steven_li
30ab74ffb2
- 集成MCP连接管理器，支持MCP服务器连接 - 添加多种内置工具：ClarifyTool、CronTool、DelegateTool、ExecuteCodeTool、 PatchFileTool、ProcessTool、SendMessageTool、SpawnTool、TerminalTool、 TodoTool、WebFetchTool、WebSearchTool、WriteFileTool等 - 实现工具注册和装配功能 - 添加技能选择上下文参数 - 支持思考模式控制参数thinking_enabled feat(coordinator): 重构任务执行计划器参数命名 - 将learning_candidate_enabled重命名为allow_candidate_generation - 更新TeamGraphScheduler中的参数传递 - 修改LocalAgentRunner中的相关参数处理 - 更新README文档中的相应描述 refactor(context): 标准化工具调用参数格式 - 添加_json导入用于参数序列化 - 实现_provider_tool_calls方法标准化OpenAI兼容的工具调用载荷 - 修复工具调用中参数非字符串类型的序列化问题 refactor(session): 优化消息历史记录过滤逻辑 - 修改get_messages_as_conversation为基于运行状态过滤消息 - 排除未完成、失败或错误结束的运行记录 - 改进对话历史的可见性控制机制 fix(store): 修复FTS索引重建逻辑 - 添加异常处理防止FTS索引创建失败 - 实现_rebuild_fts_index方法重新构建全文搜索索引 - 优化索引触发器和表的维护流程
138 lines
3.8 KiB
Python
138 lines
3.8 KiB
Python
"""Chat-related web schemas."""
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from __future__ import annotations
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from typing import Any
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try:
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from pydantic import BaseModel, Field
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except ModuleNotFoundError: # pragma: no cover - fallback for skeleton-only environments
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class BaseModel:
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"""Very small fallback shim used only so imports work without pydantic."""
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def __init__(self, **kwargs: Any) -> None:
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annotations = getattr(self.__class__, "__annotations__", {})
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for name in annotations:
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default = getattr(self.__class__, name, None)
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if name in kwargs:
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value = kwargs[name]
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else:
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value = default
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setattr(self, name, value)
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def model_dump(self, *, exclude_none: bool = False) -> dict[str, Any]:
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data = dict(self.__dict__)
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if exclude_none:
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data = {key: value for key, value in data.items() if value is not None}
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return data
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def Field(default: Any = None, **kwargs: Any) -> Any:
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default_factory = kwargs.get("default_factory")
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if default_factory is not None:
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return default_factory()
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return default
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class WebProviderTarget(BaseModel):
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"""Web-facing provider target shape.
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先保持和 runtime 里的 `ProviderTarget` 接近，但只暴露 Web 当前需要的字段。
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后面如果 provider 层扩字段，再由这里显式补齐。
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"""
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provider: str | None = None
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model: str | None = None
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api_key: str | None = None
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api_base: str | None = None
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extra_headers: dict[str, str] | None = None
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class WebChatRequest(BaseModel):
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"""最小正式 chat 请求结构。"""
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message: str = Field(min_length=1)
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session_id: str | None = None
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user_id: str | None = None
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title: str | None = None
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execution_context: str | None = None
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model: str | None = None
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provider_name: str | None = None
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embedding_model: str | None = None
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temperature: float | None = None
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max_tokens: int | None = None
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thinking_enabled: bool | None = None
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max_tool_iterations: int | None = None
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fallback_target: WebProviderTarget | None = None
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auxiliary_target: WebProviderTarget | None = None
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embedding_target: WebProviderTarget | None = None
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reply_to_scheduled_run_id: str | None = None
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scheduled_reply_intent: str | None = None
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class WebChatResponse(BaseModel):
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"""最小正式 chat 响应结构。"""
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session_id: str
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run_id: str
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output_text: str
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finish_reason: str
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tool_iterations: int
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provider_name: str | None = None
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model: str | None = None
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usage: dict[str, Any] = Field(default_factory=dict)
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task_id: str | None = None
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task_status: str | None = None
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validation_result: dict[str, Any] | None = None
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class WebChatFeedbackRequest(BaseModel):
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"""Feedback on the latest assistant result in chat."""
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session_id: str
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run_id: str
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feedback_type: str
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comment: str | None = None
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class WebChatFeedbackResponse(BaseModel):
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"""Feedback recording result."""
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session_id: str
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run_id: str
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task_id: str
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task_status: str
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feedback_type: str
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learning_candidates: list[dict[str, Any]] = Field(default_factory=list)
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class WebProviderConfigRequest(BaseModel):
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"""Provider config update from the status page."""
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enabled: bool = True
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model: str | None = None
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api_key: str | None = None
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api_base: str | None = None
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request_timeout_seconds: float | None = None
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class WebProviderConfigResponse(BaseModel):
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"""Provider config update result."""
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ok: bool
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provider: str
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enabled: bool
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class WebStatusResponse(BaseModel):
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"""Web 宿主层状态响应。"""
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status: str
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running: bool
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mode: str
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class WebErrorResponse(BaseModel):
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"""统一错误响应结构。"""
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detail: str
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