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✨ 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>
This commit is contained in:
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co-authored by
webjoin111
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webjoin111
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parent
bdc1374848
commit
80fc5b86a7
@@ -0,0 +1,547 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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import ast
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import asyncio
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from contextlib import asynccontextmanager
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import inspect
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import json
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from typing import TYPE_CHECKING, Any, cast
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import json_repair
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from nonebot.adapters import Message as PlatformMessage
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from zhenxun.services.ai.capabilities import CombinedCapability
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from zhenxun.services.ai.core.exceptions import (
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ToolFatalError,
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ToolRetryError,
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)
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from zhenxun.services.ai.core.messages import AnyLLMMessage, LLMMessage, ToolCallPart
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from zhenxun.services.ai.core.stream_events import (
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EventBus,
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ToolCallEndEvent,
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ToolCallStartEvent,
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ToolStreamChunkEvent,
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UserCustomEvent,
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)
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from zhenxun.services.ai.run.context import RunContext
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from zhenxun.services.ai.run.di import DependencyInjector
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from zhenxun.services.ai.tools.models import (
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ToolOptions,
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ToolResult,
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ValidatedToolCall,
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)
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from zhenxun.services.log import logger
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if TYPE_CHECKING:
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from zhenxun.services.ai.tools.core.tool import BaseTool
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from zhenxun.services.ai.tools.engine.registry import ToolCollection
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class ToolExecutor:
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"""
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全能工具执行器。
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负责接收工具调用请求，解析参数，触发回调，执行工具，并返回标准化的结果。
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"""
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def __init__(self):
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pass
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def _get_combined_capability(
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self, executable: Any, context: RunContext
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) -> CombinedCapability:
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"""合并 Agent 上下文 (已包含 Global) 与 Tool 私有的 Capability"""
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tool_caps = getattr(getattr(executable, "settings", None), "capabilities", [])
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agent_caps = getattr(context, "capabilities", [])
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all_caps = list(agent_caps)
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for c in tool_caps:
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if c not in all_caps:
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all_caps.append(c)
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return CombinedCapability(all_caps)
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@asynccontextmanager
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async def _tool_stream_scope(
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self,
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event_bus: EventBus | None,
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tool_name: str,
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arguments: dict[str, Any],
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intent: str | None,
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):
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"""生命周期上下文管理器：接管事件流的发送与异常包装样板代码"""
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if event_bus:
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await event_bus.emit(
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ToolCallStartEvent(
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tool_name=tool_name, arguments=arguments, intent=intent
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)
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)
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result_box: dict[str, Any] = {}
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try:
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yield result_box
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finally:
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if event_bus and "result" in result_box:
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res = result_box["result"]
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await event_bus.emit(
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ToolCallEndEvent(
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tool_name=tool_name, result=res, is_error=res.is_error
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)
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)
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@staticmethod
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def _robust_parse_args(
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args_raw: str | dict[str, Any],
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) -> tuple[bool, dict[str, Any], str | None, str]:
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"""容错解析参数纯函数，返回 (是否成功, 解析后的字典, intent意图, 原始字符串)"""
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if isinstance(args_raw, dict):
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arguments = args_raw.copy()
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parsed_successfully = True
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args_str = json.dumps(args_raw, ensure_ascii=False)
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else:
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args_str = args_raw
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arguments = {}
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parsed_successfully = False
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if not args_str.strip():
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parsed_successfully = True
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else:
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try:
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parsed = json.loads(args_str)
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if isinstance(parsed, dict):
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arguments, parsed_successfully = parsed, True
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except json.JSONDecodeError:
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try:
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parsed = ast.literal_eval(args_str)
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if isinstance(parsed, dict):
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arguments, parsed_successfully = parsed, True
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except (ValueError, SyntaxError):
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try:
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repaired_str = str(
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json_repair.repair_json(args_str, skip_json_loads=True)
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)
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parsed = json.loads(repaired_str)
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if isinstance(parsed, dict):
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arguments, parsed_successfully = parsed, True
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logger.debug(
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"⚒️ 成功修复损坏的工具参数: "
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f"{args_str} -> {repaired_str}",
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"ToolExecutor",
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)
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except Exception:
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pass
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intent_str = None
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if parsed_successfully:
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_intent = arguments.pop("_intent", None)
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if _intent:
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intent_str = str(_intent)
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return parsed_successfully, arguments, intent_str, args_str
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def _prepare_tool_context(
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self,
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context: RunContext | None,
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tool_call_id: str,
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tool_name: str,
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executable: Any,
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event_bus: EventBus | None,
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available_tools: "ToolCollection | dict[str, Any] | None" = None,
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) -> RunContext:
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"""准备/克隆工具调用所使用的隔离 RunContext"""
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from zhenxun.services.ai.run import RunContext
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safe_context = (
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context.clone_for_tool_call(tool_call_id, tool_name)
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if context
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else RunContext()
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)
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safe_context.run.event_bus = event_bus
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safe_context.call.tool_name = tool_name
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safe_context.call.current_tool = executable
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if available_tools is not None:
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safe_context.state["__available_tools"] = available_tools
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return safe_context
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async def validate_tool_call(
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self,
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tool_call: ToolCallPart,
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available_tools: "ToolCollection | dict[str, Any] | None",
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context: RunContext | None = None,
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event_bus: EventBus | None = None,
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) -> ValidatedToolCall:
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"""验证单一工具调用，完成参数解析、类型检查与交互式补全(如果触发)。"""
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if not available_tools:
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available_tools = {}
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if not tool_call.tool_name:
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return ValidatedToolCall(
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call=tool_call,
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args_valid=False,
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validation_error=ToolRetryError("tool_call.tool_name 不能为空"),
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)
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tool_name = tool_call.tool_name
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parsed_successfully, arguments, intent_str, arguments_str = (
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self._robust_parse_args(tool_call.args)
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)
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if tool_call.args and not parsed_successfully:
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if context:
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context.run.tool_retries[tool_name] = (
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context.run.tool_retries.get(tool_name, 0) + 1
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)
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return ValidatedToolCall(
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call=tool_call,
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args_valid=False,
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validation_error=ToolRetryError(
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f"参数JSON解析失败，请检查 JSON 语法是否合法: {arguments_str}"
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),
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)
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elif intent_str:
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logger.info(f"🧠 [Agent Intent] 调用工具 {tool_name} 的意图: {intent_str}")
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executable = available_tools.get(tool_name)
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if not executable or not hasattr(executable, "execute"):
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return ValidatedToolCall(
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call=tool_call,
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args_valid=False,
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validation_error=ToolRetryError(f"Tool '{tool_name}' not found."),
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)
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safe_context = self._prepare_tool_context(
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context, tool_call.id, tool_name, executable, event_bus
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)
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combined_cap = self._get_combined_capability(executable, safe_context)
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async def inner_validate(args_inner):
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if isinstance(args_inner, dict) and hasattr(executable, "validate_args"):
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import inspect
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sig = inspect.signature(executable.validate_args)
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if "context" in sig.parameters:
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return await executable.validate_args(
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args_inner, context=safe_context
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)
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return await executable.validate_args(args_inner)
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return args_inner
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try:
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validated_args = await combined_cap.wrap_tool_validate(
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safe_context, tool_name, arguments, inner_validate
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)
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return ValidatedToolCall(
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call=tool_call,
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tool=executable,
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args_valid=True,
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validated_args=validated_args,
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intent=intent_str,
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)
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except BaseException as e:
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if isinstance(e, asyncio.CancelledError):
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raise e
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return ValidatedToolCall(
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call=tool_call,
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tool=executable,
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args_valid=False,
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validation_error=e,
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)
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async def execute_tool_call(
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self,
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validated: ValidatedToolCall,
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available_tools: "ToolCollection | dict[str, Any] | None",
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context: RunContext | None = None,
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model_name: str | None = None,
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max_retries: int = 0,
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event_bus: EventBus | None = None,
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) -> tuple[ToolCallPart, ToolResult]:
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"""核心执行阶段。只接收已经通过 Validation 阶段的 ValidatedToolCall 载体。"""
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if not available_tools:
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available_tools = {}
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tool_name = validated.call.tool_name
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if not validated.args_valid or validated.tool is None:
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from zhenxun.services.ai.core.exceptions import ControlFlowExit
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if isinstance(validated.validation_error, ControlFlowExit):
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raise validated.validation_error
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err_msg = getattr(
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validated.validation_error, "message", str(validated.validation_error)
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)
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res = ToolResult(output=f"执行被拦截或参数错误: {err_msg}").as_error()
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if event_bus:
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await event_bus.emit(
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ToolCallEndEvent(
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tool_name=tool_name, result=res, is_error=res.is_error
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)
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)
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return validated.call, res
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executable = validated.tool
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arguments = validated.validated_args or {}
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safe_context = self._prepare_tool_context(
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context,
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validated.call.id,
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tool_name,
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executable,
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event_bus,
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available_tools,
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)
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from zhenxun.services.ai.run.context import set_run_context
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combined_cap = self._get_combined_capability(executable, safe_context)
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async def inner_handler(args_inner: dict) -> Any:
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return await executable.execute(context=safe_context, **args_inner)
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async with self._tool_stream_scope(
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event_bus, tool_name, arguments, validated.intent
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) as box:
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with set_run_context(safe_context):
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try:
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result = await combined_cap.wrap_tool_execute(
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safe_context, tool_name, arguments, inner_handler
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)
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if not isinstance(result, ToolResult):
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result = ToolResult(output=result)
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from zhenxun.services.ai.tools.models import StateSyncResult
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if isinstance(result, StateSyncResult) and result.state_notice:
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if safe_context:
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safe_context.run.add_system_prompt(
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f"[系统通知(状态同步)]：{result.state_notice}"
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)
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except BaseException as e:
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from zhenxun.services.ai.core.exceptions import ControlFlowExit
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if isinstance(e, ControlFlowExit):
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raise e
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if isinstance(e, asyncio.CancelledError):
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raise e
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logger.error(f"洋葱模型异常穿透: {e}")
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result = ToolResult(output=f"System Fatal Error: {e}").as_error()
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box["result"] = result
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return validated.call, box["result"]
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async def execute_batch(
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self,
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tool_calls: list[ToolCallPart],
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available_tools: "ToolCollection | dict[str, Any] | None",
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context: RunContext | None = None,
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model_name: str | None = None,
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max_retries: int = 0,
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event_bus: EventBus | None = None,
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) -> list[AnyLLMMessage]:
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"""批量并发执行多个工具调用。"""
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if not available_tools:
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available_tools = {}
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if not tool_calls:
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return []
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val_tasks = [
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self.validate_tool_call(
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call,
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available_tools,
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context,
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event_bus=event_bus,
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)
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for call in tool_calls
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]
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validated_calls = await asyncio.gather(*val_tasks)
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results: list[Any] = [None] * len(validated_calls)
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async def _run_tool(index: int, val_call: ValidatedToolCall):
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try:
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res = await self.execute_tool_call(
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val_call,
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available_tools,
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context,
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model_name=model_name,
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max_retries=max_retries,
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event_bus=event_bus,
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)
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results[index] = res
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except Exception as e:
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results[index] = e
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chunks: list[list[tuple[int, ValidatedToolCall]]] = []
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current_chunk: list[tuple[int, ValidatedToolCall]] = []
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for i, val_call in enumerate(validated_calls):
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executable = getattr(val_call, "tool", None)
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concurrency_mode = getattr(
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getattr(executable, "settings", None), "concurrency", "shared"
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)
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if concurrency_mode == "exclusive":
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if current_chunk:
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chunks.append(current_chunk)
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current_chunk = []
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chunks.append([(i, val_call)])
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else:
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current_chunk.append((i, val_call))
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if current_chunk:
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chunks.append(current_chunk)
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for chunk in chunks:
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await asyncio.gather(*[_run_tool(i, call) for i, call in chunk])
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tool_messages: list[AnyLLMMessage] = []
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for index, result_pair in enumerate(results):
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original_call = tool_calls[index]
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func_name = original_call.tool_name
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if isinstance(result_pair, BaseException):
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from zhenxun.services.ai.core.exceptions import ControlFlowExit
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if isinstance(result_pair, ControlFlowExit):
|
||||
raise result_pair
|
||||
if isinstance(result_pair, asyncio.CancelledError):
|
||||
raise result_pair
|
||||
|
||||
logger.error(f"工具批量执行并发崩溃: {func_name}, 错误: {result_pair}")
|
||||
result_pair = (
|
||||
original_call,
|
||||
ToolResult(output=f"Crash: {result_pair}").as_error(),
|
||||
)
|
||||
|
||||
tool_call_result = cast(tuple[ToolCallPart, ToolResult], result_pair)
|
||||
_, tool_result = tool_call_result
|
||||
tool_messages.append(
|
||||
LLMMessage.tool_response(
|
||||
tool_call_id=original_call.id,
|
||||
function_name=func_name,
|
||||
result=tool_result.output,
|
||||
)
|
||||
)
|
||||
return tool_messages
|
||||
|
||||
|
||||
class ToolExecutionPolicy:
|
||||
"""
|
||||
工具执行策略 (Strategy Pattern)。
|
||||
负责解析工具私有配置与系统全局配置，决定最大重试次数、Fallback 路由目标等流转行为。
|
||||
"""
|
||||
|
||||
def __init__(self, tool: BaseTool, global_max_retries: int = 0):
|
||||
self.tool = tool
|
||||
self.settings: ToolOptions = getattr(tool, "settings", ToolOptions())
|
||||
self.metadata: dict[str, Any] = (
|
||||
self.settings.metadata if self.settings else getattr(tool, "metadata", {})
|
||||
)
|
||||
self.global_max_retries = global_max_retries
|
||||
|
||||
@property
|
||||
def max_retries(self) -> int:
|
||||
"""
|
||||
计算当前工具的绝对最大重试次数。
|
||||
优先使用工具级配置 (ToolOptions.max_retries)，如果未设置，则使用全局配置。
|
||||
由于重试机制是保证 Agent 稳定性的防线，
|
||||
即使全局为 0，底层默认也会给予至少 1 次的机会。
|
||||
"""
|
||||
tool_retries = getattr(self.settings, "max_retries", None)
|
||||
if tool_retries is not None:
|
||||
return tool_retries
|
||||
return max(self.global_max_retries, 1)
|
||||
|
||||
|
||||
class ToolRunner(ABC):
|
||||
"""
|
||||
工具运行器基类协议。
|
||||
负责将参数请求物理落实为目标执行。
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
async def run(
|
||||
self, tool: BaseTool, context: RunContext, **kwargs: Any
|
||||
) -> ToolResult:
|
||||
pass
|
||||
|
||||
|
||||
class NativeToolRunner(ToolRunner):
|
||||
"""
|
||||
原生 Python 函数工具运行器。
|
||||
负责处理依赖注入 (DI)、异步包装、生成器流式收集以及框架级的多模态消息转换。
|
||||
"""
|
||||
|
||||
async def run(
|
||||
self, tool: BaseTool, context: RunContext, **kwargs: Any
|
||||
) -> ToolResult:
|
||||
target_func = tool.get_execute_target()
|
||||
signature_target = tool.get_signature_target()
|
||||
|
||||
if not target_func:
|
||||
return ToolResult(output="Error: 未找到有效的执行目标(run 方法)").as_error()
|
||||
|
||||
call_kwargs = dict(kwargs)
|
||||
|
||||
try:
|
||||
target_call_kwargs = await DependencyInjector.resolve_all(
|
||||
sig=inspect.signature(signature_target),
|
||||
call_kwargs=dict(call_kwargs),
|
||||
context=context,
|
||||
)
|
||||
except ValueError as e:
|
||||
logger.error(f"工具 {tool.name} 依赖注入失败: {e}", e=e)
|
||||
raise ToolFatalError(f"框架依赖注入失败: {e}")
|
||||
|
||||
is_async_gen = getattr(
|
||||
target_func, "_is_async_gen", False
|
||||
) or inspect.isasyncgenfunction(target_func)
|
||||
if is_async_gen:
|
||||
res = None
|
||||
async for chunk in target_func(**target_call_kwargs):
|
||||
if isinstance(chunk, ToolResult):
|
||||
res = chunk
|
||||
else:
|
||||
from zhenxun.services.ai.tools.models import ToolResultChunk
|
||||
|
||||
chunk_obj = (
|
||||
chunk
|
||||
if isinstance(chunk, ToolResultChunk)
|
||||
else ToolResultChunk(content=str(chunk))
|
||||
)
|
||||
is_silent = (
|
||||
getattr(tool.settings, "silent", False)
|
||||
if tool and hasattr(tool, "settings")
|
||||
else False
|
||||
)
|
||||
if context.run.event_bus and not is_silent:
|
||||
await context.run.event_bus.emit(
|
||||
ToolStreamChunkEvent(
|
||||
tool_name=tool.name,
|
||||
content=chunk_obj.content,
|
||||
metadata=chunk_obj.metadata,
|
||||
)
|
||||
)
|
||||
if res is None:
|
||||
res = ToolResult(output="Stream finished successfully.")
|
||||
else:
|
||||
res = await target_func(**target_call_kwargs)
|
||||
|
||||
if isinstance(res, ToolResult):
|
||||
final_result = res
|
||||
else:
|
||||
if str(type(res)).find("Message") != -1:
|
||||
from zhenxun.services.ai.message_builder import MessageBuilder
|
||||
|
||||
uni_msg = (
|
||||
MessageBuilder.message_to_unimessage(res)
|
||||
if isinstance(res, PlatformMessage)
|
||||
else res
|
||||
)
|
||||
parts = await MessageBuilder.unimsg_to_llm_parts(uni_msg)
|
||||
if context and context.run.event_bus:
|
||||
await context.run.event_bus.emit(UserCustomEvent(display=uni_msg))
|
||||
final_result = ToolResult(output=parts)
|
||||
else:
|
||||
final_result = ToolResult(output=res)
|
||||
|
||||
return final_result
|
||||
|
||||
|
||||
from zhenxun.services.ai.tools.core.tool import register_tool_runner
|
||||
|
||||
register_tool_runner(NativeToolRunner)
|
||||
Reference in New Issue
Block a user