Files
zhenxun_bot/zhenxun/services/ai/run/subscribers.py
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80fc5b86a7 ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 (#2146)
* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架

- 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构,提供向下兼容垫片
- 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式
- 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统
- 【新增】引入基于 Docker 的安全代码执行沙箱环境
- 【新增】支持 MCP 协议,允许动态管理和调用 MCP 服务
- 【新增】引入输入输出安全合规护栏与自愈反思机制
- 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等)
- 【优化】优化日志脱敏与 Token 预估机制
- 【移除】移除旧版 llm default 和 llm reset-key 命令,新增 llm mcp 管理命令

* 🔧 chore(deps): 更新项目依赖与配置

- 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt
- 在 pyright 配置中设置 reportMissingImports 为 none
- 调整 .gitignore 中 resources 目录的忽略规则

* ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出

- 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束
- 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用
- 调整指令处理器(Directive)的返回值为 `tool_res.output`
- 修复部分类型检查警告并优化联合类型判断语法

* ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制

- 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用
- 移除通过 `context.state` 传递魔术变量的隐式控制流设计
- 重构 `DirectiveManager` 处理器接口,直接在处理器中修改 `AgentState` 并构建 `AgentRunResult`
- 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令
- 补全 `MessageBuilder` 中部分核心方法的文档注释

* 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑

-【修复】修正 `is_alive` 中直接读取私有属性的问题,改用 `show()` 返回值
-【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题
-【优化】在清理工作区前增加容器存活检测,避免向已死容器发送请求
-【优化】创建容器时增加运行状态校验,若已停止则自动从缓存中移除并重建
-【优化】优化容器销毁和清理逻辑,静默处理容器不存在 (404) 的异常

* 📝 docs(core): 补充核心模块初始化方法的文档注释

* 🚨 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>
2026-07-03 08:53:56 +08:00

139 lines
5.0 KiB
Python

import time
from typing import Any
from nonebot_plugin_alconna import UniMessage
from zhenxun.services.ai.core.messages import BaseContentPart, ImagePart, TextPart
from zhenxun.services.ai.core.stream_events import (
EventBus,
LLMEndEvent,
LLMStartEvent,
ToolCallEndEvent,
ToolCallStartEvent,
ToolStreamChunkEvent,
UserCustomEvent,
)
from zhenxun.services.ai.run.context import RunContext
from zhenxun.services.ai.run.models import AgentRunEnd, AgentRunStart, AgentRunSummary
from zhenxun.services.log import logger
class TelemetrySubscriber:
"""纯粹的数据观察者:默默记录时间戳与 Token 消耗"""
def __init__(self):
self.summary = AgentRunSummary()
self._start_times: dict[str, float] = {}
def attach(self, bus: EventBus):
bus.subscribe(AgentRunStart, self.on_run_start)
bus.subscribe(AgentRunEnd, self.on_run_end)
bus.subscribe(LLMStartEvent, self.on_llm_start)
bus.subscribe(LLMEndEvent, self.on_llm_end)
bus.subscribe(ToolCallStartEvent, self.on_tool_start)
bus.subscribe(ToolCallEndEvent, self.on_tool_end)
async def on_run_start(self, event: AgentRunStart):
self._start_times["run"] = time.monotonic()
logger.debug(f"🚀 [Telemetry] 智能体 {event.agent_name} 开始运行")
async def on_run_end(self, event: AgentRunEnd):
dur = (time.monotonic() - self._start_times.get("run", time.monotonic())) * 1000
self.summary.total_latency_ms = dur
event.result.telemetry = self.summary
logger.debug(f"🏁 [Telemetry] 智能体运行结束 (总耗时: {dur:.2f}ms)")
async def on_llm_start(self, event: LLMStartEvent):
self._start_times["llm"] = time.monotonic()
async def on_llm_end(self, event: LLMEndEvent):
dur = (time.monotonic() - self._start_times.pop("llm", time.monotonic())) * 1000
self.summary.chats.total += 1
self.summary.chats.total_latency_ms += dur
response = event.response
if response:
stop_reason = "tool_calls" if response.tool_calls else "stop"
self.summary.chats.by_stop_reason[stop_reason] = (
self.summary.chats.by_stop_reason.get(stop_reason, 0) + 1
)
logger.debug(f"🧠 [Telemetry] 模型调用完成 (耗时: {dur:.2f}ms)")
async def on_tool_start(self, event: ToolCallStartEvent):
self._start_times[f"tool_{event.tool_name}"] = time.monotonic()
async def on_tool_end(self, event: ToolCallEndEvent):
dur = (
time.monotonic()
- self._start_times.pop(f"tool_{event.tool_name}", time.monotonic())
) * 1000
self.summary.tools.total += 1
self.summary.tools.total_latency_ms += dur
tool_stat = self.summary.tools.by_name.setdefault(
event.tool_name, {"total": 0, "ok": 0, "error": 0, "latency_ms": 0.0}
)
tool_stat["total"] += 1
tool_stat["latency_ms"] += dur
if event.is_error:
self.summary.tools.error += 1
tool_stat["error"] += 1
else:
self.summary.tools.ok += 1
tool_stat["ok"] += 1
logger.debug(
f"🛠️ [Telemetry] 工具 {event.tool_name} 执行完毕 (耗时: {dur:.2f}ms)"
)
class DefaultUISubscriber:
"""纯粹的 UI 观察者:负责将特定事件转化为群聊气泡发送"""
def __init__(
self, context: RunContext, reply_to: bool = False, verbose: bool = False
):
self.context = context
self.reply_to = reply_to
self.verbose = verbose
self.bot = context.get_bot()
self.event = context.get_event()
def attach(self, bus: EventBus):
bus.subscribe(ToolStreamChunkEvent, self.on_tool_stream)
bus.subscribe(UserCustomEvent, self.on_custom_event)
async def _send_to_platform(self, display: Any):
if not self.bot or not self.event or not display:
return
if (
isinstance(display, list)
and len(display) > 0
and isinstance(display[0], BaseContentPart)
):
msg = UniMessage()
for part in display:
if isinstance(part, TextPart) and part.text:
msg = msg.text(part.text)
elif isinstance(part, ImagePart):
if part.raw:
msg = msg.image(raw=part.raw)
elif part.url:
msg = msg.image(url=part.url)
elif part.path:
msg = msg.image(path=part.path)
display = msg
if isinstance(display, UniMessage):
await display.send(self.event, bot=self.bot, reply_to=self.reply_to)
else:
await self.bot.send(self.event, str(display))
async def on_tool_stream(self, event: ToolStreamChunkEvent):
if self.verbose and event.content:
await self._send_to_platform(event.content)
async def on_custom_event(self, event: UserCustomEvent):
await self._send_to_platform(event.display)