.	None

Source code in pydantic_ai_slim/pydantic_ai/models/google.py

def __init__(
    self,
    model_name: GoogleModelName,
    *,
    provider: Literal['google-gla', 'google-vertex', 'gateway'] | Provider[Client] = 'google-gla',
    profile: ModelProfileSpec | None = None,
    settings: ModelSettings | None = None,
):
    """Initialize a Gemini model.

    Args:
        model_name: The name of the model to use.
        provider: The provider to use for authentication and API access. Can be either the string
            'google-gla' or 'google-vertex' or an instance of `Provider[google.genai.AsyncClient]`.
            Defaults to 'google-gla'.
        profile: The model profile to use. Defaults to a profile picked by the provider based on the model name.
        settings: The model settings to use. Defaults to None.
    """
    self._model_name = model_name

    if isinstance(provider, str):
        provider = infer_provider('gateway/google-vertex' if provider == 'gateway' else provider)
    self._provider = provider
    self.client = provider.client

    super().__init__(settings=settings, profile=profile or provider.model_profile)

model_name property

model_name: GoogleModelName

The model name.

system property

system: str

The model provider.

supported_builtin_tools classmethod

supported_builtin_tools() -> (
    frozenset[type[AbstractBuiltinTool]]
)

Return the set of builtin tool types this model can handle.

Source code in pydantic_ai_slim/pydantic_ai/models/google.py

@classmethod
def supported_builtin_tools(cls) -> frozenset[type[AbstractBuiltinTool]]:
    """Return the set of builtin tool types this model can handle."""
    return frozenset({WebSearchTool, CodeExecutionTool, FileSearchTool, WebFetchTool, ImageGenerationTool})

GeminiStreamedResponse dataclass

Bases: StreamedResponse

Implementation of StreamedResponse for the Gemini model.

Source code in pydantic_ai_slim/pydantic_ai/models/google.py

@dataclass
class GeminiStreamedResponse(StreamedResponse):
    """Implementation of `StreamedResponse` for the Gemini model."""

    _model_name: GoogleModelName
    _response: AsyncIterator[GenerateContentResponse]
    _provider_name: str
    _provider_url: str
    _provider_timestamp: datetime | None = None
    _timestamp: datetime = field(default_factory=_utils.now_utc)
    _file_search_tool_call_id: str | None = field(default=None, init=False)
    _code_execution_tool_call_id: str | None = field(default=None, init=False)
    _has_content_filter: bool = field(default=False, init=False)

    async def _get_event_iterator(self) -> AsyncIterator[ModelResponseStreamEvent]:  # noqa: C901
        if self._provider_timestamp is not None:
            self.provider_details = {'timestamp': self._provider_timestamp}
        async for chunk in self._response:
            self._usage = _metadata_as_usage(chunk, self._provider_name, self._provider_url)

            if not chunk.candidates:
                if chunk.prompt_feedback and chunk.prompt_feedback.block_reason:
                    self._has_content_filter = True
                    block_reason = chunk.prompt_feedback.block_reason
                    self.provider_details = {
                        **(self.provider_details or {}),
                        'block_reason': block_reason.value,
                    }
                    if chunk.prompt_feedback.block_reason_message:
                        self.provider_details['block_reason_message'] = chunk.prompt_feedback.block_reason_message
                    if chunk.prompt_feedback.safety_ratings:
                        self.provider_details['safety_ratings'] = [
                            r.model_dump(by_alias=True) for r in chunk.prompt_feedback.safety_ratings
                        ]
                    self.finish_reason = 'content_filter'
                    if chunk.response_id:  # pragma: no branch
                        self.provider_response_id = chunk.response_id
                continue

            candidate = chunk.candidates[0]

            if chunk.response_id:  # pragma: no branch
                self.provider_response_id = chunk.response_id

            raw_finish_reason = candidate.finish_reason
            if raw_finish_reason and not self._has_content_filter:
                self.provider_details = {**(self.provider_details or {}), 'finish_reason': raw_finish_reason.value}

                if candidate.safety_ratings:
                    self.provider_details['safety_ratings'] = [
                        r.model_dump(by_alias=True) for r in candidate.safety_ratings
                    ]

                self.finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason)

            # Google streams the grounding metadata (including the web search queries and results)
            # _after_ the text that was generated using it, so it would show up out of order in the stream,
            # and cause issues with the logic that doesn't consider text ahead of built-in tool calls as output.
            # If that gets fixed (or we have a workaround), we can uncomment this:
            # web_search_call, web_search_return = _map_grounding_metadata(
            #     candidate.grounding_metadata, self.provider_name
            # )
            # if web_search_call and web_search_return:
            #     yield self._parts_manager.handle_part(vendor_part_id=uuid4(), part=web_search_call)
            #     yield self._parts_manager.handle_part(
            #         vendor_part_id=uuid4(), part=web_search_return
            #     )

            # URL context metadata (for WebFetchTool) is streamed in the first chunk, before the text,
            # so we can safely yield it here
            web_fetch_call, web_fetch_return = _map_url_context_metadata(
                candidate.url_context_metadata, self.provider_name
            )
            if web_fetch_call and web_fetch_return:
                yield self._parts_manager.handle_part(vendor_part_id=uuid4(), part=web_fetch_call)
                yield self._parts_manager.handle_part(vendor_part_id=uuid4(), part=web_fetch_return)

            if candidate.content is None or candidate.content.parts is None:
                continue

            parts = candidate.content.parts
            if not parts:
                continue  # pragma: no cover

            for part in parts:
                provider_details: dict[str, Any] | None = None
                if part.thought_signature:
                    # Per https://ai.google.dev/gemini-api/docs/function-calling?example=meeting#thought-signatures:
                    # - Always send the thought_signature back to the model inside its original Part.
                    # - Don't merge a Part containing a signature with one that does not. This breaks the positional context of the thought.
                    # - Don't combine two Parts that both contain signatures, as the signature strings cannot be merged.
                    thought_signature = base64.b64encode(part.thought_signature).decode('utf-8')
                    provider_details = {'thought_signature': thought_signature}

                if part.text is not None:
                    if len(part.text) == 0 and not provider_details:
                        continue
                    if part.thought:
                        for event in self._parts_manager.handle_thinking_delta(
                            vendor_part_id=None,
                            content=part.text,
                            provider_name=self.provider_name if provider_details else None,
                            provider_details=provider_details,
                        ):
                            yield event
                    else:
                        for event in self._parts_manager.handle_text_delta(
                            vendor_part_id=None,
                            content=part.text,
                            provider_name=self.provider_name if provider_details else None,
                            provider_details=provider_details,
                        ):
                            yield event
                elif part.function_call:
                    maybe_event = self._parts_manager.handle_tool_call_delta(
                        vendor_part_id=uuid4(),
                        tool_name=part.function_call.name,
                        args=part.function_call.args,
                        tool_call_id=part.function_call.id,
                        provider_name=self.provider_name if provider_details else None,
                        provider_details=provider_details,
                    )
                    if maybe_event is not None:  # pragma: no branch
                        yield maybe_event
                elif part.inline_data is not None:
                    if part.thought:  # pragma: no cover
                        # Per https://ai.google.dev/gemini-api/docs/image-generation#thinking-process:
                        # > The model generates up to two interim images to test composition and logic. The last image within Thinking is also the final rendered image.
                        # We currently don't expose these image thoughts as they can't be represented with `ThinkingPart`
                        continue
                    data = part.inline_data.data
                    mime_type = part.inline_data.mime_type
                    assert data and mime_type, 'Inline data must have data and mime type'
                    content = BinaryContent(data=data, media_type=mime_type)
                    yield self._parts_manager.handle_part(
                        vendor_part_id=uuid4(),
                        part=FilePart(
                            content=BinaryContent.narrow_type(content),
                            provider_name=self.provider_name if provider_details else None,
                            provider_details=provider_details,
                        ),
                    )
                elif part.executable_code is not None:
                    part_obj = self._handle_executable_code_streaming(part.executable_code)
                    part_obj.provider_details = provider_details
                    yield self._parts_manager.handle_part(vendor_part_id=uuid4(), part=part_obj)
                elif part.code_execution_result is not None:
                    part = self._map_code_execution_result(part.code_execution_result)
                    part.provider_details = provider_details
                    yield self._parts_manager.handle_part(vendor_part_id=uuid4(), part=part)
                else:
                    assert part.function_response is not None, f'Unexpected part: {part}'  # pragma: no cover

            # Grounding metadata is attached to the final text chunk, so
            # we emit the `BuiltinToolReturnPart` after the text delta so
            # that the delta is properly added to the same `TextPart` as earlier chunks
            file_search_part = self._handle_file_search_grounding_metadata_streaming(candidate.grounding_metadata)
            if file_search_part is not None:
                yield self._parts_manager.handle_part(vendor_part_id=uuid4(), part=file_search_part)

    def _handle_file_search_grounding_metadata_streaming(
        self, grounding_metadata: GroundingMetadata | None
    ) -> BuiltinToolReturnPart | None:
        """Handle file search grounding metadata for streaming responses.

        Returns a BuiltinToolReturnPart if file search results are available in the grounding metadata.
        """
        if not self._file_search_tool_call_id or not grounding_metadata:
            return None

        grounding_chunks = grounding_metadata.grounding_chunks
        retrieved_contexts = _extract_file_search_retrieved_contexts(grounding_chunks)
        if retrieved_contexts:  # pragma: no branch
            part = BuiltinToolReturnPart(
                provider_name=self.provider_name,
                tool_name=FileSearchTool.kind,
                tool_call_id=self._file_search_tool_call_id,
                content=retrieved_contexts,
            )
            self._file_search_tool_call_id = None
            return part
        return None  # pragma: no cover

    def _map_code_execution_result(self, code_execution_result: CodeExecutionResult) -> BuiltinToolReturnPart:
        """Map code execution result to a BuiltinToolReturnPart using instance state."""
        assert self._code_execution_tool_call_id is not None
        return _map_code_execution_result(code_execution_result, self.provider_name, self._code_execution_tool_call_id)

    def _handle_executable_code_streaming(self, executable_code: ExecutableCode) -> ModelResponsePart:
        """Handle executable code for streaming responses.

        Returns a BuiltinToolCallPart for file search or code execution.
        Sets self._code_execution_tool_call_id or self._file_search_tool_call_id as appropriate.
        """
        code = executable_code.code
        has_file_search_tool = any(
            isinstance(tool, FileSearchTool) for tool in self.model_request_parameters.builtin_tools
        )

        if code and has_file_search_tool and (file_search_query := self._extract_file_search_query(code)):
            self._file_search_tool_call_id = _utils.generate_tool_call_id()
            return BuiltinToolCallPart(
                provider_name=self.provider_name,
                tool_name=FileSearchTool.kind,
                tool_call_id=self._file_search_tool_call_id,
                args={'query': file_search_query},
            )

        self._code_execution_tool_call_id = _utils.generate_tool_call_id()
        return _map_executable_code(executable_code, self.provider_name, self._code_execution_tool_call_id)

    def _extract_file_search_query(self, code: str) -> str | None:
        """Extract the query from file_search.query() executable code.

        Handles escaped quotes in the query string.

        Example: 'print(file_search.query(query="what is the capital of France?"))'
        Returns: 'what is the capital of France?'
        """
        match = _FILE_SEARCH_QUERY_PATTERN.search(code)
        if match:
            query = match.group(2)
            query = query.replace('\\\\', '\\').replace('\\"', '"').replace("\\'", "'")
            return query
        return None  # pragma: no cover

    @property
    def model_name(self) -> GoogleModelName:
        """Get the model name of the response."""
        return self._model_name

    @property
    def provider_name(self) -> str:
        """Get the provider name."""
        return self._provider_name

    @property
    def provider_url(self) -> str:
        """Get the provider base URL."""
        return self._provider_url

    @property
    def timestamp(self) -> datetime:
        """Get the timestamp of the response."""
        return self._timestamp

model_name property

model_name: GoogleModelName

Get the model name of the response.

provider_name property

provider_name: str

Get the provider name.

provider_url property

provider_url: str

Get the provider base URL.

timestamp property

timestamp: datetime

Get the timestamp of the response.