1e74ae3cd6
- 新增 OpenAI-compatible LLM client，支持 base_url、api_key、model 配置 - 固化意图识别、参数抽取、部署计划生成的结构化 JSON 提示词 - 增加 MCP client 配置读取和真实 session 接入说明 - 实现 checkpoint 自动保存、resume 断点续跑和已完成步骤跳过 - 实现人工确认流程，支持失败 IP 回滚 approve/reject - 新增 chat 常驻式 CLI 对话框，支持自然语言分析、参数设置、执行确认、状态查看、回滚确认和续跑 - 同步 README，补充 LLM、MCP、checkpoint、confirm/resume、chat 使用方式 - 增加相关单元测试，覆盖 LLM client、MCP 配置、确认/续跑和交互式 CLI
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
"""Shared dataclasses for the PAM deploy agent."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Literal
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BackendName = Literal["mcp", "script", "fake"]
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ExecutionStrategy = Literal["hybrid_node_mcp", "script_only", "fake"]
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IntentName = Literal["deploy", "show_usage", "preview", "query_node_ips", "rollback"]
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ModePreference = Literal["MCP", "API脚本", "未指定"]
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StrategyPreference = Literal["hybrid_node_mcp", "script_only", "fake", "未指定"]
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@dataclass(slots=True)
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class ActionResult:
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action: str
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backend: BackendName
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ok: bool
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values: dict[str, Any] = field(default_factory=dict)
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exit_code: int = 0
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tool_name: str = ""
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stdout: str = ""
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stderr: str = ""
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raw_output: str = ""
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error_summary: str = ""
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@dataclass(slots=True)
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class SkillPolicy:
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name: str
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source_path: str
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description: str = ""
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allowed_modes: tuple[str, ...] = ("MCP", "API脚本")
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allowed_actions: tuple[str, ...] = ()
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required_confirmations: tuple[str, ...] = (
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"params",
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"target_scope",
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"rollback",
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)
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required_params: tuple[str, ...] = ()
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optional_params: dict[str, Any] = field(default_factory=dict)
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action_sequence: tuple[str, ...] = ()
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ip_action_sequence: tuple[str, ...] = ()
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forbidden_actions: tuple[str, ...] = (
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"script-main-flow",
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"auto-rollback",
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"modify-deploy-scripts",
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)
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@dataclass(slots=True)
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class LlmIntentResult:
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intent: IntentName
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mode_preference: ModePreference = "未指定"
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strategy_preference: StrategyPreference = "未指定"
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confidence: float = 0.0
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reasons: list[str] = field(default_factory=list)
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needs_clarification: bool = False
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clarification_questions: list[str] = field(default_factory=list)
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@dataclass(slots=True)
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class LlmParamResult:
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extracted_params: dict[str, Any] = field(default_factory=dict)
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extracted_control: dict[str, Any] = field(default_factory=dict)
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missing_required_params: list[str] = field(default_factory=list)
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ambiguous_fields: list[str] = field(default_factory=list)
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sensitive_fields_present: list[str] = field(default_factory=list)
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@dataclass(slots=True)
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class LlmDeployPlan:
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summary: str
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risk_notes: list[str] = field(default_factory=list)
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planned_actions: list[str] = field(default_factory=list)
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requires_confirmation: bool = True
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execution_strategy: StrategyPreference = "未指定"
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@dataclass(slots=True)
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class AgentState:
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run_id: str
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params: dict[str, Any]
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execution_strategy: ExecutionStrategy
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action_backends: dict[str, BackendName]
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script_entry: str = ""
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script_base_dir: str = "."
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config_path: str = ""
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trace_file_path: str = ""
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node_mcp_server_name: str = ""
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node_mcp_tool_names: dict[str, str] = field(default_factory=dict)
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completed_global_steps: list[str] = field(default_factory=list)
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hash_code: str = ""
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node_url: str = ""
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online_ips: list[str] = field(default_factory=list)
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target_ips: list[str] = field(default_factory=list)
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ip_states: dict[str, dict[str, Any]] = field(default_factory=dict)
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pending_confirmation: str = ""
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last_success_step: str = ""
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last_failed_step: str = ""
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checkpoint_path: str = ""
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events: list[dict[str, Any]] = field(default_factory=list)
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