✨ 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>
2026-10-19 11:10:10 +08:00 · 2026-07-03 08:53:56 +08:00
parent bdc1374848
commit 80fc5b86a7
223 changed files with 41885 additions and 10006 deletions
@@ -0,0 +1,167 @@
+"""
+LLM 适配器工厂类
+"""
+
+from __future__ import annotations
+
+import fnmatch
+from typing import Any, ClassVar
+
+import httpx
+
+from zhenxun.services.ai.core.exceptions import ConfigurationException
+from zhenxun.services.ai.core.models import ModelIdentity
+
+from .base import BaseAdapter, RequestData
+
+
+class LLMAdapterFactory:
+    """适配器注册与按 API 类型分发的统一入口。"""
+
+    _adapters: ClassVar[dict[str, BaseAdapter]] = {}
+    _api_type_mapping: ClassVar[dict[str, str]] = {}
+
+    @classmethod
+    def initialize(cls) -> None:
+        """初始化默认适配器"""
+        if cls._adapters:
+            return
+
+        from .deepseek import DeepSeekAdapter
+        from .doubao import DoubaoAdapter
+        from .gemini import GeminiAdapter
+        from .glm import GLMAdapter
+        from .jina import JinaAdapter
+        from .mimo import MiMoAdapter
+        from .minimax import MiniMaxAdapter
+        from .openai import OpenAIAdapter
+        from .openrouter import OpenRouterAdapter
+
+        cls.register_adapter(OpenAIAdapter())
+        cls.register_adapter(OpenRouterAdapter())
+        cls.register_adapter(DeepSeekAdapter())
+        cls.register_adapter(JinaAdapter())
+        cls.register_adapter(GeminiAdapter())
+        cls.register_adapter(GLMAdapter())
+        cls.register_adapter(SmartAdapter())
+        cls.register_adapter(MiMoAdapter())
+        cls.register_adapter(MiniMaxAdapter())
+        cls.register_adapter(DoubaoAdapter())
+
+    @classmethod
+    def register_adapter(cls, adapter: BaseAdapter) -> None:
+        """注册适配器"""
+        adapter_key = adapter.api_type
+        cls._adapters[adapter_key] = adapter
+
+        for api_type in adapter.supported_api_types:
+            cls._api_type_mapping[api_type] = adapter_key
+
+    @classmethod
+    def get_adapter(cls, api_type: str) -> BaseAdapter:
+        """获取适配器"""
+        cls.initialize()
+
+        adapter_key = cls._api_type_mapping.get(api_type)
+        if not adapter_key:
+            raise ConfigurationException(
+                f"不支持的API类型: {api_type}",
+                details={
+                    "api_type": api_type,
+                    "supported_types": list(cls._api_type_mapping.keys()),
+                },
+            )
+
+        return cls._adapters[adapter_key]
+
+    @classmethod
+    def list_supported_types(cls) -> list[str]:
+        """列出所有支持的API类型"""
+        cls.initialize()
+        return list(cls._api_type_mapping.keys())
+
+    @classmethod
+    def list_adapters(cls) -> dict[str, BaseAdapter]:
+        """列出所有注册的适配器"""
+        cls.initialize()
+        return cls._adapters.copy()
+
+
+def get_adapter_for_api_type(api_type: str) -> BaseAdapter:
+    """按 API 类型获取适配器实例。"""
+    return LLMAdapterFactory.get_adapter(api_type)
+
+
+def register_adapter(adapter: BaseAdapter) -> None:
+    """向工厂注册新的适配器实例。"""
+    LLMAdapterFactory.register_adapter(adapter)
+
+
+class SmartAdapter(BaseAdapter):
+    """
+    智能路由适配器。
+    本身不处理序列化，而是根据规则委托给 OpenAIAdapter 或 GeminiAdapter。
+    """
+
+    @property
+    def log_sanitization_context(self) -> str:
+        """返回智能路由适配器的默认日志清洗上下文。"""
+        return "openai_request"
+
+    _ROUTING_RULES: ClassVar[list[tuple[str, str]]] = [
+        ("*nano-banana*", "gemini"),
+        ("*gemini*", "gemini"),
+        ("*deepseek*", "deepseek"),
+        ("*minimax*", "minimax"),
+        ("*gpt*", "openai_responses"),
+    ]
+    _DEFAULT_API_TYPE: ClassVar[str] = "openai"
+
+    def __init__(self):
+        """初始化模型名到目标适配器的路由缓存。"""
+        self._adapter_cache: dict[str, BaseAdapter] = {}
+
+    @property
+    def api_type(self) -> str:
+        """适配器主类型标识。"""
+        return "smart"
+
+    @property
+    def supported_api_types(self) -> list[str]:
+        """当前适配器支持的 API 类型列表。"""
+        return ["smart"]
+
+    def _get_delegate_adapter(self, identity: ModelIdentity) -> BaseAdapter:
+        """
+        核心路由逻辑：决定使用哪个适配器 (带缓存)
+        """
+        if identity.api_type and identity.api_type != "smart":
+            return get_adapter_for_api_type(identity.api_type)
+
+        model_name = identity.model_name
+        if model_name in self._adapter_cache:
+            return self._adapter_cache[model_name]
+
+        target_api_type = self._DEFAULT_API_TYPE
+        model_name_lower = model_name.lower()
+
+        for pattern, api_type in self._ROUTING_RULES:
+            if fnmatch.fnmatch(model_name_lower, pattern):
+                target_api_type = api_type
+                break
+
+        adapter = get_adapter_for_api_type(target_api_type)
+        self._adapter_cache[model_name] = adapter
+        return adapter
+
+    async def prepare_payload(
+        self, identity: ModelIdentity, api_key: str, request: Any
+    ) -> RequestData:
+        adapter = self._get_delegate_adapter(identity)
+        return await adapter.prepare_payload(identity, api_key, request)
+
+    async def parse_payload(
+        self, identity: ModelIdentity, request: Any, raw_response: httpx.Response
+    ) -> Any:
+        adapter = self._get_delegate_adapter(identity)
+        return await adapter.parse_payload(identity, request, raw_response)