315 lines
8.9 KiB
Python
315 lines
8.9 KiB
Python
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"""Pydantic contract for the supported Responses API surface."""
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from __future__ import annotations
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from typing import Annotated, Any, Literal
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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ItemStatus = Literal["in_progress", "completed", "incomplete"]
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ResponseStatus = Literal[
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"in_progress",
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"completed",
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"failed",
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"cancelled",
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"incomplete",
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]
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class _StrictModel(BaseModel):
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model_config = ConfigDict(extra="forbid")
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class InputText(_StrictModel):
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type: Literal["input_text"] = "input_text"
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text: str
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class OutputTextInput(_StrictModel):
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type: Literal["output_text"] = "output_text"
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text: str
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annotations: list[dict[str, Any]] = Field(default_factory=list)
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MessageContent = Annotated[
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InputText | OutputTextInput,
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Field(discriminator="type"),
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]
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class ResponseInputMessage(_StrictModel):
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type: Literal["message"] = "message"
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role: Literal["user", "assistant"] = "user"
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content: str | list[MessageContent]
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class ResponseFunctionCallOutputInput(_StrictModel):
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type: Literal["function_call_output"] = "function_call_output"
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call_id: str
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output: Any
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ResponseInputItem = Annotated[
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ResponseInputMessage | ResponseFunctionCallOutputInput,
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Field(discriminator="type"),
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]
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class InteractiveMediaRequestExtension(_StrictModel):
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version: Literal["1"] | None = Field(
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default=None,
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description="Interactive Media extension version. Omit or use `1`.",
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)
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variables: dict[str, Any] = Field(default_factory=dict)
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class ResponseCreateRequest(BaseModel):
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"""Supported request fields; extras are preserved for semantic rejection."""
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model_config = ConfigDict(
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extra="allow",
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json_schema_extra={
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"examples": [
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{
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"model": "assistant:asst_xxx",
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"input": "请介绍一下这个产品",
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"store": True,
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},
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{
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"previous_response_id": "resp_xxx",
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"input": "继续说明",
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"stream": True,
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},
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]
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},
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)
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model: str | None = Field(
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default=None,
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examples=["assistant:asst_xxx"],
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description="Required on the first turn; inherited on later turns.",
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)
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previous_response_id: str | None = None
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input: str | list[ResponseInputItem]
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store: bool | None = Field(
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default=None,
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description="Stateful subset: omit this field or set it to true.",
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)
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stream: bool = False
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x_interactive_media: InteractiveMediaRequestExtension | None = None
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@model_validator(mode="before")
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@classmethod
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def default_message_discriminator(cls, value: Any) -> Any:
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"""Accept the official SDK's easy input message with omitted `type`."""
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if not isinstance(value, dict) or not isinstance(value.get("input"), list):
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return value
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copied = dict(value)
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copied["input"] = [
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({"type": "message", **item} if isinstance(item, dict) and "type" not in item else item)
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for item in value["input"]
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]
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return copied
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class ResponseOutputText(_StrictModel):
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type: Literal["output_text"] = "output_text"
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text: str
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annotations: list[dict[str, Any]] = Field(default_factory=list)
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logprobs: list[dict[str, Any]] | None = None
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class ResponseOutputMessage(_StrictModel):
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id: str
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type: Literal["message"] = "message"
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status: ItemStatus
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role: Literal["assistant"] = "assistant"
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content: list[ResponseOutputText]
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class ResponseFunctionCall(_StrictModel):
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id: str
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type: Literal["function_call"] = "function_call"
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status: ItemStatus
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call_id: str
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name: str
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arguments: str
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ResponseOutputItem = Annotated[
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ResponseOutputMessage | ResponseFunctionCall,
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Field(discriminator="type"),
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]
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class ResponseError(_StrictModel):
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code: str
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message: str
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class ResponseUsage(_StrictModel):
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input_tokens: int
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output_tokens: int
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total_tokens: int
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input_tokens_details: dict[str, Any] = Field(default_factory=dict)
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output_tokens_details: dict[str, Any] = Field(default_factory=dict)
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class ResponseObject(_StrictModel):
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model_config = ConfigDict(
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extra="forbid",
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json_schema_extra={
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"examples": [
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{
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"id": "resp_xxx",
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"object": "response",
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"created_at": 1788739200,
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"completed_at": 1788739201,
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"status": "completed",
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"error": None,
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"incomplete_details": None,
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"instructions": None,
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"model": "assistant:asst_xxx",
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"output": [
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{
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"id": "msg_xxx",
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"type": "message",
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"status": "completed",
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"role": "assistant",
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"content": [
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{
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"type": "output_text",
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"text": "这是一个实时对话助手。",
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"annotations": [],
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}
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],
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}
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],
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"parallel_tool_calls": False,
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"previous_response_id": None,
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"store": True,
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"tool_choice": "auto",
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"tools": [],
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"metadata": {},
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"usage": None,
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}
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]
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},
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)
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id: str
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object: Literal["response"] = "response"
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created_at: int
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completed_at: int | None = None
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status: ResponseStatus
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error: ResponseError | None = None
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incomplete_details: dict[str, Any] | None = None
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instructions: None = None
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model: str | None
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output: list[ResponseOutputItem]
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parallel_tool_calls: Literal[False] = False
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previous_response_id: str | None = None
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store: Literal[True] = True
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tool_choice: Literal["auto"] = "auto"
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tools: list[Any] = Field(default_factory=list)
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metadata: dict[str, str] = Field(default_factory=dict)
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usage: ResponseUsage | None = None
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class ResponseInputMessageItem(_StrictModel):
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id: str
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type: Literal["message"] = "message"
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role: Literal["user", "assistant"]
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content: list[MessageContent]
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status: ItemStatus = "completed"
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class ResponseFunctionCallOutputItem(_StrictModel):
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id: str
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type: Literal["function_call_output"] = "function_call_output"
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call_id: str
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output: str
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status: ItemStatus = "completed"
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ResponseStoredInputItem = Annotated[
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ResponseInputMessageItem | ResponseFunctionCallOutputItem,
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Field(discriminator="type"),
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]
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class ResponseInputItemList(_StrictModel):
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model_config = ConfigDict(
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extra="forbid",
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json_schema_extra={
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"examples": [
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{
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"object": "list",
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"data": [
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{
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"id": "msg_input_xxx",
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"type": "message",
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"role": "user",
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"content": [
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{"type": "input_text", "text": "你好"}
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],
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"status": "completed",
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}
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],
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"first_id": "msg_input_xxx",
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"last_id": "msg_input_xxx",
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"has_more": False,
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}
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]
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},
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)
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object: Literal["list"] = "list"
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data: list[ResponseStoredInputItem]
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first_id: str | None = None
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last_id: str | None = None
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has_more: bool
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class ModelObject(_StrictModel):
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id: str
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object: Literal["model"] = "model"
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created: int
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owned_by: str = "interactive-media"
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name: str
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capabilities: list[Literal["responses", "realtime"]]
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assistant_id: str
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assistant_type: str
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runtime_mode: str
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class ModelList(_StrictModel):
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model_config = ConfigDict(
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extra="forbid",
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json_schema_extra={
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"examples": [
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{
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"object": "list",
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"data": [
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{
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"id": "assistant:asst_xxx",
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"object": "model",
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"created": 1788739200,
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"owned_by": "interactive-media",
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"name": "产品顾问",
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"capabilities": ["responses", "realtime"],
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"assistant_id": "asst_xxx",
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"assistant_type": "prompt",
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"runtime_mode": "pipeline",
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}
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],
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}
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]
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},
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)
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object: Literal["list"] = "list"
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data: list[ModelObject]
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