e572a26e6f
llm 提示词和规则：新增 progress_complete 判断字段。 deploy.sh / deploy.ps1：poll-* action 入口改为单次查询。 interactive.py：chat 会播报进度更新。 config.txt.example / README / packaging 文档 / Skill 文档：同步进度查询参数和新 workflow 语义。 测试补充了进度重复查询、超时暂停、chat 进度播报。
136 lines
4.2 KiB
Python
136 lines
4.2 KiB
Python
"""PAM 部署 Agent 共享数据模型。"""
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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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ActionAnalysisSeverity = Literal["info", "low", "medium", "high"]
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@dataclass(slots=True)
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class ActionResult:
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"""单个 action 的统一执行结果。"""
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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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"""从 Skill 文档提取出的部署策略约束。"""
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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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"""LLM 意图识别结果。"""
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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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"""LLM 参数抽取结果。"""
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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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"""LLM 生成的部署计划。"""
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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 LlmActionAnalysis:
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"""LLM 或规则对单次 action 结果的诊断建议。"""
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action: str
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has_anomaly: bool = False
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severity: ActionAnalysisSeverity = "info"
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possible_reason: str = ""
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suggested_action: str = ""
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requires_confirmation: bool = False
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should_continue: bool = True
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progress_complete: bool | None = None
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notes: list[str] = field(default_factory=list)
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@dataclass(slots=True)
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class AgentState:
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"""一次部署运行的完整状态，可序列化到 checkpoint。"""
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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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paused: bool = False
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pause_reason: str = ""
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review_context: dict[str, Any] = field(default_factory=dict)
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events: list[dict[str, Any]] = field(default_factory=list)
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poll_attempts: dict[str, int] = field(default_factory=dict)
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