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oliver
4a23b715a2
本次迁移将网关/通道/工具/技能/脚本与协议资源集中到新结构，统一路径常量与脚本转发机制，减少顶层噪音并保证运行与测试行为一致。 Made-with: Cursor
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Callable
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ToolHandler = Callable[[dict[str, Any]], Any]
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@dataclass(frozen=True)
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class ToolRateLimit:
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"""Best-effort per-tool rate limiting metadata (enforced by runtime when implemented)."""
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# tokens per window (simple leaky bucket style); None means unlimited.
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limit: int | None = None
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window_s: int = 60
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@dataclass(frozen=True)
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class ToolSpec:
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name: str
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description: str
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parameters: dict[str, Any]
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handler: ToolHandler
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tags: frozenset[str] = field(default_factory=frozenset)
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# Contract metadata (non-OpenAI; used by orchestrator/runtime)
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version: str = "v1"
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risk_level: str = "low" # low|high (extendable)
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timeout_s: float | None = None
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rate_limit: ToolRateLimit | None = None
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required_permissions: frozenset[str] = field(default_factory=frozenset)
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execution_mode: str = "in_process" # in_process|subprocess (best-effort)
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#: If true, may run in parallel with other consecutive read-only tools (cc-mini-style batching).
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read_only: bool = False
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def is_read_only(self) -> bool:
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"""Compatibility helper mirroring cc-mini Tool.is_read_only()."""
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return bool(self.read_only)
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def as_openai_tool(self) -> dict[str, Any]:
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return {
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"type": "function",
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"function": {
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"name": self.name,
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"description": self.description,
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"parameters": self.parameters,
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},
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}
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class ToolRegistry:
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def __init__(self, tools: list[ToolSpec] | None = None):
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self._tools: dict[str, ToolSpec] = {}
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self._openai_tools_cache: list[dict[str, Any]] | None = None
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if tools:
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for t in tools:
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self.register(t)
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def register(self, tool: ToolSpec) -> None:
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self._tools[tool.name] = tool
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self._openai_tools_cache = None
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def get(self, name: str) -> ToolSpec | None:
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return self._tools.get(name)
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def list(self) -> list[ToolSpec]:
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return list(self._tools.values())
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def as_openai_tools(self) -> list[dict[str, Any]]:
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if self._openai_tools_cache is None:
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self._openai_tools_cache = [t.as_openai_tool() for t in self.list()]
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return list(self._openai_tools_cache)
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__all__ = [
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"ToolHandler",
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"ToolRateLimit",
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"ToolSpec",
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"ToolRegistry",
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]
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