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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>
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co-authored by
webjoin111
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webjoin111
pre-commit-ci[bot]
parent
bdc1374848
commit
80fc5b86a7
@@ -0,0 +1,341 @@
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from __future__ import annotations
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from collections.abc import Callable
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import graphlib
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from typing import TYPE_CHECKING, Any
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from zhenxun.services.ai.core.messages import ChatRequest, ChatResponse
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from zhenxun.services.ai.core.options import GenerationConfig
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from .base import (
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AbstractCapability,
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CapabilityRef,
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WrapModelRequestHandler,
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WrapRunHandler,
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WrapToolExecuteHandler,
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WrapToolValidateHandler,
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)
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if TYPE_CHECKING:
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from zhenxun.services.ai.core.models import LLMContext
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from zhenxun.services.ai.run import AgentRunResult, RunContext
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def sort_capabilities(caps: list["AbstractCapability"]) -> list["AbstractCapability"]:
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"""使用标准库 graphlib.TopologicalSorter 实现拦截器拓扑排序，解决执行顺序冲突"""
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if len(caps) <= 1:
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return caps
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ts = graphlib.TopologicalSorter()
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n = len(caps)
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for i in range(n):
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ts.add(i)
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orderings = [c.get_ordering() for c in caps]
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leaf_types = [{type(c)} for c in caps]
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def _ref_matches(
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ref: CapabilityRef, types: set[type], inst: AbstractCapability
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) -> bool:
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if isinstance(ref, type):
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return any(issubclass(t, ref) for t in types)
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return inst is ref
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all_types = set().union(*leaf_types)
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for i, o in enumerate(orderings):
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if o and o.requires:
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for req in o.requires:
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if not any(issubclass(t, req) for t in all_types):
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raise ValueError(
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f"Capability '{type(caps[i]).__name__}' 依赖 '{req.__name__}' "
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f"但未在管线中找到该组件。"
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)
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outermost = {i for i, o in enumerate(orderings) if o and o.position == "outermost"}
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innermost = {i for i, o in enumerate(orderings) if o and o.position == "innermost"}
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for oi in outermost:
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for j in range(n):
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if j != oi and j not in outermost:
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ts.add(j, oi)
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for ii in innermost:
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for j in range(n):
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if j != ii and j not in innermost:
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ts.add(ii, j)
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for i, o in enumerate(orderings):
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if not o:
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continue
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for ref in o.wraps:
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for j in range(n):
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if i != j and _ref_matches(ref, leaf_types[j], caps[j]):
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ts.add(j, i)
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for ref in o.wrapped_by:
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for j in range(n):
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if i != j and _ref_matches(ref, leaf_types[j], caps[j]):
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ts.add(i, j)
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try:
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order = list(ts.static_order())
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except graphlib.CycleError:
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raise ValueError(
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"Capability 拓扑排序失败，存在循环依赖约束。"
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"请检查 wraps 或 wrapped_by 的配置。"
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)
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return [caps[i] for i in order]
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class CombinedCapability(AbstractCapability):
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"""
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组合能力容器。
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将多个 Capability 按顺序融合成一个复合的洋葱模型，
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处理生命周期的正序/倒序和链式调用。
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"""
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def __init__(self, capabilities: list[AbstractCapability]):
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flat = []
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for c in capabilities:
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if isinstance(c, CombinedCapability):
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flat.extend(c.capabilities)
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else:
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flat.append(c)
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deduped = []
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seen = set()
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for c in flat:
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if id(c) not in seen:
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seen.add(id(c))
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deduped.append(c)
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self.capabilities = sort_capabilities(deduped)
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async def for_run(self, context: RunContext) -> "AbstractCapability":
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new_caps = []
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changed = False
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for cap in self.capabilities:
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new_cap = await cap.for_run(context)
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new_caps.append(new_cap)
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if new_cap is not cap:
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changed = True
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if changed:
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return CombinedCapability(new_caps)
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return self
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async def get_generation_config(
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self, context: RunContext
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) -> GenerationConfig | None:
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final_config = None
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for cap in self.capabilities:
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cap_config = await cap.get_generation_config(context)
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if cap_config:
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if final_config is None:
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final_config = cap_config
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else:
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final_config = final_config.merge_with(cap_config)
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return final_config
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async def get_system_prompts(self, context: RunContext) -> list[str]:
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prompts = []
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for cap in self.capabilities:
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prompts.extend(await cap.get_system_prompts(context))
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return prompts
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async def get_tools(self, context: RunContext) -> list[Any]:
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tools = []
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for cap in self.capabilities:
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tools.extend(await cap.get_tools(context))
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return tools
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async def prepare_tools(
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self, context: RunContext, tool_defs: list[Any]
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) -> list[Any]:
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current_defs = list(tool_defs)
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for cap in self.capabilities:
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res = await cap.prepare_tools(context, current_defs)
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if res is not None:
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current_defs = res
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return current_defs
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async def wrap_run(
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self, context: RunContext, handler: WrapRunHandler
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) -> "AgentRunResult[Any]":
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chain = handler
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for cap in reversed(self.capabilities):
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chain = _make_wrap_link(cap, "wrap_run", context, {}, chain, None)
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return await chain()
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async def wrap_model_request(
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self,
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context: RunContext,
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llm_context: LLMContext[ChatRequest, ChatResponse],
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handler: WrapModelRequestHandler,
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) -> ChatResponse:
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chain = handler
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for cap in reversed(self.capabilities):
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chain = _make_wrap_link(
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cap, "wrap_model_request", context, {}, chain, "llm_context"
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)
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return await chain(llm_context)
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async def wrap_tool_validate(
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self,
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context: RunContext,
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tool_name: str,
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args: str | dict[str, Any],
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handler: WrapToolValidateHandler,
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) -> dict[str, Any]:
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chain = handler
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for cap in reversed(self.capabilities):
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chain = _make_wrap_link(
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cap,
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"wrap_tool_validate",
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context,
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{"tool_name": tool_name},
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chain,
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"args",
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)
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return await chain(args)
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async def wrap_tool_execute(
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self,
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context: RunContext,
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tool_name: str,
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arguments: dict[str, Any],
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handler: WrapToolExecuteHandler,
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) -> Any:
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chain = handler
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for cap in reversed(self.capabilities):
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chain = _make_wrap_link(
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cap,
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"wrap_tool_execute",
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context,
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{"tool_name": tool_name},
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chain,
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"arguments",
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)
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return await chain(arguments)
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def _make_wrap_link(
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cap: AbstractCapability,
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hook_name: str,
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ctx: RunContext,
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static_kwargs: dict[str, Any],
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inner_handler: Callable[..., Any],
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handler_arg: str | None,
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) -> Callable[..., Any]:
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"""构建洋葱模型中间件链的单一闭包节点。"""
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frozen_kwargs = dict(static_kwargs)
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if handler_arg:
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async def wrapper(value: Any) -> Any:
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kw = dict(frozen_kwargs)
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kw[handler_arg] = value
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hook_method = getattr(cap, hook_name)
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return await hook_method(ctx, handler=inner_handler, **kw)
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return wrapper
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async def wrapper_no_arg() -> Any:
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hook_method = getattr(cap, hook_name)
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return await hook_method(ctx, handler=inner_handler, **frozen_kwargs)
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return wrapper_no_arg
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class DynamicCapability(AbstractCapability):
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"""动态能力注入：允许在运行时基于上下文生成真正的 Capability"""
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def __init__(self, capability_func: Callable):
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self.capability_func = capability_func
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@classmethod
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def get_serialization_name(cls) -> str | None:
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return None
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async def for_run(self, context: RunContext) -> "AbstractCapability":
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from nonebot.utils import is_coroutine_callable
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if is_coroutine_callable(self.capability_func):
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cap = await self.capability_func(context)
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else:
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cap = self.capability_func(context)
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if cap is None:
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return self
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return await cap.for_run(context)
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class WrapperCapability(AbstractCapability):
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"""
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代理包装能力基类 (Decorator Pattern)。
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默认将所有生命周期钩子透明透传给内部包裹的 (wrapped) 实例。
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"""
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def __init__(self, wrapped: AbstractCapability):
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self.wrapped = wrapped
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@classmethod
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def get_serialization_name(cls) -> str | None:
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return None
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async def for_run(self, context: RunContext) -> "AbstractCapability":
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new_wrapped = await self.wrapped.for_run(context)
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if new_wrapped is self.wrapped:
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return self
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import copy
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new_self = copy.copy(self)
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new_self.wrapped = new_wrapped
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return new_self
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async def get_generation_config(
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self, context: RunContext
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) -> GenerationConfig | None:
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return await self.wrapped.get_generation_config(context)
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async def get_system_prompts(self, context: RunContext) -> list[str]:
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return await self.wrapped.get_system_prompts(context)
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async def get_tools(self, context: RunContext) -> list[Any]:
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return await self.wrapped.get_tools(context)
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async def prepare_tools(
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self, context: RunContext, tool_defs: list[Any]
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) -> list[Any]:
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return await self.wrapped.prepare_tools(context, tool_defs)
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async def wrap_run(
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self, context: RunContext, handler: WrapRunHandler
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) -> "AgentRunResult[Any]":
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return await self.wrapped.wrap_run(context, handler)
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async def wrap_model_request(
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self,
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context: RunContext,
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llm_context: LLMContext[ChatRequest, ChatResponse],
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handler: WrapModelRequestHandler,
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) -> ChatResponse:
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return await self.wrapped.wrap_model_request(context, llm_context, handler)
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async def wrap_tool_validate(
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self,
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context: RunContext,
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tool_name: str,
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args: str | dict[str, Any],
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handler: WrapToolValidateHandler,
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) -> dict[str, Any]:
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return await self.wrapped.wrap_tool_validate(context, tool_name, args, handler)
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async def wrap_tool_execute(
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self,
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context: RunContext,
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tool_name: str,
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arguments: dict[str, Any],
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handler: WrapToolExecuteHandler,
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) -> Any:
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return await self.wrapped.wrap_tool_execute(
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context, tool_name, arguments, handler
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)
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