agent_deply/pam_deploy_graph/tool_catalog.py

"""统一维护面向 LLM 和 runtime 的 PAM action tool schema。"""

from __future__ import annotations

from pam_deploy_graph.action_router import build_action_backends
from pam_deploy_graph.constants import GLOBAL_ACTION_SEQUENCE, IP_ACTION_SEQUENCE
from pam_deploy_graph.models import AgentExecutionMode, ExecutionStrategy
from pam_deploy_graph.models import ActionToolSpec, SkillPolicy


ACTION_TOOL_SPECS: dict[str, ActionToolSpec] = {
    "get-token": ActionToolSpec(
        name="get_token",
        action="get-token",
        scope="global",
        description="获取 PAM HOME OAuth token。",
        risk_level="low",
        required_param_fields=("HOME_BASE_URL", "CLIENT_ID", "CLIENT_SECRET"),
        preferred_backend="script",
    ),
    "create-version": ActionToolSpec(
        name="create_version",
        action="create-version",
        scope="global",
        description="创建版本记录。",
        risk_level="medium",
        preferred_backend="script",
    ),
    "upload-package": ActionToolSpec(
        name="upload_package",
        action="upload-package",
        scope="global",
        description="上传软件包并返回 HASH_CODE。",
        risk_level="high",
        preferred_backend="script",
    ),
    "publish-version": ActionToolSpec(
        name="publish_version",
        action="publish-version",
        scope="global",
        description="发布版本，需要已有 HASH_CODE。",
        risk_level="high",
        requires_confirmation=True,
        required_runtime_fields=("hash_code",),
        preferred_backend="script",
    ),
    "get-node-url": ActionToolSpec(
        name="get_node_url",
        action="get-node-url",
        scope="global",
        description="获取目标 PAM NODE 地址。",
        risk_level="low",
        preferred_backend="script",
    ),
    "get-online-ips": ActionToolSpec(
        name="get_online_ips",
        action="get-online-ips",
        scope="global",
        description="获取当前在线工作站 IP 列表。",
        risk_level="low",
    ),
    "create-download-task": ActionToolSpec(
        name="create_download_task",
        action="create-download-task",
        scope="global",
        description="创建云下载任务。",
        risk_level="high",
    ),
    "poll-download-progress": ActionToolSpec(
        name="poll_download_progress",
        action="poll-download-progress",
        scope="global",
        description="单次查询云下载任务进度；是否继续查询由 Agent workflow 和 LLM 审核决定。",
        risk_level="medium",
    ),
    "upgrade-ip": ActionToolSpec(
        name="upgrade_ip",
        action="upgrade-ip",
        scope="ip",
        description="对单个工作站创建升级任务。",
        risk_level="high",
        requires_confirmation=True,
    ),
    "poll-upgrade-progress": ActionToolSpec(
        name="poll_upgrade_progress",
        action="poll-upgrade-progress",
        scope="ip",
        description="单次查询单个工作站升级进度；是否继续查询由 Agent workflow 和 LLM 审核决定。",
        risk_level="medium",
    ),
    "start-ip": ActionToolSpec(
        name="start_ip",
        action="start-ip",
        scope="ip",
        description="启动单个工作站应用。",
        risk_level="high",
        requires_confirmation=True,
    ),
    "stop-ip": ActionToolSpec(
        name="stop_ip",
        action="stop-ip",
        scope="ip",
        description="停止单个工作站应用。",
        risk_level="high",
        requires_confirmation=True,
    ),
    "verify-ip": ActionToolSpec(
        name="verify_ip",
        action="verify-ip",
        scope="ip",
        description="校验单个工作站版本和健康状态。",
        risk_level="medium",
    ),
    "download-log": ActionToolSpec(
        name="download_log",
        action="download-log",
        scope="ip",
        description="下载单个工作站日志。",
        risk_level="low",
    ),
    "rollback-ip": ActionToolSpec(
        name="rollback_ip",
        action="rollback-ip",
        scope="ip",
        description="对单个工作站执行回滚。",
        risk_level="high",
        requires_confirmation=True,
    ),
}


def ordered_actions_for_skill(policy: SkillPolicy) -> list[str]:
    """根据 skill 策略返回默认 action 顺序。"""
    global_actions = list(policy.action_sequence or GLOBAL_ACTION_SEQUENCE)
    ip_actions = list(policy.ip_action_sequence or IP_ACTION_SEQUENCE)
    return [*global_actions, *ip_actions]


ACTION_DEPENDENCIES: dict[str, tuple[str, ...]] = {
    "create-version": ("get-token",),
    "upload-package": ("get-token", "create-version"),
    "publish-version": ("get-token", "create-version", "upload-package"),
    "get-node-url": ("get-token",),
    "get-online-ips": ("get-token", "get-node-url"),
    "create-download-task": ("get-token", "get-node-url", "get-online-ips"),
    "poll-download-progress": ("get-token", "get-node-url", "get-online-ips", "create-download-task"),
}
for _ip_action in IP_ACTION_SEQUENCE:
    ACTION_DEPENDENCIES[_ip_action] = tuple(GLOBAL_ACTION_SEQUENCE)


def allowed_tool_specs(policy: SkillPolicy) -> list[ActionToolSpec]:
    """按 skill 限制过滤并排序 tool specs。"""
    ordered_actions = ordered_actions_for_skill(policy)
    specs: list[ActionToolSpec] = []
    for action in ordered_actions:
        if action not in policy.allowed_actions:
            continue
        if action in policy.forbidden_actions:
            continue
        spec = ACTION_TOOL_SPECS.get(action)
        if spec is not None:
            specs.append(spec)
    return specs


def tool_summaries(policy: SkillPolicy, strategy: ExecutionStrategy) -> list[dict[str, str]]:
    """生成给 LLM 使用的受控 tool 摘要。"""
    routes = build_action_backends(strategy)
    summaries: list[dict[str, str]] = []
    for spec in allowed_tool_specs(policy):
        summaries.append(
            {
                "name": spec.name,
                "action": spec.action,
                "scope": spec.scope,
                "description": spec.description,
                "risk_level": spec.risk_level,
                "backend": routes.get(spec.action, spec.preferred_backend or ""),
                "requires_confirmation": "true" if spec.requires_confirmation else "false",
            }
        )
    return summaries


def normalize_planned_actions(
    planned_actions: list[str],
    *,
    policy: SkillPolicy,
    mode: AgentExecutionMode,
) -> list[str]:
    """按 skill 限制和依赖关系归一化 planned actions。"""
    allowed = set(policy.allowed_actions)
    forbidden = set(policy.forbidden_actions)
    ordered = ordered_actions_for_skill(policy)
    if not planned_actions:
        return [action for action in ordered if action in allowed and action not in forbidden]

    normalized: list[str] = []
    for action in planned_actions:
        if action in allowed and action not in forbidden and action not in normalized:
            normalized.append(action)

    expanded: list[str] = []
    for action in normalized:
        for dependency in ACTION_DEPENDENCIES.get(action, ()):
            if dependency in allowed and dependency not in forbidden and dependency not in expanded:
                expanded.append(dependency)
        if action not in expanded:
            expanded.append(action)

    global_order = [action for action in ordered if action in GLOBAL_ACTION_SEQUENCE and action in expanded]
    ip_order = [action for action in ordered if action in IP_ACTION_SEQUENCE and action in expanded]
    return [*global_order, *ip_order]