♻️ refactor(tools): 重构工具装饰器系统并优化沙箱与文档注释 (#2147)

* ♻️ refactor(ai): 重构 AI 服务模块并完善文档注释

- 【重构】统一清理并优化所有 AI 服务模块文件的导入语句,将其移至文件顶部
- 【重构】重构 `hooks.py` 中的 `Hooks` 派发逻辑,使用通用管道函数消除重复代码,并引入 `HookPoint` 描述符
- 【重构】重构工具装饰器实现,新增 `toolkit` 类装饰器,优化 `BaseToolkit` 配置合并与前缀处理
- 【功能】Docker 沙箱容器创建时支持自动注入系统代理环境变量并配置 `ExtraHosts`
- 【功能】Jupyter 服务启动前自动清理旧进程并初始化临时目录权限
- 【修复】优化 Pydantic 结构化输出校验失败时的错误信息提取,提供更详细的字段级错误反馈
- 【修复】在 `api.py` 中避免将 `ModelRetry` 和 `ControlFlowExit` 异常错误地包装为 `LLMException`
- 【文档】为 AI 服务、沙箱、工具链、工作流等核心模块补充完整的 Docstring 和类型注释

* 📝 docs(ai): 补全核心模块文档注释并清理冗余代码

- 补全 `run/context`、`run/hooks` 和 `tools/engine/registry` 中类与方法的中文文档注释
- 清理 `tools/providers/builtin/sandbox` 中未使用的 `PythonPluginProtocol` 协议及相关导入
- 规范化部分代码的格式与尾随逗号

* ♻️ refactor!(flow): 重构 Task 为 AgentTask 并优化工作流元数据定义

- 【Breaking Change】将 `Task` 重命名为 `AgentTask` 以避免命名冲突
- 更新 Agent、Team、Workflow 等模块中的类型声明与相关逻辑
- 引入 `AutoNodeMeta` 强类型元数据,替换工作流装饰器中的裸字典定义
- 将 `StepMeta`、`ConditionMeta` 和 `RouterMeta` 统一移动至 `types.py`
- 优化 `RunnableNode` 对上游 `AgentTask` 的处理与拼接逻辑
- 调整团队协作策略中 `FinishAction` 的返回值为完整结果对象

* ♻️ refactor(workflow): 移除人工确认机制并重构错误策略

- 移除工作流节点的人工确认(HITL)与挂起继续机制
- 删除 `auto` 自动化工作流及相关装饰器文件
- 将错误处理策略类从 `types.py` 拆分并移动到新文件 `policies.py`
- 优化节点执行失败时的异常信息格式化输出
- 移除 `WorkflowRunResult` 和 `StepOutput` 中与挂起相关的状态字段

* 🚨 auto fix by pre-commit hooks

---------

Co-authored-by: webjoin111 <455457521@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Rumio
2026-07-05 11:47:21 +08:00
committed by GitHub
co-authored by webjoin111 pre-commit-ci[bot]
parent 80fc5b86a7
commit 0b32d69c9c
61 changed files with 1659 additions and 1511 deletions
+305 -359
View File
@@ -1,6 +1,6 @@
from collections.abc import Awaitable, Callable
from dataclasses import dataclass
from typing import Any, Generic, Protocol, TypeVar
from typing import Any, Generic, Protocol, TypeVar, overload
import anyio
from nonebot.utils import is_coroutine_callable
@@ -25,6 +25,7 @@ class HookTimeoutError(TimeoutError):
"""当 Hook 函数执行超过配置的时间时抛出此异常。"""
def __init__(self, hook_name: str, func_name: str, timeout: float):
"""初始化 HookTimeoutError 异常实例。"""
self.hook_name = hook_name
self.func_name = func_name
self.timeout = timeout
@@ -49,28 +50,38 @@ class _ToolHookEntry(_HookEntry[_FuncT]):
class BeforeRunHookFunc(Protocol):
"""运行前钩子:在 Agent 启动任何流转前触发。"""
def __call__(self, ctx: RunContext[Any], /) -> None | Awaitable[None]: ...
class AfterRunHookFunc(Protocol):
"""运行后钩子:在 Agent 获取最终结果后触发,可用于修改最终输出。"""
def __call__(
self, ctx: RunContext[Any], /, *, result: AgentRunResult[Any]
) -> AgentRunResult[Any] | Awaitable[AgentRunResult[Any]]: ...
class WrapRunHookFunc(Protocol):
"""包裹运行钩子:以洋葱模型接管整个 Agent 运行生命周期。"""
def __call__(
self, ctx: RunContext[Any], /, *, handler: WrapRunHandler
) -> AgentRunResult[Any] | Awaitable[AgentRunResult[Any]]: ...
class OnRunErrorHookFunc(Protocol):
"""运行异常钩子:捕获 Agent 级别的致命错误。"""
def __call__(
self, ctx: RunContext[Any], /, *, error: BaseException
) -> AgentRunResult[Any] | Awaitable[AgentRunResult[Any]]: ...
class BeforeModelRequestHookFunc(Protocol):
"""大模型请求前钩子:在发送网络请求前触发,可篡改 Messages 上下文。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -83,6 +94,8 @@ class BeforeModelRequestHookFunc(Protocol):
class AfterModelRequestHookFunc(Protocol):
"""大模型请求后钩子:在接收到网络响应后触发,可验证或篡改原始 Response。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -94,6 +107,8 @@ class AfterModelRequestHookFunc(Protocol):
class WrapModelRequestHookFunc(Protocol):
"""包裹大模型请求钩子:以洋葱模型接管 LLM 网络请求过程(可用于实现缓存)。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -105,6 +120,8 @@ class WrapModelRequestHookFunc(Protocol):
class OnModelRequestErrorHookFunc(Protocol):
"""大模型请求异常钩子:捕获超时或网络等异常,可用于发起重试。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -116,24 +133,32 @@ class OnModelRequestErrorHookFunc(Protocol):
class PrepareToolsHookFunc(Protocol):
"""准备工具集钩子:向大模型渲染 JSON Schema 前触发,可动态增删工具。"""
def __call__(
self, ctx: RunContext[Any], tool_defs: list[Any], /
) -> list[Any] | Awaitable[list[Any]]: ...
class BeforeToolValidateHookFunc(Protocol):
"""工具验证前钩子:在反序列化前触发,可篡改原始 JSON 字符串或字典。"""
def __call__(
self, ctx: RunContext[Any], /, *, tool_name: str, args: str | dict[str, Any]
) -> str | dict[str, Any] | Awaitable[str | dict[str, Any]]: ...
class AfterToolValidateHookFunc(Protocol):
"""工具验证后钩子:在 Schema 校验通过后触发,接收并可篡改强类型参数字典。"""
def __call__(
self, ctx: RunContext[Any], /, *, tool_name: str, args: dict[str, Any]
) -> dict[str, Any] | Awaitable[dict[str, Any]]: ...
class WrapToolValidateHookFunc(Protocol):
"""包裹工具验证钩子:以洋葱模型接管工具参数校验过程(可用于交互式拦截)。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -146,6 +171,8 @@ class WrapToolValidateHookFunc(Protocol):
class OnToolValidateErrorHookFunc(Protocol):
"""工具验证异常钩子:捕获校验失败的异常,可将其转化为大模型自愈提示。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -158,12 +185,16 @@ class OnToolValidateErrorHookFunc(Protocol):
class BeforeToolExecuteHookFunc(Protocol):
"""工具执行前钩子:在工具物理执行前触发,可用于最后的权限判定或参数篡改。"""
def __call__(
self, ctx: RunContext[Any], /, *, tool_name: str, arguments: dict[str, Any]
) -> dict[str, Any] | Awaitable[dict[str, Any]]: ...
class AfterToolExecuteHookFunc(Protocol):
"""工具执行后钩子:工具执行完毕后触发,可篡改最终发往大模型的返回结果。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -176,6 +207,8 @@ class AfterToolExecuteHookFunc(Protocol):
class WrapToolExecuteHookFunc(Protocol):
"""包裹工具执行钩子:以洋葱模型接管特定工具的物理执行逻辑。"""
def __call__(
self,
ctx: RunContext[Any],
@@ -188,12 +221,15 @@ class WrapToolExecuteHookFunc(Protocol):
class OnToolExecuteErrorHookFunc(Protocol):
"""工具执行异常钩子:捕获工具执行时的崩溃异常,可用于兜底返回或自愈。"""
def __call__(
self, ctx: RunContext[Any], /, *, tool_name: str, error: Exception
) -> Any | Awaitable[Any]: ...
async def _call_func(func: Callable[..., Any], *args: Any, **kwargs: Any) -> Any:
"""统一执行同步或异步的可调用函数。"""
if is_coroutine_callable(func):
return await func(*args, **kwargs)
return func(*args, **kwargs)
@@ -276,224 +312,139 @@ def _tool_bare_or_parameterized(
return decorator
class BoundHookPoint(Generic[_FuncT]):
"""绑定到具体实例的 Hook 注册点。"""
def __init__(self, key: str, registry_holder: Any):
"""初始化绑定的 Hook 注册点。"""
self.key = key
self.registry_holder = registry_holder
@overload
def __call__(self, func: _FuncT, /) -> _FuncT: ...
@overload
def __call__(
self, *, timeout: float | None = None
) -> Callable[[_FuncT], _FuncT]: ...
def __call__(
self, func: _FuncT | None = None, *, timeout: float | None = None
) -> Any:
"""支持装饰器语法注册钩子函数。"""
return _bare_or_parameterized(
self.registry_holder._r, self.key, func, timeout=timeout
)
class HookPoint(Generic[_FuncT]):
"""描述符形式的 Hook 注册点。"""
def __init__(self, key: str):
"""初始化 Hook 描述符。"""
self.key = key
def __get__(self, instance: Any, owner: Any) -> BoundHookPoint[_FuncT]:
"""通过描述符协议绑定 Hook 实例。"""
return BoundHookPoint(self.key, instance)
class BoundToolHookPoint(Generic[_FuncT]):
"""绑定到具体实例的工具级别 Hook 注册点。"""
def __init__(self, key: str, registry_holder: Any):
"""初始化绑定的工具 Hook 注册点。"""
self.key = key
self.registry_holder = registry_holder
@overload
def __call__(self, func: _FuncT, /) -> _FuncT: ...
@overload
def __call__(
self, *, tools: list[str] | None = None, timeout: float | None = None
) -> Callable[[_FuncT], _FuncT]: ...
def __call__(
self,
func: _FuncT | None = None,
*,
tools: list[str] | None = None,
timeout: float | None = None,
) -> Any:
"""支持装饰器语法注册工具钩子函数。"""
return _tool_bare_or_parameterized(
self.registry_holder._r, self.key, func, tools=tools, timeout=timeout
)
class ToolHookPoint(Generic[_FuncT]):
"""描述符形式的工具级别 Hook 注册点。"""
def __init__(self, key: str):
"""初始化工具 Hook 描述符。"""
self.key = key
def __get__(self, instance: Any, owner: Any) -> BoundToolHookPoint[_FuncT]:
"""通过描述符协议绑定工具 Hook 实例。"""
return BoundToolHookPoint(self.key, instance)
class _HookRegistration:
"""
Hooks 的装饰器命名空间。
利用 @overload 提供完美的 IDE 强类型补全和文档提示。
"""
"""Hooks 装饰器注册辅助类,用于各阶段钩子的声明式注册"""
def __init__(self, hooks: "Hooks"):
"""初始化钩子注册辅助实例。"""
self._hooks = hooks
@property
def _r(self) -> dict[str, list[_HookEntry[Any]]]:
"""获取底层注册的钩子字典。"""
return self._hooks._registry
from typing import overload
before_run = HookPoint[BeforeRunHookFunc]("before_run")
"""注册运行前钩子。在 Agent 启动任何流转前触发。"""
after_run = HookPoint[AfterRunHookFunc]("after_run")
"""注册运行后钩子。在 Agent 获取最终结果后触发,可修改结果。"""
wrap_run = HookPoint[WrapRunHookFunc]("wrap_run")
"""注册运行包裹钩子。以洋葱模型接管整个 Agent 运行过程。"""
on_run_error = HookPoint[OnRunErrorHookFunc]("on_run_error")
"""注册运行异常钩子。捕获 Agent 级别的致命错误。"""
@overload
def before_run(self, func: BeforeRunHookFunc, /) -> BeforeRunHookFunc: ...
@overload
def before_run(
self, *, timeout: float | None = None
) -> Callable[[BeforeRunHookFunc], BeforeRunHookFunc]: ...
def before_run(
self, func: BeforeRunHookFunc | None = None, *, timeout: float | None = None
) -> Any:
"""注册运行前钩子。在 Agent 启动任何流转前触发。"""
return _bare_or_parameterized(self._r, "before_run", func, timeout=timeout)
before_model_request = HookPoint[BeforeModelRequestHookFunc]("before_model_request")
"""注册大模型请求前钩子。可在此修改发送给 LLM 的 Messages 等上下文。"""
after_model_request = HookPoint[AfterModelRequestHookFunc]("after_model_request")
"""注册大模型请求后钩子。可在此验证或修改 LLM 的原始 Response。"""
wrap_model_request = HookPoint[WrapModelRequestHookFunc]("wrap_model_request")
"""注册大模型请求包裹钩子。以洋葱模型接管 LLM 的网络请求过程。"""
on_model_request_error = HookPoint[OnModelRequestErrorHookFunc](
"on_model_request_error"
)
"""注册大模型请求异常钩子。捕获超时或网络等异常。"""
@overload
def after_run(self, func: AfterRunHookFunc, /) -> AfterRunHookFunc: ...
@overload
def after_run(
self, *, timeout: float | None = None
) -> Callable[[AfterRunHookFunc], AfterRunHookFunc]: ...
def after_run(
self, func: AfterRunHookFunc | None = None, *, timeout: float | None = None
) -> Any:
"""注册运行后钩子。在 Agent 获取最终结果后触发,可修改结果。"""
return _bare_or_parameterized(self._r, "after_run", func, timeout=timeout)
before_tool_validate = HookPoint[BeforeToolValidateHookFunc]("before_tool_validate")
"""注册工具验证前钩子。在工具输入参数校验前触发,可篡改或校验参数。"""
after_tool_validate = HookPoint[AfterToolValidateHookFunc]("after_tool_validate")
"""注册工具验证后钩子。在工具输入参数校验成功后触发,可篡改最终参数。"""
wrap_tool_validate = HookPoint[WrapToolValidateHookFunc]("wrap_tool_validate")
"""注册工具验证包裹钩子。以洋葱模型接管工具参数校验过程。"""
on_tool_validate_error = HookPoint[OnToolValidateErrorHookFunc](
"on_tool_validate_error"
)
"""注册工具验证异常钩子。捕获并处理校验阶段抛出的异常。"""
@overload
def wrap_run(self, func: WrapRunHookFunc, /) -> WrapRunHookFunc: ...
@overload
def wrap_run(
self, *, timeout: float | None = None
) -> Callable[[WrapRunHookFunc], WrapRunHookFunc]: ...
def wrap_run(
self, func: WrapRunHookFunc | None = None, *, timeout: float | None = None
) -> Any:
"""注册运行包裹钩子。以洋葱模型接管整个 Agent 运行过程。"""
return _bare_or_parameterized(self._r, "wrap_run", func, timeout=timeout)
@overload
def on_run_error(self, func: OnRunErrorHookFunc, /) -> OnRunErrorHookFunc: ...
@overload
def on_run_error(
self, *, timeout: float | None = None
) -> Callable[[OnRunErrorHookFunc], OnRunErrorHookFunc]: ...
def on_run_error(
self, func: OnRunErrorHookFunc | None = None, *, timeout: float | None = None
) -> Any:
"""注册运行异常钩子。捕获 Agent 级别的致命错误。"""
return _bare_or_parameterized(self._r, "on_run_error", func, timeout=timeout)
@overload
def before_model_request(
self, func: BeforeModelRequestHookFunc, /
) -> BeforeModelRequestHookFunc: ...
@overload
def before_model_request(
self, *, timeout: float | None = None
) -> Callable[[BeforeModelRequestHookFunc], BeforeModelRequestHookFunc]: ...
def before_model_request(
self,
func: BeforeModelRequestHookFunc | None = None,
*,
timeout: float | None = None,
) -> Any:
"""注册大模型请求前钩子。可在此修改发送给 LLM 的 Messages 等上下文。"""
return _bare_or_parameterized(
self._r, "before_model_request", func, timeout=timeout
)
@overload
def after_model_request(
self, func: AfterModelRequestHookFunc, /
) -> AfterModelRequestHookFunc: ...
@overload
def after_model_request(
self, *, timeout: float | None = None
) -> Callable[[AfterModelRequestHookFunc], AfterModelRequestHookFunc]: ...
def after_model_request(
self,
func: AfterModelRequestHookFunc | None = None,
*,
timeout: float | None = None,
) -> Any:
"""注册大模型请求后钩子。可在此验证或修改 LLM 的原始 Response。"""
return _bare_or_parameterized(
self._r, "after_model_request", func, timeout=timeout
)
@overload
def wrap_model_request(
self, func: WrapModelRequestHookFunc, /
) -> WrapModelRequestHookFunc: ...
@overload
def wrap_model_request(
self, *, timeout: float | None = None
) -> Callable[[WrapModelRequestHookFunc], WrapModelRequestHookFunc]: ...
def wrap_model_request(
self,
func: WrapModelRequestHookFunc | None = None,
*,
timeout: float | None = None,
) -> Any:
"""注册大模型请求包裹钩子。以洋葱模型接管 LLM 的网络请求过程。"""
return _bare_or_parameterized(
self._r, "wrap_model_request", func, timeout=timeout
)
@overload
def on_model_request_error(
self, func: OnModelRequestErrorHookFunc, /
) -> OnModelRequestErrorHookFunc: ...
@overload
def on_model_request_error(
self, *, timeout: float | None = None
) -> Callable[[OnModelRequestErrorHookFunc], OnModelRequestErrorHookFunc]: ...
def on_model_request_error(
self,
func: OnModelRequestErrorHookFunc | None = None,
*,
timeout: float | None = None,
) -> Any:
"""注册大模型请求异常钩子。捕获超时或网络等异常。"""
return _bare_or_parameterized(
self._r, "on_model_request_error", func, timeout=timeout
)
@overload
def before_tool_execute(
self, func: BeforeToolExecuteHookFunc, /
) -> BeforeToolExecuteHookFunc: ...
@overload
def before_tool_execute(
self, *, tools: list[str] | None = None, timeout: float | None = None
) -> Callable[[BeforeToolExecuteHookFunc], BeforeToolExecuteHookFunc]: ...
def before_tool_execute(
self,
func: BeforeToolExecuteHookFunc | None = None,
*,
tools: list[str] | None = None,
timeout: float | None = None,
) -> Any:
"""注册工具执行前钩子。可通过 tools 参数指定拦截特定工具,可篡改传入参数。"""
return _tool_bare_or_parameterized(
self._r, "before_tool_execute", func, tools=tools, timeout=timeout
)
@overload
def after_tool_execute(
self, func: AfterToolExecuteHookFunc, /
) -> AfterToolExecuteHookFunc: ...
@overload
def after_tool_execute(
self, *, tools: list[str] | None = None, timeout: float | None = None
) -> Callable[[AfterToolExecuteHookFunc], AfterToolExecuteHookFunc]: ...
def after_tool_execute(
self,
func: AfterToolExecuteHookFunc | None = None,
*,
tools: list[str] | None = None,
timeout: float | None = None,
) -> Any:
"""注册工具执行后钩子。可通过 tools 参数指定拦截特定工具,可篡改返回结果。"""
return _tool_bare_or_parameterized(
self._r, "after_tool_execute", func, tools=tools, timeout=timeout
)
@overload
def wrap_tool_execute(
self, func: WrapToolExecuteHookFunc, /
) -> WrapToolExecuteHookFunc: ...
@overload
def wrap_tool_execute(
self, *, tools: list[str] | None = None, timeout: float | None = None
) -> Callable[[WrapToolExecuteHookFunc], WrapToolExecuteHookFunc]: ...
def wrap_tool_execute(
self,
func: WrapToolExecuteHookFunc | None = None,
*,
tools: list[str] | None = None,
timeout: float | None = None,
) -> Any:
"""注册工具执行包裹钩子。以洋葱模型接管特定工具的执行逻辑。"""
return _tool_bare_or_parameterized(
self._r, "wrap_tool_execute", func, tools=tools, timeout=timeout
)
@overload
def on_tool_execute_error(
self, func: OnToolExecuteErrorHookFunc, /
) -> OnToolExecuteErrorHookFunc: ...
@overload
def on_tool_execute_error(
self, *, tools: list[str] | None = None, timeout: float | None = None
) -> Callable[[OnToolExecuteErrorHookFunc], OnToolExecuteErrorHookFunc]: ...
def on_tool_execute_error(
self,
func: OnToolExecuteErrorHookFunc | None = None,
*,
tools: list[str] | None = None,
timeout: float | None = None,
) -> Any:
"""注册工具执行异常钩子。捕获特定工具的崩溃异常,可用于自愈重试。"""
return _tool_bare_or_parameterized(
self._r, "on_tool_execute_error", func, tools=tools, timeout=timeout
)
before_tool_execute = ToolHookPoint[BeforeToolExecuteHookFunc](
"before_tool_execute"
)
"""注册工具执行前钩子。可通过 tools 参数指定拦截特定工具,可篡改传入参数。"""
after_tool_execute = ToolHookPoint[AfterToolExecuteHookFunc]("after_tool_execute")
"""注册工具执行后钩子。可通过 tools 参数指定拦截特定工具,可篡改返回结果。"""
wrap_tool_execute = ToolHookPoint[WrapToolExecuteHookFunc]("wrap_tool_execute")
"""注册工具执行包裹钩子。以洋葱模型接管特定工具的执行逻辑。"""
on_tool_execute_error = ToolHookPoint[OnToolExecuteErrorHookFunc](
"on_tool_execute_error"
)
"""注册工具执行异常钩子。捕获特定工具的崩溃异常,可用于自愈重试。"""
class Hooks(AbstractCapability):
@@ -503,93 +454,111 @@ class Hooks(AbstractCapability):
"""
def __init__(self):
"""初始化 Hooks 拦截器实例。"""
self._registry: dict[str, list[_HookEntry[Any]]] = {}
self.on = _HookRegistration(self)
async def wrap_run(
self, context: RunContext, handler: WrapRunHandler
) -> AgentRunResult[Any]:
"""派发:洋葱模型组装与全周期执行接管"""
for entry in self._registry.get("before_run", []):
await _call_entry(entry, "before_run", context)
async def _dispatch_pipeline(
self,
hook_prefix: str,
context: RunContext,
handler: Callable,
get_entries: Callable[[str], list[_HookEntry[Any]]],
do_before: Callable[[_HookEntry[Any]], Awaitable[Any]],
get_wrap_kwargs: Callable[[Callable], dict[str, Any]],
invoke_chain: Callable[[Callable], Awaitable[Any]],
do_error: Callable[[_HookEntry[Any], BaseException], Awaitable[Any]],
do_after: Callable[[_HookEntry[Any], Any], Awaitable[Any]],
) -> Any:
"""核心泛型流水线引擎,按阶段分发并执行注册的钩子链。"""
for entry in get_entries(f"before_{hook_prefix}"):
await do_before(entry)
entries = self._registry.get("wrap_run", [])
chain = handler
if entries:
for entry in reversed(entries):
wrap_entries = get_entries(f"wrap_{hook_prefix}")
if wrap_entries:
for entry in reversed(wrap_entries):
def _wrap(
e: _HookEntry[Any], h: Callable[..., Any]
) -> Callable[..., Any]:
async def _wrapped() -> Any:
return await _call_entry(e, "wrap_run", context, h)
def _wrap(e: _HookEntry[Any], h: Callable) -> Callable:
async def _wrapped(*args, **kwargs) -> Any:
return await _call_entry(
e, f"wrap_{hook_prefix}", context, **get_wrap_kwargs(h)
)
return _wrapped
chain = _wrap(entry, chain)
try:
result = await chain()
result = await invoke_chain(chain)
except BaseException as error:
for err_entry in reversed(self._registry.get("on_run_error", [])):
err_entries = get_entries(f"on_{hook_prefix}_error")
for err_entry in reversed(err_entries):
try:
return await _call_entry(err_entry, "on_run_error", context, error)
return await do_error(err_entry, error)
except BaseException as new_err:
error = new_err
raise error
for after_entry in reversed(self._registry.get("after_run", [])):
result = await _call_entry(after_entry, "after_run", context, result)
for after_entry in reversed(get_entries(f"after_{hook_prefix}")):
res = await do_after(after_entry, result)
if res is not None:
result = res
return result
async def wrap_run(
self, context: RunContext, handler: WrapRunHandler
) -> AgentRunResult[Any]:
"""接管并包裹 Agent 运行生命周期的执行。"""
return await self._dispatch_pipeline(
"run",
context,
handler,
get_entries=lambda p: self._registry.get(p, []),
do_before=lambda e: _call_entry(e, "before_run", context),
get_wrap_kwargs=lambda h: {"handler": h},
invoke_chain=lambda c: c(),
do_error=lambda e, err: _call_entry(e, "on_run_error", context, error=err),
do_after=lambda e, res: _call_entry(e, "after_run", context, result=res),
)
async def wrap_model_request(
self,
context: RunContext,
llm_context: LLMContext[ChatRequest, ChatResponse],
handler: WrapModelRequestHandler,
) -> ChatResponse:
"""派发:洋葱模型接管网络请求及前后生命周期"""
for entry in self._registry.get("before_model_request", []):
llm_context = await _call_entry(
entry, "before_model_request", context, llm_context
)
"""接管并包裹 LLM 大模型请求的网络交互过程。"""
entries = self._registry.get("wrap_model_request", [])
chain = handler
if entries:
for entry in reversed(entries):
async def do_before(e):
nonlocal llm_context
res = await _call_entry(e, "before_model_request", context, llm_context)
if res is not None:
llm_context = res
def _make_chain(
e: _HookEntry[Any], h: Callable[..., Any]
) -> Callable[..., Any]:
async def _wrapped(
ctx_inner: LLMContext[ChatRequest, ChatResponse],
) -> Any:
return await _call_entry(
e, "wrap_model_request", context, ctx_inner, h
)
return _wrapped
chain = _make_chain(entry, chain)
try:
response = await chain(llm_context)
except Exception as error:
for err_entry in reversed(self._registry.get("on_model_request_error", [])):
try:
return await _call_entry(
err_entry, "on_model_request_error", context, llm_context, error
)
except Exception as new_err:
error = new_err
raise error
for after_entry in reversed(self._registry.get("after_model_request", [])):
response = await _call_entry(
after_entry, "after_model_request", context, llm_context, response
)
return response
return await self._dispatch_pipeline(
"model_request",
context,
handler,
get_entries=lambda p: self._registry.get(p, []),
do_before=do_before,
get_wrap_kwargs=lambda h: {"request_context": llm_context, "handler": h},
invoke_chain=lambda c: c(llm_context),
do_error=lambda e, err: _call_entry(
e,
"on_model_request_error",
context,
request_context=llm_context,
error=err,
),
do_after=lambda e, res: _call_entry(
e,
"after_model_request",
context,
request_context=llm_context,
response=res,
),
)
async def wrap_tool_validate(
self,
@@ -598,51 +567,40 @@ class Hooks(AbstractCapability):
args: str | dict[str, Any],
handler: WrapToolValidateHandler,
) -> dict[str, Any]:
"""派发:洋葱模型接管工具参数校验及生命周期"""
for entry in self._registry.get("before_tool_validate", []):
args = await _call_entry(
entry, "before_tool_validate", context, tool_name, args
"""接管并包裹工具参数的校验过程。"""
async def do_before(e):
nonlocal args
res = await _call_entry(
e, "before_tool_validate", context, tool_name=tool_name, args=args
)
if res is not None:
args = res
entries = self._registry.get("wrap_tool_validate", [])
chain = handler
if entries:
for entry in reversed(entries):
def _wrap(
e: _HookEntry[Any], h: Callable[..., Any]
) -> Callable[..., Any]:
async def _wrapped(args_inner: str | dict[str, Any]) -> Any:
return await _call_entry(
e, "wrap_tool_validate", context, tool_name, args_inner, h
)
return _wrapped
chain = _wrap(entry, chain)
try:
validated_args = await chain(args)
except Exception as error:
for err_entry in reversed(self._registry.get("on_tool_validate_error", [])):
try:
return await _call_entry(
err_entry,
"on_tool_validate_error",
context,
tool_name,
args,
error,
)
except Exception as new_err:
error = new_err
raise error
for after_entry in reversed(self._registry.get("after_tool_validate", [])):
validated_args = await _call_entry(
after_entry, "after_tool_validate", context, tool_name, validated_args
)
return validated_args
return await self._dispatch_pipeline(
"tool_validate",
context,
handler,
get_entries=lambda p: self._registry.get(p, []),
do_before=do_before,
get_wrap_kwargs=lambda h: {
"tool_name": tool_name,
"args": args,
"handler": h,
},
invoke_chain=lambda c: c(args),
do_error=lambda e, err: _call_entry(
e,
"on_tool_validate_error",
context,
tool_name=tool_name,
args=args,
error=err,
),
do_after=lambda e, res: _call_entry(
e, "after_tool_validate", context, tool_name=tool_name, args=res
),
)
async def wrap_tool_execute(
self,
@@ -651,55 +609,43 @@ class Hooks(AbstractCapability):
arguments: dict[str, Any],
handler: WrapToolExecuteHandler,
) -> Any:
"""派发:洋葱模型接管工具执行(支持匹配名称)及生命周期"""
for entry in _filter_tool_entries(
self._registry.get("before_tool_execute", []), tool_name=tool_name
):
arguments = await _call_entry(
entry, "before_tool_execute", context, tool_name, arguments
)
"""接管并包裹特定工具的物理执行逻辑。"""
entries = _filter_tool_entries(
self._registry.get("wrap_tool_execute", []), tool_name=tool_name
async def do_before(e):
nonlocal arguments
res = await _call_entry(
e,
"before_tool_execute",
context,
tool_name=tool_name,
arguments=arguments,
)
if res is not None:
arguments = res
return await self._dispatch_pipeline(
"tool_execute",
context,
handler,
get_entries=lambda p: _filter_tool_entries(
self._registry.get(p, []), tool_name=tool_name
),
do_before=do_before,
get_wrap_kwargs=lambda h: {
"tool_name": tool_name,
"arguments": arguments,
"handler": h,
},
invoke_chain=lambda c: c(arguments),
do_error=lambda e, err: _call_entry(
e, "on_tool_execute_error", context, tool_name=tool_name, error=err
),
do_after=lambda e, res: _call_entry(
e,
"after_tool_execute",
context,
tool_name=tool_name,
arguments=arguments,
result=res,
),
)
chain = handler
if entries:
for entry in reversed(entries):
def _wrap(
e: _HookEntry[Any], h: Callable[..., Any]
) -> Callable[..., Any]:
async def _wrapped(args_inner: dict[str, Any]) -> Any:
return await _call_entry(
e, "wrap_tool_execute", context, tool_name, args_inner, h
)
return _wrapped
chain = _wrap(entry, chain)
try:
result = await chain(arguments)
except Exception as error:
for err_entry in reversed(
_filter_tool_entries(
self._registry.get("on_tool_execute_error", []), tool_name=tool_name
)
):
try:
return await _call_entry(
err_entry, "on_tool_execute_error", context, tool_name, error
)
except Exception as new_err:
error = new_err
raise error
for after_entry in reversed(
_filter_tool_entries(
self._registry.get("after_tool_execute", []), tool_name=tool_name
)
):
result = await _call_entry(
after_entry, "after_tool_execute", context, tool_name, arguments, result
)
return result