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[ pydantic/pydantic-ai
  * v1.63.0
  * 15.1k
  * 1.7k

](https://github.com/pydantic/pydantic-ai "Go to repository")
  * [ Pydantic AI  ](https://ai.pydantic.dev/)
  * [ Installation  ](https://ai.pydantic.dev/install/)
  * [ Getting Help  ](https://ai.pydantic.dev/help/)
  * [ Troubleshooting  ](https://ai.pydantic.dev/troubleshooting/)
  * [ Pydantic AI Gateway  ](https://ai.pydantic.dev/gateway/)
  * Documentation
    * Core Concepts
      * [ Agents  ](https://ai.pydantic.dev/agent/)
      * [ Dependencies  ](https://ai.pydantic.dev/dependencies/)
      * [ Function Tools  ](https://ai.pydantic.dev/tools/)
      * [ Output  ](https://ai.pydantic.dev/output/)
      * [ Messages and chat history  ](https://ai.pydantic.dev/message-history/)
      * [ Direct Model Requests  ](https://ai.pydantic.dev/direct/)
    * Models & Providers
      * [ Overview  ](https://ai.pydantic.dev/models/overview/)
      * [ OpenAI  ](https://ai.pydantic.dev/models/openai/)
      * [ Anthropic  ](https://ai.pydantic.dev/models/anthropic/)
      * [ Google  ](https://ai.pydantic.dev/models/google/)
      * [ xAI  ](https://ai.pydantic.dev/models/xai/)
      * [ Bedrock  ](https://ai.pydantic.dev/models/bedrock/)
      * [ Cerebras  ](https://ai.pydantic.dev/models/cerebras/)
      * [ Cohere  ](https://ai.pydantic.dev/models/cohere/)
      * [ Groq  ](https://ai.pydantic.dev/models/groq/)
      * [ Hugging Face  ](https://ai.pydantic.dev/models/huggingface/)
      * [ Mistral  ](https://ai.pydantic.dev/models/mistral/)
      * [ OpenRouter  ](https://ai.pydantic.dev/models/openrouter/)
      * [ Outlines  ](https://ai.pydantic.dev/models/outlines/)
    * Tools & Toolsets
      * [ Function Tools  ](https://ai.pydantic.dev/tools/)
      * [ Advanced Tool Features  ](https://ai.pydantic.dev/tools-advanced/)
      * [ Toolsets  ](https://ai.pydantic.dev/toolsets/)
      * [ Deferred Tools  ](https://ai.pydantic.dev/deferred-tools/)
      * [ Built-in Tools  ](https://ai.pydantic.dev/builtin-tools/)
      * [ Common Tools  ](https://ai.pydantic.dev/common-tools/)
      * [ Third-Party Tools  ](https://ai.pydantic.dev/third-party-tools/)
    * Advanced Features
      * [ Image, Audio, Video & Document Input  ](https://ai.pydantic.dev/input/)
      * [ Thinking  ](https://ai.pydantic.dev/thinking/)
      * [ HTTP Request Retries  ](https://ai.pydantic.dev/retries/)
    * MCP
      * [ Overview  ](https://ai.pydantic.dev/mcp/overview/)
      * [ Client  ](https://ai.pydantic.dev/mcp/client/)
      * [ FastMCP Client  ](https://ai.pydantic.dev/mcp/fastmcp-client/)
      * [ Server  ](https://ai.pydantic.dev/mcp/server/)
    * [ Multi-Agent Patterns  ](https://ai.pydantic.dev/multi-agent-applications/)
    * [ Web Chat UI  ](https://ai.pydantic.dev/web/)
    * [ Embeddings  ](https://ai.pydantic.dev/embeddings/)
    * [ Testing  ](https://ai.pydantic.dev/testing/)
  * Pydantic Evals
    * [ Overview  ](https://ai.pydantic.dev/evals/)
    * Getting Started
      * [ Quick Start  ](https://ai.pydantic.dev/evals/quick-start/)
      * [ Core Concepts  ](https://ai.pydantic.dev/evals/core-concepts/)
    * Evaluators
      * [ Overview  ](https://ai.pydantic.dev/evals/evaluators/overview/)
      * [ Built-in Evaluators  ](https://ai.pydantic.dev/evals/evaluators/built-in/)
      * [ LLM Judge  ](https://ai.pydantic.dev/evals/evaluators/llm-judge/)
      * [ Custom Evaluators  ](https://ai.pydantic.dev/evals/evaluators/custom/)
      * [ Report Evaluators  ](https://ai.pydantic.dev/evals/evaluators/report-evaluators/)
      * [ Span-Based  ](https://ai.pydantic.dev/evals/evaluators/span-based/)
    * How-To Guides
      * [ Logfire Integration  ](https://ai.pydantic.dev/evals/how-to/logfire-integration/)
      * [ Dataset Management  ](https://ai.pydantic.dev/evals/how-to/dataset-management/)
      * [ Dataset Serialization  ](https://ai.pydantic.dev/evals/how-to/dataset-serialization/)
      * [ Concurrency & Performance  ](https://ai.pydantic.dev/evals/how-to/concurrency/)
      * [ Multi-Run Evaluation  ](https://ai.pydantic.dev/evals/how-to/multi-run/)
      * [ Retry Strategies  ](https://ai.pydantic.dev/evals/how-to/retry-strategies/)
      * [ Metrics & Attributes  ](https://ai.pydantic.dev/evals/how-to/metrics-attributes/)
    * Examples
      * [ Simple Validation  ](https://ai.pydantic.dev/evals/examples/simple-validation/)
  * Pydantic Graph
    * [ Overview  ](https://ai.pydantic.dev/graph/)
    * [ Beta API  ](https://ai.pydantic.dev/graph/beta/)
      * [ Steps  ](https://ai.pydantic.dev/graph/beta/steps/)
      * [ Joins & Reducers  ](https://ai.pydantic.dev/graph/beta/joins/)
      * [ Decisions  ](https://ai.pydantic.dev/graph/beta/decisions/)
      * [ Parallel Execution  ](https://ai.pydantic.dev/graph/beta/parallel/)
  * Integrations
    * [ Debugging & Monitoring with Pydantic Logfire  ](https://ai.pydantic.dev/logfire/)
    * Durable Execution
      * [ Overview  ](https://ai.pydantic.dev/durable_execution/overview/)
      * [ Temporal  ](https://ai.pydantic.dev/durable_execution/temporal/)
      * [ DBOS  ](https://ai.pydantic.dev/durable_execution/dbos/)
      * [ Prefect  ](https://ai.pydantic.dev/durable_execution/prefect/)
    * UI Event Streams
      * [ Overview  ](https://ai.pydantic.dev/ui/overview/)
      * [ AG-UI  ](https://ai.pydantic.dev/ui/ag-ui/)
      * [ Vercel AI  ](https://ai.pydantic.dev/ui/vercel-ai/)
    * [ Agent2Agent (A2A)  ](https://ai.pydantic.dev/a2a/)
  * Related Packages
    * [ clai  ](https://ai.pydantic.dev/cli/)
  * Examples
    * [ Setup  ](https://ai.pydantic.dev/examples/setup/)
    * Getting Started
      * [ Pydantic Model  ](https://ai.pydantic.dev/examples/pydantic-model/)
      * [ Weather agent  ](https://ai.pydantic.dev/examples/weather-agent/)
    * Conversational Agents
      * [ Chat App with FastAPI  ](https://ai.pydantic.dev/examples/chat-app/)
      * [ Bank support  ](https://ai.pydantic.dev/examples/bank-support/)
    * Data & Analytics
      * [ SQL Generation  ](https://ai.pydantic.dev/examples/sql-gen/)
      * [ Data Analyst  ](https://ai.pydantic.dev/examples/data-analyst/)
      * [ RAG  ](https://ai.pydantic.dev/examples/rag/)
    * Streaming
      * [ Stream markdown  ](https://ai.pydantic.dev/examples/stream-markdown/)
      * [ Stream whales  ](https://ai.pydantic.dev/examples/stream-whales/)
    * Complex Workflows
      * [ Flight booking  ](https://ai.pydantic.dev/examples/flight-booking/)
      * [ Question Graph  ](https://ai.pydantic.dev/examples/question-graph/)
    * Business Applications
      * [ Slack Lead Qualifier with Modal  ](https://ai.pydantic.dev/examples/slack-lead-qualifier/)
    * UI Examples
      * [ Agent User Interaction (AG-UI)  ](https://ai.pydantic.dev/examples/ag-ui/)
  * API Reference
    * pydantic_ai
      * [ pydantic_ai.ag_ui  ](https://ai.pydantic.dev/api/ag_ui/)
      * [ pydantic_ai.agent  ](https://ai.pydantic.dev/api/agent/)
      * [ pydantic_ai.builtin_tools  ](https://ai.pydantic.dev/api/builtin_tools/)
      * [ pydantic_ai.common_tools  ](https://ai.pydantic.dev/api/common_tools/)
      * [ pydantic_ai — Concurrency  ](https://ai.pydantic.dev/api/concurrency/)
      * [ pydantic_ai.direct  ](https://ai.pydantic.dev/api/direct/)
      * [ pydantic_ai.durable_exec  ](https://ai.pydantic.dev/api/durable_exec/)
      * [ pydantic_ai.embeddings  ](https://ai.pydantic.dev/api/embeddings/)
      * [ pydantic_ai.exceptions  ](https://ai.pydantic.dev/api/exceptions/)
      * [ pydantic_ai.ext  ](https://ai.pydantic.dev/api/ext/)
      * [ pydantic_ai.format_prompt  ](https://ai.pydantic.dev/api/format_prompt/)
      * [ pydantic_ai.mcp  ](https://ai.pydantic.dev/api/mcp/)
      * [ pydantic_ai.messages  ](https://ai.pydantic.dev/api/messages/)
      * [ pydantic_ai.models.anthropic  ](https://ai.pydantic.dev/api/models/anthropic/)
      * [ pydantic_ai.models  ](https://ai.pydantic.dev/api/models/base/)
      * [ pydantic_ai.models.bedrock  ](https://ai.pydantic.dev/api/models/bedrock/)
      * [ pydantic_ai.models.cerebras  ](https://ai.pydantic.dev/api/models/cerebras/)
      * [ pydantic_ai.models.cohere  ](https://ai.pydantic.dev/api/models/cohere/)
      * [ pydantic_ai.models.fallback  ](https://ai.pydantic.dev/api/models/fallback/)
      * [ pydantic_ai.models.function  ](https://ai.pydantic.dev/api/models/function/)
      * pydantic_ai.models.google  [ pydantic_ai.models.google  ](https://ai.pydantic.dev/api/models/google/)
        * [ Setup  ](https://ai.pydantic.dev/api/models/google/#setup)
          * [ google  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google)
          * [ LatestGoogleModelNames  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.LatestGoogleModelNames)
          * [ GoogleModelName  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelName)
          * [ GoogleModelSettings  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings)
            * [ google_safety_settings  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_safety_settings)
            * [ google_thinking_config  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_thinking_config)
            * [ google_labels  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_labels)
            * [ google_video_resolution  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_video_resolution)
            * [ google_cached_content  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_cached_content)
            * [ google_logprobs  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_logprobs)
            * [ google_top_logprobs  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_top_logprobs)
          * [ GoogleModel  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel)
            * [ __init__  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.__init__)
            * [ model_name  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.model_name)
            * [ system  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.system)
            * [ supported_builtin_tools  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.supported_builtin_tools)
          * [ GeminiStreamedResponse  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse)
            * [ model_name  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.model_name)
            * [ provider_name  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.provider_name)
            * [ provider_url  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.provider_url)
            * [ timestamp  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.timestamp)
      * [ pydantic_ai.models.xai  ](https://ai.pydantic.dev/api/models/xai/)
      * [ pydantic_ai.models.groq  ](https://ai.pydantic.dev/api/models/groq/)
      * [ pydantic_ai.models.huggingface  ](https://ai.pydantic.dev/api/models/huggingface/)
      * [ pydantic_ai.models.instrumented  ](https://ai.pydantic.dev/api/models/instrumented/)
      * [ pydantic_ai.models.mcp_sampling  ](https://ai.pydantic.dev/api/models/mcp-sampling/)
      * [ pydantic_ai.models.mistral  ](https://ai.pydantic.dev/api/models/mistral/)
      * [ pydantic_ai.models.openai  ](https://ai.pydantic.dev/api/models/openai/)
      * [ pydantic_ai.models.openrouter  ](https://ai.pydantic.dev/api/models/openrouter/)
      * [ pydantic_ai.models.outlines  ](https://ai.pydantic.dev/api/models/outlines/)
      * [ pydantic_ai.models.test  ](https://ai.pydantic.dev/api/models/test/)
      * [ pydantic_ai.models.wrapper  ](https://ai.pydantic.dev/api/models/wrapper/)
      * [ pydantic_ai.output  ](https://ai.pydantic.dev/api/output/)
      * [ pydantic_ai.profiles  ](https://ai.pydantic.dev/api/profiles/)
      * [ pydantic_ai.providers  ](https://ai.pydantic.dev/api/providers/)
      * [ pydantic_ai.result  ](https://ai.pydantic.dev/api/result/)
      * [ pydantic_ai.retries  ](https://ai.pydantic.dev/api/retries/)
      * [ pydantic_ai.run  ](https://ai.pydantic.dev/api/run/)
      * [ pydantic_ai.settings  ](https://ai.pydantic.dev/api/settings/)
      * [ pydantic_ai.tools  ](https://ai.pydantic.dev/api/tools/)
      * [ pydantic_ai.toolsets  ](https://ai.pydantic.dev/api/toolsets/)
      * [ pydantic_ai.ui.ag_ui  ](https://ai.pydantic.dev/api/ui/ag_ui/)
      * [ pydantic_ai.ui  ](https://ai.pydantic.dev/api/ui/base/)
      * [ pydantic_ai.ui.vercel_ai  ](https://ai.pydantic.dev/api/ui/vercel_ai/)
      * [ pydantic_ai.usage  ](https://ai.pydantic.dev/api/usage/)
    * pydantic_evals
      * [ pydantic_evals.dataset  ](https://ai.pydantic.dev/api/pydantic_evals/dataset/)
      * [ pydantic_evals.evaluators  ](https://ai.pydantic.dev/api/pydantic_evals/evaluators/)
      * [ pydantic_evals.reporting  ](https://ai.pydantic.dev/api/pydantic_evals/reporting/)
      * [ pydantic_evals.otel  ](https://ai.pydantic.dev/api/pydantic_evals/otel/)
      * [ pydantic_evals.generation  ](https://ai.pydantic.dev/api/pydantic_evals/generation/)
    * pydantic_graph
      * [ pydantic_graph  ](https://ai.pydantic.dev/api/pydantic_graph/graph/)
      * [ pydantic_graph.nodes  ](https://ai.pydantic.dev/api/pydantic_graph/nodes/)
      * [ pydantic_graph.persistence  ](https://ai.pydantic.dev/api/pydantic_graph/persistence/)
      * [ pydantic_graph.mermaid  ](https://ai.pydantic.dev/api/pydantic_graph/mermaid/)
      * [ pydantic_graph.exceptions  ](https://ai.pydantic.dev/api/pydantic_graph/exceptions/)
      * Beta API
        * [ pydantic_graph.beta  ](https://ai.pydantic.dev/api/pydantic_graph/beta/)
        * [ pydantic_graph.beta.graph  ](https://ai.pydantic.dev/api/pydantic_graph/beta_graph/)
        * [ pydantic_graph.beta.graph_builder  ](https://ai.pydantic.dev/api/pydantic_graph/beta_graph_builder/)
        * [ pydantic_graph.beta.step  ](https://ai.pydantic.dev/api/pydantic_graph/beta_step/)
        * [ pydantic_graph.beta.join  ](https://ai.pydantic.dev/api/pydantic_graph/beta_join/)
        * [ pydantic_graph.beta.decision  ](https://ai.pydantic.dev/api/pydantic_graph/beta_decision/)
        * [ pydantic_graph.beta.node  ](https://ai.pydantic.dev/api/pydantic_graph/beta_node/)
    * fasta2a
      * [ fasta2a  ](https://ai.pydantic.dev/api/fasta2a/)
  * Project
    * [ Contributing  ](https://ai.pydantic.dev/contributing/)
    * [ Upgrade Guide  ](https://ai.pydantic.dev/changelog/)
    * [ Version policy  ](https://ai.pydantic.dev/version-policy/)


  * [ Setup  ](https://ai.pydantic.dev/api/models/google/#setup)
    * [ google  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google)
    * [ LatestGoogleModelNames  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.LatestGoogleModelNames)
    * [ GoogleModelName  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelName)
    * [ GoogleModelSettings  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings)
      * [ google_safety_settings  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_safety_settings)
      * [ google_thinking_config  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_thinking_config)
      * [ google_labels  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_labels)
      * [ google_video_resolution  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_video_resolution)
      * [ google_cached_content  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_cached_content)
      * [ google_logprobs  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_logprobs)
      * [ google_top_logprobs  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelSettings.google_top_logprobs)
    * [ GoogleModel  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel)
      * [ __init__  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.__init__)
      * [ model_name  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.model_name)
      * [ system  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.system)
      * [ supported_builtin_tools  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModel.supported_builtin_tools)
    * [ GeminiStreamedResponse  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse)
      * [ model_name  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.model_name)
      * [ provider_name  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.provider_name)
      * [ provider_url  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.provider_url)
      * [ timestamp  ](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GeminiStreamedResponse.timestamp)


  1. [ Pydantic AI  ](https://ai.pydantic.dev/)
  2. [ API Reference  ](https://ai.pydantic.dev/api/ag_ui/)
  3. [ pydantic_ai  ](https://ai.pydantic.dev/api/ag_ui/)


# `pydantic_ai.models.google`
Interface that uses the [`google-genai`](https://pypi.org/project/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](https://ai.pydantic.dev/models/google/).
###  LatestGoogleModelNames `module-attribute`
```
LatestGoogleModelNames = Literal[](https://docs.python.org/3/library/typing.html#typing.Literal "typing.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[](https://docs.python.org/3/library/stdtypes.html#str) | LatestGoogleModelNames[](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.LatestGoogleModelNames "LatestGoogleModelNames



      module-attribute
   \(pydantic_ai.models.google.LatestGoogleModelNames\)")

```

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](https://ai.google.dev/gemini-api/docs/models/gemini#model-variations) for a full list.
###  GoogleModelSettings
Bases: `ModelSettings[](https://ai.pydantic.dev/api/settings/#pydantic_ai.settings.ModelSettings "ModelSettings \(pydantic_ai.settings.ModelSettings\)")`
Settings used for a Gemini model request.
Source code in `pydantic_ai_slim/pydantic_ai/models/google.py`
```
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```
| ```
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[](https://docs.python.org/3/library/stdtypes.html#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[](https://docs.python.org/3/library/stdtypes.html#dict)[str[](https://docs.python.org/3/library/stdtypes.html#str), str[](https://docs.python.org/3/library/stdtypes.html#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 `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[](https://docs.python.org/3/library/stdtypes.html#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[](https://docs.python.org/3/library/functions.html#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[](https://docs.python.org/3/library/functions.html#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[](https://ai.pydantic.dev/api/models/base/#pydantic_ai.models.Model "Model \(pydantic_ai.models.Model\)")`
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.
Source code in `pydantic_ai_slim/pydantic_ai/models/google.py`
```
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```
| ```
@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[](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelName "GoogleModelName



      module-attribute
   \(pydantic_ai.models.google.GoogleModelName\)"),
    *,
    provider: (
        Literal[](https://docs.python.org/3/library/typing.html#typing.Literal "typing.Literal")["google-gla", "google-vertex", "gateway"]
        | Provider[](https://ai.pydantic.dev/api/providers/#pydantic_ai.providers.Provider "pydantic_ai.providers.Provider")[Client]
    ) = "google-gla",
    profile: ModelProfileSpec | None = None,
    settings: ModelSettings[](https://ai.pydantic.dev/api/settings/#pydantic_ai.settings.ModelSettings "ModelSettings \(pydantic_ai.settings.ModelSettings\)") | None = None
)

```

Initialize a Gemini model.
Parameters:
Name | Type | Description | Default
---|---|---|---
`model_name` |  `GoogleModelName[](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelName "GoogleModelName



      module-attribute
   \(pydantic_ai.models.google.GoogleModelName\)")` |  The name of the model to use. |  _required_
`provider` |  `Literal[](https://docs.python.org/3/library/typing.html#typing.Literal "typing.Literal")['google-gla', 'google-vertex', 'gateway'] | Provider[](https://ai.pydantic.dev/api/providers/#pydantic_ai.providers.Provider "pydantic_ai.providers.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[](https://ai.pydantic.dev/api/settings/#pydantic_ai.settings.ModelSettings "ModelSettings \(pydantic_ai.settings.ModelSettings\)") | None` |  The model settings to use. Defaults to None. |  `None`
Source code in `pydantic_ai_slim/pydantic_ai/models/google.py`
```
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```
| ```
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[](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelName "GoogleModelName



      module-attribute
   \(pydantic_ai.models.google.GoogleModelName\)")

```

The model name.
####  system `property`
```
system: str[](https://docs.python.org/3/library/stdtypes.html#str)

```

The model provider.
####  supported_builtin_tools `classmethod`
```
supported_builtin_tools() -> (
    frozenset[](https://docs.python.org/3/library/stdtypes.html#frozenset)[type[](https://docs.python.org/3/library/functions.html#type)[AbstractBuiltinTool[](https://ai.pydantic.dev/api/builtin_tools/#pydantic_ai.builtin_tools.AbstractBuiltinTool "AbstractBuiltinTool



      dataclass
   \(pydantic_ai.builtin_tools.AbstractBuiltinTool\)")]]
)

```

Return the set of builtin tool types this model can handle.
Source code in `pydantic_ai_slim/pydantic_ai/models/google.py`
```
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```
| ```
@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[](https://ai.pydantic.dev/api/models/base/#pydantic_ai.models.StreamedResponse "StreamedResponse



      dataclass
   \(pydantic_ai.models.StreamedResponse\)")`
Implementation of `StreamedResponse` for the Gemini model.
Source code in `pydantic_ai_slim/pydantic_ai/models/google.py`
```
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```
| ```
@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[](https://ai.pydantic.dev/api/models/google/#pydantic_ai.models.google.GoogleModelName "GoogleModelName



      module-attribute
   \(pydantic_ai.models.google.GoogleModelName\)")

```

Get the model name of the response.
####  provider_name `property`
```
provider_name: str[](https://docs.python.org/3/library/stdtypes.html#str)

```

Get the provider name.
####  provider_url `property`
```
provider_url: str[](https://docs.python.org/3/library/stdtypes.html#str)

```

Get the provider base URL.
####  timestamp `property`
```
timestamp: datetime[](https://docs.python.org/3/library/datetime.html#datetime.datetime "datetime.datetime")

```

Get the timestamp of the response.
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