# generated by datamodel-codegen:
#   filename:  https://ai.google.dev/static/api/interactions.openapi.json
#   timestamp: 2025-12-16T21:25:12+00:00

from __future__ import annotations

from enum import Enum
from typing import Any, Dict, List, Literal, Optional, Union

from pydantic import AwareDatetime, Base64Str, BaseModel, Field, RootModel


class Annotation(BaseModel):
    start_index: Optional[int] = Field(
        None,
        description="Start of segment of the response that is attributed to this source.\n\nIndex indicates the start of the segment, measured in bytes.",
    )
    end_index: Optional[int] = Field(
        None, description="End of the attributed segment, exclusive."
    )
    source: Optional[str] = Field(
        None,
        description="Source attributed for a portion of the text. Could be a URL, title, or\nother identifier.",
    )


class DocumentContent(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[str] = None
    type: Literal["document"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class FunctionCallContent(BaseModel):
    name: str = Field(..., description="The name of the tool to call.")
    arguments: Dict[str, Any] = Field(
        ..., description="The arguments to pass to the function."
    )
    type: Literal["function_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: str = Field(..., description="A unique ID for this specific tool call.")


class Language(Enum):
    python = "python"


class CodeExecutionCallArguments(BaseModel):
    language: Optional[Language] = Field(
        None, description="Programming language of the `code`."
    )
    code: Optional[str] = Field(None, description="The code to be executed.")


class UrlContextCallArguments(BaseModel):
    urls: Optional[List[str]] = Field(None, description="The URLs to fetch.")


class McpServerToolCallContent(BaseModel):
    name: str = Field(..., description="The name of the tool which was called.")
    server_name: str = Field(..., description="The name of the used MCP server.")
    arguments: Dict[str, Any] = Field(
        ..., description="The JSON object of arguments for the function."
    )
    type: Literal["mcp_server_tool_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: str = Field(..., description="A unique ID for this specific tool call.")


class GoogleSearchCallArguments(BaseModel):
    queries: Optional[List[str]] = Field(
        None, description="Web search queries for the following-up web search."
    )


class CodeExecutionResultContent(BaseModel):
    result: Optional[str] = Field(None, description="The output of the code execution.")
    is_error: Optional[bool] = Field(
        None, description="Whether the code execution resulted in an error."
    )
    signature: Optional[str] = Field(
        None, description="A signature hash for backend validation."
    )
    type: Literal["code_execution_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the code execution call block."
    )


class Status(Enum):
    success = "success"
    error = "error"
    paywall = "paywall"
    unsafe = "unsafe"


class UrlContextResult(BaseModel):
    url: Optional[str] = Field(None, description="The URL that was fetched.")
    status: Optional[Status] = Field(
        None, description="The status of the URL retrieval."
    )


class GoogleSearchResult(BaseModel):
    url: Optional[str] = Field(None, description="URI reference of the search result.")
    title: Optional[str] = Field(None, description="Title of the search result.")
    rendered_content: Optional[str] = Field(
        None,
        description="Web content snippet that can be embedded in a web page or an app webview.",
    )


class FileSearchResult(BaseModel):
    title: Optional[str] = Field(None, description="The title of the search result.")
    text: Optional[str] = Field(None, description="The text of the search result.")
    file_search_store: Optional[str] = Field(
        None, description="The name of the file search store."
    )


class SpeechConfig(BaseModel):
    voice: Optional[str] = Field(None, description="The voice of the speaker.")
    language: Optional[str] = Field(None, description="The language of the speech.")
    speaker: Optional[str] = Field(
        None,
        description="The speaker's name, it should match the speaker name given in the prompt.",
    )


class DynamicAgentConfig(BaseModel):
    type: Literal["dynamic"] = Field(
        "dynamic",
        description="Used as the OpenAPI type discriminator for the content oneof.",
    )


class Function(BaseModel):
    name: Optional[str] = Field(None, description="The name of the function.")
    description: Optional[str] = Field(
        None, description="A description of the function."
    )
    parameters: Optional[Any] = Field(
        None, description="The JSON Schema for the function's parameters."
    )
    type: Literal["function"]


class CodeExecution(BaseModel):
    type: Literal["code_execution"]


class UrlContext(BaseModel):
    type: Literal["url_context"]


class Environment(Enum):
    browser = "browser"


class ComputerUse(BaseModel):
    type: Literal["computer_use"]
    environment: Optional[Environment] = Field(
        None, description="The environment being operated."
    )
    excludedPredefinedFunctions: Optional[List[str]] = Field(
        None,
        description="The list of predefined functions that are excluded from the model call.",
    )


class GoogleSearch(BaseModel):
    type: Literal["google_search"]


class FileSearch(BaseModel):
    file_search_store_names: Optional[List[str]] = Field(
        None, description="The file search store names to search."
    )
    top_k: Optional[int] = Field(
        None, description="The number of semantic retrieval chunks to retrieve."
    )
    metadata_filter: Optional[str] = Field(
        None,
        description="Metadata filter to apply to the semantic retrieval documents and chunks.",
    )
    type: Literal["file_search"]


class EventType(Enum):
    interaction_start = "interaction.start"
    interaction_complete = "interaction.complete"


class Status1(Enum):
    in_progress = "in_progress"
    requires_action = "requires_action"
    completed = "completed"
    failed = "failed"
    cancelled = "cancelled"


class InteractionStatusUpdate(BaseModel):
    interaction_id: Optional[str] = None
    status: Optional[Status1] = None
    event_type: Literal["interaction.status_update"] = "interaction.status_update"
    event_id: Optional[str] = Field(
        None,
        description="The event_id token to be used to resume the interaction stream, from\nthis event.",
    )


class TextDelta(BaseModel):
    text: Optional[str] = None
    type: Literal["text"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    annotations: Optional[List[Annotation]] = Field(
        None, description="Citation information for model-generated content."
    )


class DocumentDelta(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[str] = None
    type: Literal["document"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class ThoughtSignatureDelta(BaseModel):
    signature: Optional[Base64Str] = Field(
        None,
        description="Signature to match the backend source to be part of the generation.",
    )
    type: Literal["thought_signature"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class FunctionCallDelta(BaseModel):
    name: Optional[str] = None
    arguments: Optional[Dict[str, Any]] = None
    type: Literal["function_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class CodeExecutionCallDelta(BaseModel):
    arguments: Optional[CodeExecutionCallArguments] = None
    type: Literal["code_execution_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class UrlContextCallDelta(BaseModel):
    arguments: Optional[UrlContextCallArguments] = None
    type: Literal["url_context_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class GoogleSearchCallDelta(BaseModel):
    arguments: Optional[GoogleSearchCallArguments] = None
    type: Literal["google_search_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class McpServerToolCallDelta(BaseModel):
    name: Optional[str] = None
    server_name: Optional[str] = None
    arguments: Optional[Dict[str, Any]] = None
    type: Literal["mcp_server_tool_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class CodeExecutionResultDelta(BaseModel):
    result: Optional[str] = None
    is_error: Optional[bool] = None
    signature: Optional[str] = None
    type: Literal["code_execution_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the function call block."
    )


class UrlContextResultDelta(BaseModel):
    signature: Optional[str] = None
    result: Optional[List[UrlContextResult]] = None
    is_error: Optional[bool] = None
    type: Literal["url_context_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the function call block."
    )


class GoogleSearchResultDelta(BaseModel):
    signature: Optional[str] = None
    result: Optional[List[GoogleSearchResult]] = None
    is_error: Optional[bool] = None
    type: Literal["google_search_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the function call block."
    )


class FileSearchResultDelta(BaseModel):
    result: Optional[List[FileSearchResult]] = None
    type: Literal["file_search_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class ContentStop(BaseModel):
    index: Optional[int] = None
    event_type: Literal["content.stop"] = "content.stop"
    event_id: Optional[str] = Field(
        None,
        description="The event_id token to be used to resume the interaction stream, from\nthis event.",
    )


class Error(BaseModel):
    code: Optional[str] = Field(
        None, description="A URI that identifies the error type."
    )
    message: Optional[str] = Field(None, description="A human-readable error message.")


class MediaResolution(Enum):
    low = "low"
    medium = "medium"
    high = "high"


class ToolChoiceType(Enum):
    auto = "auto"
    any = "any"
    none = "none"
    validated = "validated"


class ThinkingLevel(Enum):
    low = "low"
    high = "high"


class ThinkingSummaries(Enum):
    auto = "auto"
    none = "none"


class ResponseModality(Enum):
    text = "text"
    image = "image"
    audio = "audio"


class Status3(Enum):
    UNSPECIFIED = "UNSPECIFIED"
    IN_PROGRESS = "IN_PROGRESS"
    REQUIRES_ACTION = "REQUIRES_ACTION"
    COMPLETED = "COMPLETED"
    FAILED = "FAILED"
    CANCELLED = "CANCELLED"
    INCOMPLETE = "INCOMPLETE"


class ModelOption(RootModel[str]):
    root: str = Field(
        ...,
        description="The model that will complete your prompt.\\n\\nSee [models](https://ai.google.dev/gemini-api/docs/models) for additional details.",
        title="Model",
    )


class AgentOption(RootModel[str]):
    root: str = Field(..., description="The agent to interact with.", title="Agent")


class ImageMimeTypeOption(RootModel[str]):
    root: str = Field(
        ..., description="The mime type of the image.", title="ImageMimeType"
    )


class AudioMimeTypeOption(RootModel[str]):
    root: str = Field(
        ..., description="The mime type of the audio.", title="AudioMimeType"
    )


class VideoMimeTypeOption(RootModel[str]):
    root: str = Field(
        ..., description="The mime type of the video.", title="VideoMimeType"
    )


class TextContent(BaseModel):
    text: Optional[str] = Field(None, description="The text content.")
    type: Literal["text"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    annotations: Optional[List[Annotation]] = Field(
        None, description="Citation information for model-generated content."
    )


class ImageContent(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[ImageMimeTypeOption] = None
    type: Literal["image"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    resolution: Optional[MediaResolution] = Field(
        None, description="The resolution of the media."
    )


class AudioContent(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[AudioMimeTypeOption] = None
    type: Literal["audio"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class VideoContent(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[VideoMimeTypeOption] = None
    type: Literal["video"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    resolution: Optional[MediaResolution] = Field(
        None, description="The resolution of the media."
    )


class ThoughtSummary1(RootModel[Union[TextContent, ImageContent]]):
    root: Union[TextContent, ImageContent] = Field(..., discriminator="type")


class ThoughtSummary(RootModel[List[ThoughtSummary1]]):
    root: List[ThoughtSummary1] = Field(..., description="A summary of the thought.")


class CodeExecutionCallContent(BaseModel):
    arguments: Optional[CodeExecutionCallArguments] = Field(
        None, description="The arguments to pass to the code execution."
    )
    type: Literal["code_execution_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class UrlContextCallContent(BaseModel):
    arguments: Optional[UrlContextCallArguments] = Field(
        None, description="The arguments to pass to the URL context."
    )
    type: Literal["url_context_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class GoogleSearchCallContent(BaseModel):
    arguments: Optional[GoogleSearchCallArguments] = Field(
        None, description="The arguments to pass to Google Search."
    )
    type: Literal["google_search_call"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    id: Optional[str] = Field(
        None, description="A unique ID for this specific tool call."
    )


class Result(BaseModel):
    items: Optional[List[Union[str, ImageContent]]] = None


class FunctionResultContent(BaseModel):
    name: Optional[str] = Field(
        None, description="The name of the tool that was called."
    )
    is_error: Optional[bool] = Field(
        None, description="Whether the tool call resulted in an error."
    )
    type: Literal["function_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    result: Union[Result, Dict[str, Any], str] = Field(
        ..., description="The result of the tool call."
    )
    call_id: str = Field(
        ..., description="ID to match the ID from the function call block."
    )


class UrlContextResultContent(BaseModel):
    signature: Optional[str] = Field(
        None, description="The signature of the URL context result."
    )
    result: Optional[List[UrlContextResult]] = Field(
        None, description="The results of the URL context."
    )
    is_error: Optional[bool] = Field(
        None, description="Whether the URL context resulted in an error."
    )
    type: Literal["url_context_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the url context call block."
    )


class GoogleSearchResultContent(BaseModel):
    signature: Optional[str] = Field(
        None, description="The signature of the Google Search result."
    )
    result: Optional[List[GoogleSearchResult]] = Field(
        None, description="The results of the Google Search."
    )
    is_error: Optional[bool] = Field(
        None, description="Whether the Google Search resulted in an error."
    )
    type: Literal["google_search_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the google search call block."
    )


class McpServerToolResultContent(BaseModel):
    name: Optional[str] = Field(
        None,
        description="Name of the tool which is called for this specific tool call.",
    )
    server_name: Optional[str] = Field(
        None, description="The name of the used MCP server."
    )
    type: Literal["mcp_server_tool_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    result: Union[Result, Dict[str, Any], str] = Field(
        ..., description="The result of the tool call."
    )
    call_id: str = Field(
        ..., description="ID to match the ID from the MCP server tool call block."
    )


class FileSearchResultContent(BaseModel):
    result: Optional[List[FileSearchResult]] = Field(
        None, description="The results of the File Search."
    )
    type: Literal["file_search_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class AllowedTools(BaseModel):
    mode: Optional[ToolChoiceType] = Field(
        None, description="The mode of the tool choice."
    )
    tools: Optional[List[str]] = Field(
        None, description="The names of the allowed tools."
    )


class DeepResearchAgentConfig(BaseModel):
    type: Literal["deep-research"] = Field(
        "deep-research",
        description="Used as the OpenAPI type discriminator for the content oneof.",
    )
    thinking_summaries: Optional[ThinkingSummaries] = Field(
        None, description="Whether to include thought summaries in the response."
    )


class McpServer(BaseModel):
    type: Literal["mcp_server"]
    name: Optional[str] = Field(None, description="The name of the MCPServer.")
    url: Optional[str] = Field(
        None,
        description='The full URL for the MCPServer endpoint.\nExample: "https://api.example.com/mcp"',
    )
    headers: Optional[Dict[str, str]] = Field(
        None,
        description="Optional: Fields for authentication headers, timeouts, etc., if needed.",
    )
    allowed_tools: Optional[List[AllowedTools]] = Field(
        None, description="The allowed tools."
    )


class ModalityTokens(BaseModel):
    modality: Optional[ResponseModality] = Field(
        None, description="The modality associated with the token count."
    )
    tokens: Optional[int] = Field(
        None, description="Number of tokens for the modality."
    )


class ImageDelta(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[ImageMimeTypeOption] = None
    type: Literal["image"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    resolution: Optional[MediaResolution] = Field(
        None, description="The resolution of the media."
    )


class AudioDelta(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[AudioMimeTypeOption] = None
    type: Literal["audio"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )


class VideoDelta(BaseModel):
    data: Optional[Base64Str] = None
    uri: Optional[str] = None
    mime_type: Optional[VideoMimeTypeOption] = None
    type: Literal["video"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    resolution: Optional[MediaResolution] = Field(
        None, description="The resolution of the media."
    )


class ThoughtSummaryDelta(BaseModel):
    type: Literal["thought_summary"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    content: Optional[Union[TextContent, ImageContent]] = Field(
        None, discriminator="type"
    )


class FunctionResultDelta(BaseModel):
    name: Optional[str] = None
    is_error: Optional[bool] = None
    type: Literal["function_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    result: Optional[Union[Result, str]] = Field(
        None, description="Tool call result delta."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the function call block."
    )


class McpServerToolResultDelta(BaseModel):
    name: Optional[str] = None
    server_name: Optional[str] = None
    type: Literal["mcp_server_tool_result"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    result: Optional[Union[Result, str]] = Field(
        None, description="Tool call result delta."
    )
    call_id: Optional[str] = Field(
        None, description="ID to match the ID from the function call block."
    )


class ErrorEvent(BaseModel):
    event_type: Literal["error"] = "error"
    error: Optional[Error] = None
    event_id: Optional[str] = Field(
        None,
        description="The event_id token to be used to resume the interaction stream, from\nthis event.",
    )


class ToolChoiceConfig(BaseModel):
    allowed_tools: Optional[AllowedTools] = None


class Tool(
    RootModel[
        Union[
            Function,
            GoogleSearch,
            CodeExecution,
            UrlContext,
            ComputerUse,
            McpServer,
            FileSearch,
        ]
    ]
):
    root: Union[
        Function,
        GoogleSearch,
        CodeExecution,
        UrlContext,
        ComputerUse,
        McpServer,
        FileSearch,
    ] = Field(..., discriminator="type")


class ThoughtContent(BaseModel):
    signature: Optional[Base64Str] = Field(
        None,
        description="Signature to match the backend source to be part of the generation.",
    )
    type: Literal["thought"] = Field(
        ..., description="Used as the OpenAPI type discriminator for the content oneof."
    )
    summary: Optional[ThoughtSummary] = Field(
        None, description="A summary of the thought."
    )


class ToolChoice(RootModel[Union[ToolChoiceType, ToolChoiceConfig]]):
    root: Union[ToolChoiceType, ToolChoiceConfig] = Field(
        ..., description="The configuration for tool choice."
    )


class Usage(BaseModel):
    total_input_tokens: Optional[int] = Field(
        None, description="Number of tokens in the prompt (context)."
    )
    input_tokens_by_modality: Optional[List[ModalityTokens]] = Field(
        None, description="A breakdown of input token usage by modality."
    )
    total_cached_tokens: Optional[int] = Field(
        None,
        description="Number of tokens in the cached part of the prompt (the cached content).",
    )
    cached_tokens_by_modality: Optional[List[ModalityTokens]] = Field(
        None, description="A breakdown of cached token usage by modality."
    )
    total_output_tokens: Optional[int] = Field(
        None, description="Total number of tokens across all the generated responses."
    )
    output_tokens_by_modality: Optional[List[ModalityTokens]] = Field(
        None, description="A breakdown of output token usage by modality."
    )
    total_tool_use_tokens: Optional[int] = Field(
        None, description="Number of tokens present in tool-use prompt(s)."
    )
    tool_use_tokens_by_modality: Optional[List[ModalityTokens]] = Field(
        None, description="A breakdown of tool-use token usage by modality."
    )
    total_reasoning_tokens: Optional[int] = Field(
        None, description="Number of tokens of thoughts for thinking models."
    )
    total_tokens: Optional[int] = Field(
        None,
        description="Total token count for the interaction request (prompt + responses + other\ninternal tokens).",
    )


class ContentDelta(BaseModel):
    index: Optional[int] = None
    event_type: Literal["content.delta"] = "content.delta"
    event_id: Optional[str] = Field(
        None,
        description="The event_id token to be used to resume the interaction stream, from\nthis event.",
    )
    delta: Optional[
        Union[
            TextDelta,
            ImageDelta,
            AudioDelta,
            DocumentDelta,
            VideoDelta,
            ThoughtSummaryDelta,
            ThoughtSignatureDelta,
            FunctionCallDelta,
            FunctionResultDelta,
            CodeExecutionCallDelta,
            CodeExecutionResultDelta,
            UrlContextCallDelta,
            UrlContextResultDelta,
            GoogleSearchCallDelta,
            GoogleSearchResultDelta,
            McpServerToolCallDelta,
            McpServerToolResultDelta,
            FileSearchResultDelta,
        ]
    ] = Field(None, discriminator="type")


class Content(
    RootModel[
        Union[
            TextContent,
            ImageContent,
            AudioContent,
            DocumentContent,
            VideoContent,
            ThoughtContent,
            FunctionCallContent,
            FunctionResultContent,
            CodeExecutionCallContent,
            CodeExecutionResultContent,
            UrlContextCallContent,
            UrlContextResultContent,
            GoogleSearchCallContent,
            GoogleSearchResultContent,
            McpServerToolCallContent,
            McpServerToolResultContent,
            FileSearchResultContent,
        ]
    ]
):
    root: Union[
        TextContent,
        ImageContent,
        AudioContent,
        DocumentContent,
        VideoContent,
        ThoughtContent,
        FunctionCallContent,
        FunctionResultContent,
        CodeExecutionCallContent,
        CodeExecutionResultContent,
        UrlContextCallContent,
        UrlContextResultContent,
        GoogleSearchCallContent,
        GoogleSearchResultContent,
        McpServerToolCallContent,
        McpServerToolResultContent,
        FileSearchResultContent,
    ] = Field(..., description="The content of the response.", discriminator="type")


class Turn(BaseModel):
    role: Optional[str] = Field(
        None,
        description="The originator of this turn. Must be user for input or model for\nmodel output.",
    )
    content: Optional[Union[str, List[Content]]] = Field(
        None, description="The content of the turn."
    )


class GenerationConfig(BaseModel):
    temperature: Optional[float] = Field(
        None, description="Controls the randomness of the output."
    )
    top_p: Optional[float] = Field(
        None,
        description="The maximum cumulative probability of tokens to consider when sampling.",
    )
    seed: Optional[int] = Field(
        None, description="Seed used in decoding for reproducibility."
    )
    stop_sequences: Optional[List[str]] = Field(
        None,
        description="A list of character sequences that will stop output interaction.",
    )
    tool_choice: Optional[ToolChoice] = Field(
        None, description="The tool choice for the interaction."
    )
    thinking_level: Optional[ThinkingLevel] = Field(
        None, description="The level of thought tokens that the model should generate."
    )
    thinking_summaries: Optional[ThinkingSummaries] = Field(
        None, description="Whether to include thought summaries in the response."
    )
    max_output_tokens: Optional[int] = Field(
        None, description="The maximum number of tokens to include in the response."
    )
    speech_config: Optional[List[SpeechConfig]] = Field(
        None, description="Configuration for speech interaction."
    )


class ContentStart(BaseModel):
    index: Optional[int] = None
    content: Optional[Content] = None
    event_type: Literal["content.start"] = "content.start"
    event_id: Optional[str] = Field(
        None,
        description="The event_id token to be used to resume the interaction stream, from\nthis event.",
    )


class Interaction(BaseModel):
    model: Optional[ModelOption] = Field(
        None, description="The name of the `Model` used for generating the interaction."
    )
    agent: Optional[AgentOption] = Field(
        None, description="The name of the `Agent` used for generating the interaction."
    )
    id: str = Field(
        ...,
        description="Output only. A unique identifier for the interaction completion.",
    )
    status: Status1 = Field(
        ..., description="Output only. The status of the interaction."
    )
    created: Optional[AwareDatetime] = Field(
        None,
        description="Output only. The time at which the response was created in ISO 8601 format\n(YYYY-MM-DDThh:mm:ssZ).",
    )
    updated: Optional[AwareDatetime] = Field(
        None,
        description="Output only. The time at which the response was last updated in ISO 8601 format\n(YYYY-MM-DDThh:mm:ssZ).",
    )
    role: Optional[str] = Field(
        None, description="Output only. The role of the interaction."
    )
    outputs: Optional[List[Content]] = Field(
        None, description="Output only. Responses from the model."
    )
    system_instruction: Optional[str] = Field(
        None, description="System instruction for the interaction."
    )
    tools: Optional[List[Tool]] = Field(
        None,
        description="A list of tool declarations the model may call during interaction.",
    )
    background: Optional[bool] = Field(
        None, description="Whether to run the model interaction in the background."
    )
    object: Literal["interaction"] = Field(
        "interaction",
        description="Output only. The object type of the interaction. Always set to `interaction`.",
    )
    usage: Optional[Usage] = Field(
        None,
        description="Output only. Statistics on the interaction request's token usage.",
    )
    response_modalities: Optional[List[ResponseModality]] = Field(
        None,
        description="The requested modalities of the response (TEXT, IMAGE, AUDIO).",
    )
    response_format: Optional[Any] = Field(
        None,
        description="Enforces that the generated response is a JSON object that complies with\nthe JSON schema specified in this field.",
    )
    response_mime_type: Optional[str] = Field(
        None,
        description="The mime type of the response. This is required if response_format is set.",
    )
    previous_interaction_id: Optional[str] = Field(
        None, description="The ID of the previous interaction, if any."
    )
    input: Optional[Union[str, List[Content], List[Turn], Content]] = Field(
        None, description="The inputs for the interaction."
    )
    generation_config: Optional[GenerationConfig] = Field(
        None,
        description="Input only. Configuration parameters for the model interaction.",
    )
    agent_config: Optional[Union[DynamicAgentConfig, DeepResearchAgentConfig]] = Field(
        None, description="Configuration for the agent.", discriminator="type"
    )


class CreateModelInteractionParams(BaseModel):
    model: ModelOption = Field(
        ..., description="The name of the `Model` used for generating the interaction."
    )
    stream: Optional[bool] = Field(
        None, description="Input only. Whether the interaction will be streamed."
    )
    store: Optional[bool] = Field(
        None,
        description="Input only. Whether to store the response and request for later retrieval.",
    )
    id: Optional[str] = Field(
        None,
        description="Output only. A unique identifier for the interaction completion.",
    )
    status: Optional[Status3] = Field(
        None, description="Output only. The status of the interaction."
    )
    created: Optional[AwareDatetime] = Field(
        None,
        description="Output only. The time at which the response was created in ISO 8601 format\n(YYYY-MM-DDThh:mm:ssZ).",
    )
    updated: Optional[AwareDatetime] = Field(
        None,
        description="Output only. The time at which the response was last updated in ISO 8601 format\n(YYYY-MM-DDThh:mm:ssZ).",
    )
    role: Optional[str] = Field(
        None, description="Output only. The role of the interaction."
    )
    outputs: Optional[List[Content]] = Field(
        None, description="Output only. Responses from the model."
    )
    system_instruction: Optional[str] = Field(
        None, description="System instruction for the interaction."
    )
    tools: Optional[List[Tool]] = Field(
        None,
        description="A list of tool declarations the model may call during interaction.",
    )
    background: Optional[bool] = Field(
        None, description="Whether to run the model interaction in the background."
    )
    usage: Optional[Usage] = Field(
        None,
        description="Output only. Statistics on the interaction request's token usage.",
    )
    response_modalities: Optional[List[ResponseModality]] = Field(
        None,
        description="The requested modalities of the response (TEXT, IMAGE, AUDIO).",
    )
    response_format: Optional[Any] = Field(
        None,
        description="Enforces that the generated response is a JSON object that complies with\nthe JSON schema specified in this field.",
    )
    response_mime_type: Optional[str] = Field(
        None,
        description="The mime type of the response. This is required if response_format is set.",
    )
    previous_interaction_id: Optional[str] = Field(
        None, description="The ID of the previous interaction, if any."
    )
    input: Union[str, List[Content], List[Turn], Content] = Field(
        ..., description="The inputs for the interaction."
    )
    generation_config: Optional[GenerationConfig] = Field(
        None,
        description="Input only. Configuration parameters for the model interaction.",
    )


class CreateAgentInteractionParams(BaseModel):
    agent: AgentOption = Field(
        ..., description="The name of the `Agent` used for generating the interaction."
    )
    stream: Optional[bool] = Field(
        None, description="Input only. Whether the interaction will be streamed."
    )
    store: Optional[bool] = Field(
        None,
        description="Input only. Whether to store the response and request for later retrieval.",
    )
    id: Optional[str] = Field(
        None,
        description="Output only. A unique identifier for the interaction completion.",
    )
    status: Optional[Status3] = Field(
        None, description="Output only. The status of the interaction."
    )
    created: Optional[AwareDatetime] = Field(
        None,
        description="Output only. The time at which the response was created in ISO 8601 format\n(YYYY-MM-DDThh:mm:ssZ).",
    )
    updated: Optional[AwareDatetime] = Field(
        None,
        description="Output only. The time at which the response was last updated in ISO 8601 format\n(YYYY-MM-DDThh:mm:ssZ).",
    )
    role: Optional[str] = Field(
        None, description="Output only. The role of the interaction."
    )
    outputs: Optional[List[Content]] = Field(
        None, description="Output only. Responses from the model."
    )
    system_instruction: Optional[str] = Field(
        None, description="System instruction for the interaction."
    )
    tools: Optional[List[Tool]] = Field(
        None,
        description="A list of tool declarations the model may call during interaction.",
    )
    background: Optional[bool] = Field(
        None, description="Whether to run the model interaction in the background."
    )
    usage: Optional[Usage] = Field(
        None,
        description="Output only. Statistics on the interaction request's token usage.",
    )
    response_modalities: Optional[List[ResponseModality]] = Field(
        None,
        description="The requested modalities of the response (TEXT, IMAGE, AUDIO).",
    )
    response_format: Optional[Any] = Field(
        None,
        description="Enforces that the generated response is a JSON object that complies with\nthe JSON schema specified in this field.",
    )
    response_mime_type: Optional[str] = Field(
        None,
        description="The mime type of the response. This is required if response_format is set.",
    )
    previous_interaction_id: Optional[str] = Field(
        None, description="The ID of the previous interaction, if any."
    )
    input: Union[str, List[Content], List[Turn], Content] = Field(
        ..., description="The inputs for the interaction."
    )
    agent_config: Optional[Union[DynamicAgentConfig, DeepResearchAgentConfig]] = Field(
        None, description="Configuration for the agent.", discriminator="type"
    )


class InteractionEvent(BaseModel):
    event_type: Literal["interaction.start", "interaction.complete"]
    interaction: Optional[Interaction] = None
    event_id: Optional[str] = Field(
        None,
        description="The event_id token to be used to resume the interaction stream, from\nthis event.",
    )


class InteractionSseEvent(
    RootModel[
        Union[
            InteractionEvent,
            InteractionStatusUpdate,
            ContentStart,
            ContentDelta,
            ContentStop,
            ErrorEvent,
        ]
    ]
):
    root: Union[
        InteractionEvent,
        InteractionStatusUpdate,
        ContentStart,
        ContentDelta,
        ContentStop,
        ErrorEvent,
    ] = Field(..., discriminator="event_type")


# ============================================================
# LiteLLM-specific types (added manually after generation)
# ============================================================
#
# When regenerating this file, copy these types to the end.
# See README.md for regeneration instructions.

from pydantic import PrivateAttr

from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject

# Type alias for input
InteractionInput = Union[str, Content, List[Content], List[Turn]]


class InteractionsAPIResponse(BaseLiteLLMOpenAIResponseObject):
    """
    Response from the Interactions API.

    Wraps the API response with LiteLLM-specific hidden params.
    """

    id: Optional[str] = None
    object: Optional[str] = "interaction"
    model: Optional[str] = None
    agent: Optional[str] = None
    status: Optional[str] = None
    created: Optional[str] = None
    updated: Optional[str] = None
    role: Optional[str] = None
    outputs: Optional[List[Dict[str, Any]]] = None
    usage: Optional[Dict[str, Any]] = None

    _hidden_params: dict = PrivateAttr(default_factory=dict)


class InteractionsAPIStreamingResponse(BaseLiteLLMOpenAIResponseObject):
    """
    Streaming response chunk from the Interactions API.

    Event types per OpenAPI spec:
    - interaction.start, interaction.status_update, interaction.complete
    - content.start, content.delta, content.stop
    - error
    """

    event_type: Optional[str] = None
    id: Optional[str] = None
    object: Optional[str] = "interaction"
    model: Optional[str] = None
    agent: Optional[str] = None
    status: Optional[str] = None
    created: Optional[str] = None
    updated: Optional[str] = None
    role: Optional[str] = None
    outputs: Optional[List[Dict[str, Any]]] = None
    usage: Optional[Dict[str, Any]] = None
    delta: Optional[Dict[str, Any]] = None

    _hidden_params: dict = PrivateAttr(default_factory=dict)


class DeleteInteractionResult(BaseLiteLLMOpenAIResponseObject):
    """Result of deleting an interaction."""

    success: bool = True
    id: Optional[str] = None

    _hidden_params: dict = PrivateAttr(default_factory=dict)


class CancelInteractionResult(BaseLiteLLMOpenAIResponseObject):
    """Result of cancelling an interaction."""

    id: Optional[str] = None
    status: Optional[str] = None

    _hidden_params: dict = PrivateAttr(default_factory=dict)


# Backwards compatibility aliases
InteractionTool = Tool
InteractionToolChoiceConfig = ToolChoiceConfig
InteractionsAPIOptionalRequestParams = Dict[str, Any]