x

api/endpoints.py

@@ -13,6 +13,7 @@ from schemas.tool_input import ToolInput
 from schemas.tool_output import ToolOutput
 from schemas.chat_message_response import ChatMessageResponseDTO
 from schemas.chat_message_request import ChatMessageRequestDTO
+from schemas.super_agent import SuperAgentRequest, SuperAgentResponse, SuperAgentStreamEvent
 from workflows.workflow_manager import WorkflowType
 from api.dependencies import get_workflow_manager, get_nacos_manager, get_service_config, get_tool_router, get_prompt_manager
 from services.app_errors import AppError, ErrorCode
@@ -190,6 +191,54 @@ def run_workflow_stream(payload: ChatMessageRequestDTO, workflow_manager=Depends
     return StreamingResponse(event_stream(), media_type="text/event-stream")
 
 
+@router.get("/api/workflows/list")
+def list_workflows(workflow_manager=Depends(get_workflow_manager)):
+    """列出所有可用工作流"""
+    workflows = workflow_manager.get_available_workflows()
+    result = []
+    for name in workflows:
+        info = workflow_manager.get_workflow_info(name)
+        if info:
+            result.append(info)
+    return {"workflows": result}
+
+
+@router.get("/api/workflows/{workflow_name}")
+def get_workflow_detail(workflow_name: str, workflow_manager=Depends(get_workflow_manager)):
+    """获取工作流详情"""
+    info = workflow_manager.get_workflow_info(workflow_name)
+    if not info:
+        raise HTTPException(status_code=404, detail=f"工作流不存在: {workflow_name}")
+    return info
+
+
+@router.get("/api/tools/list")
+def list_tools(tool_router=Depends(get_tool_router)):
+    """列出所有可用工具"""
+    tools = tool_router.list_tools()
+    result = []
+    for name in tools:
+        info = tool_router.get_tool_info(name)
+        if info:
+            result.append(info)
+    return {"tools": result}
+
+
+@router.get("/api/tools/{tool_name}")
+def get_tool_detail(tool_name: str, tool_router=Depends(get_tool_router)):
+    """获取工具详情"""
+    info = tool_router.get_tool_info(tool_name)
+    if not info:
+        raise HTTPException(status_code=404, detail=f"工具不存在: {tool_name}")
+    return info
+
+
+@router.get("/api/tools/stats")
+def get_tools_stats(tool_router=Depends(get_tool_router)):
+    """获取工具执行统计"""
+    return tool_router.get_all_stats()
+
+
 @router.post("/api/tools/execute", response_model=ToolOutput)
 def run_tool(payload: ToolInput, tool_router=Depends(get_tool_router)):
     result = tool_router.call(payload.tool_name, payload.payload)
@@ -251,3 +300,135 @@ def update_sql_gen():
         return {"ok": True, "result": result}
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/api/super-agent/query", response_model=SuperAgentResponse)
+def super_agent_query(payload: SuperAgentRequest, workflow_manager=Depends(get_workflow_manager)):
+    """Super Agent 同步查询接口"""
+    trace_id = uuid.uuid4().hex
+    slog = get_structured_logger()
+    slog.log("INFO", "super_agent.query.start", trace_id, {
+        "query": payload.query[:100],
+        "workflow_type": payload.workflow_type,
+        "user_id": payload.user_id,
+    })
+
+    conversation_id = payload.conversation_id or uuid.uuid4().hex
+
+    try:
+        workflow_type = _resolve_workflow_type(payload.workflow_type)
+    except Exception as e:
+        slog.log("ERROR", "super_agent.query.invalid_type", trace_id, error_code=ErrorCode.INVALID_WORKFLOW_TYPE.value)
+        return SuperAgentResponse(
+            conversation_id=conversation_id,
+            workflow_type=payload.workflow_type,
+            status="error",
+            error=f"不支持的工作流类型: {payload.workflow_type}",
+        )
+
+    try:
+        result = workflow_manager.execute_workflow(
+            workflow_type=workflow_type,
+            user_input=payload.query,
+            session_id=conversation_id,
+        )
+
+        context = (result.get("result") or {}).get("context") or {}
+        sql_text = context.get("final_sql")
+        sr_api_result = context.get("sr_api_result")
+
+        slog.log("INFO", "super_agent.query.success", trace_id, {
+            "conversation_id": conversation_id,
+            "has_sql": bool(sql_text),
+            "has_result": bool(sr_api_result),
+        })
+
+        return SuperAgentResponse(
+            conversation_id=conversation_id,
+            workflow_type=workflow_type.value,
+            status="success",
+            sql=sql_text,
+            result=str(sr_api_result) if sr_api_result else None,
+            metadata={"trace_id": trace_id},
+        )
+
+    except Exception as e:
+        slog.log("ERROR", "super_agent.query.failed", trace_id, error_code=ErrorCode.INTERNAL_ERROR.value, payload={"error": str(e)})
+        return SuperAgentResponse(
+            conversation_id=conversation_id,
+            workflow_type=payload.workflow_type,
+            status="error",
+            error=str(e),
+            metadata={"trace_id": trace_id},
+        )
+
+
+@router.post("/api/super-agent/stream")
+def super_agent_stream(payload: SuperAgentRequest, workflow_manager=Depends(get_workflow_manager)):
+    """Super Agent 流式查询接口"""
+    trace_id = uuid.uuid4().hex
+    slog = get_structured_logger()
+    stream_cfg = Config.get_section("stream")
+    progress_interval = float(stream_cfg.get("progress_interval", 0.3))
+
+    conversation_id = payload.conversation_id or uuid.uuid4().hex
+
+    def _build_sse_event(event: str, data: str) -> str:
+        dto = SuperAgentStreamEvent(
+            conversation_id=conversation_id,
+            event=event,
+            data=data,
+            timestamp=int(time.time() * 1000),
+        )
+        return f"event: {event}\ndata: {json.dumps(dto.model_dump(), ensure_ascii=False)}\n\n"
+
+    async def event_stream():
+        try:
+            slog.log("INFO", "super_agent.stream.start", trace_id, {
+                "query": payload.query[:100],
+                "user_id": payload.user_id,
+            })
+
+            workflow_type = _resolve_workflow_type(payload.workflow_type)
+
+            result = await asyncio.to_thread(
+                workflow_manager.execute_workflow,
+                workflow_type,
+                payload.query,
+                conversation_id,
+                skip_sr_api=True,
+            )
+
+            context = (result.get("result") or {}).get("context") or {}
+            sql_text = context.get("final_sql")
+
+            if not sql_text:
+                slog.log("ERROR", "super_agent.stream.sql_failed", trace_id, error_code=ErrorCode.SQL_GENERATION_FAILED.value)
+                yield _build_sse_event("error", "SQL 生成失败")
+                yield _build_sse_event("done", "")
+                return
+
+            yield _build_sse_event("sql_generated", sql_text)
+
+            yield _build_sse_event("sql_executing", "")
+
+            tool = SrApiQueryTool()
+            task = asyncio.create_task(
+                asyncio.to_thread(tool.run, json.dumps({"sql": sql_text}, ensure_ascii=False))
+            )
+
+            while not task.done():
+                yield _build_sse_event("sql_executing", "")
+                await asyncio.sleep(progress_interval)
+
+            sql_result = await task
+            slog.log("INFO", "super_agent.stream.success", trace_id, {"result_len": len(str(sql_result))})
+            yield _build_sse_event("result", str(sql_result))
+
+        except Exception as e:
+            slog.log("ERROR", "super_agent.stream.failed", trace_id, error_code=ErrorCode.INTERNAL_ERROR.value, payload={"error": str(e)})
+            yield _build_sse_event("error", str(e))
+
+        yield _build_sse_event("done", "")
+
+    return StreamingResponse(event_stream(), media_type="text/event-stream")

config/config.ini

@@ -35,28 +35,9 @@ cache_ttl = 600
 table_retrieval_dataset_id = 9945baf512ea11f18ccb6a681b3130b2
 sql_gen_dataset_id = ee68f53a12ec11f18e436a681b3130b2
 
-[redis]
-enabled = true
-host = led-redis.lenovo.com
-port = 30398
-password = bgs123456
-database = 0
-sql_prompt_ttl = 600
-
 [stream]
 progress_interval = 0.3
 
-[logging_mysql]
-enabled = false
-host = 127.0.0.1
-port = 3306
-user = root
-password =
-database = more_dots
-table = structured_logs
-connect_timeout = 5
-
-
 [app]
 service_name = local-model-streaming-api
 host = 0.0.0.0

config/config.ini.example

@@ -38,29 +38,10 @@ retrieval_top_k = 3
 table_retrieval_dataset_id =
 sql_gen_dataset_id =
 
-[redis]
-# 是否启用 Redis 缓存（用于 sql_gen_prompts）
-enabled = false
-url = redis://localhost:6379/0
-db = 0
-# SQL 提示词缓存过期秒数
-sql_prompt_ttl = 600
-
 [stream]
 # /api/workflows/stream 进度事件间隔（秒）
 progress_interval = 0.3
 
-[logging_mysql]
-# 是否启用结构化日志写入 MySQL
-enabled = false
-host = 127.0.0.1
-port = 3306
-user = root
-password =
-database = more_dots
-table = structured_logs
-connect_timeout = 5
-
 [nacos]
 # 是否启用 Nacos 注册
 enabled = false

core/__init__.py

@@ -0,0 +1,52 @@
+"""
+核心模块 - 提供扩展性基础设施
+
+包含:
+- Registry: 注册机制
+- State: 增强的状态管理
+- Provider: LLM Provider 抽象
+- Response: 统一响应格式
+"""
+
+from .registry import (
+    BaseRegistry,
+    NodeRegistry,
+    ToolRegistry,
+    WorkflowRegistry,
+    ProviderRegistry,
+    RegistryEntry,
+    ToolMetadata,
+    WorkflowMetadata,
+    ProviderMetadata,
+    register_tool,
+    register_workflow,
+    register_provider,
+)
+from .state import AgentState, StateContext
+from .providers import LLMProvider, LLMFactory
+from .response import ApiResponse, StreamEvent
+
+__all__ = [
+    # Registry
+    "BaseRegistry",
+    "NodeRegistry",
+    "ToolRegistry",
+    "WorkflowRegistry",
+    "ProviderRegistry",
+    "RegistryEntry",
+    "ToolMetadata",
+    "WorkflowMetadata",
+    "ProviderMetadata",
+    "register_tool",
+    "register_workflow",
+    "register_provider",
+    # State
+    "AgentState",
+    "StateContext",
+    # Provider
+    "LLMProvider",
+    "LLMFactory",
+    # Response
+    "ApiResponse",
+    "StreamEvent",
+]

core/providers.py

@@ -0,0 +1,218 @@
+"""
+LLM Provider 抽象层
+
+支持多种 LLM 提供商的统一接口
+"""
+
+from typing import Any, Dict, List, Optional, Protocol, Type, runtime_checkable
+from abc import ABC, abstractmethod
+import logging
+
+from langchain_core.language_models import BaseChatModel
+
+from config import Config
+from .registry import ProviderRegistry, register_provider
+
+logger = logging.getLogger(__name__)
+
+
+@runtime_checkable
+class LLMProvider(Protocol):
+    """
+    LLM Provider 协议
+    
+    定义所有 LLM 提供商必须实现的接口
+    """
+    
+    @property
+    def name(self) -> str:
+        """Provider 名称"""
+        ...
+    
+    @property
+    def supported_models(self) -> List[str]:
+        """支持的模型列表"""
+        ...
+    
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        """
+        创建 LLM 模型实例
+        
+        Args:
+            config: 模型配置
+            
+        Returns:
+            BaseChatModel 实例
+        """
+        ...
+
+
+class BaseLLMProvider(ABC):
+    """LLM Provider 基类"""
+    
+    def __init__(self, name: str, supported_models: List[str]):
+        self._name = name
+        self._supported_models = supported_models
+    
+    @property
+    def name(self) -> str:
+        return self._name
+    
+    @property
+    def supported_models(self) -> List[str]:
+        return self._supported_models
+    
+    @abstractmethod
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        """子类实现具体的模型创建逻辑"""
+        pass
+
+
+class OpenAICompatibleProvider(BaseLLMProvider):
+    """
+    OpenAI 兼容的 Provider
+    
+    支持所有兼容 OpenAI API 的服务：
+    - OpenAI
+    - Azure OpenAI
+    - 本地部署的兼容服务
+    - 国产大模型（通义千问、文心一言等）
+    """
+    
+    def __init__(self, name: str = "openai_compatible", supported_models: Optional[List[str]] = None):
+        super().__init__(name, supported_models or [])
+    
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        from langchain_openai import ChatOpenAI
+        
+        return ChatOpenAI(
+            model=config.get("model_name") or config.get("MODEL_NAME"),
+            openai_api_key=config.get("api_key") or config.get("OPENAI_API_KEY"),
+            openai_api_base=config.get("base_url") or config.get("URL"),
+            temperature=config.get("temperature", 0.7),
+            max_tokens=config.get("max_tokens"),
+            timeout=config.get("timeout", 30),
+            max_retries=config.get("max_retries", 3),
+        )
+
+
+class AzureOpenAIProvider(BaseLLMProvider):
+    """Azure OpenAI Provider"""
+    
+    def __init__(self):
+        super().__init__("azure", ["gpt-4", "gpt-35-turbo"])
+    
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        from langchain_openai import AzureChatOpenAI
+        
+        return AzureChatOpenAI(
+            azure_deployment=config.get("deployment_name"),
+            openai_api_version=config.get("api_version", "2024-02-15-preview"),
+            azure_endpoint=config.get("endpoint"),
+            openai_api_key=config.get("api_key"),
+            temperature=config.get("temperature", 0.7),
+        )
+
+
+class LLMFactory:
+    """
+    LLM 工厂类
+    
+    统一管理 LLM 实例的创建，支持：
+    - 多种 Provider
+    - 配置驱动
+    - 单例缓存
+    """
+    
+    _instances: Dict[str, BaseChatModel] = {}
+    _default_provider: str = "openai_compatible"
+    
+    @classmethod
+    def register_provider(cls, name: str, provider: LLMProvider) -> None:
+        """注册 Provider"""
+        ProviderRegistry._entries[name] = type(
+            "RegistryEntry",
+            (),
+            {"instance": provider, "metadata": {}}
+        )()
+        logger.info(f"Registered LLM provider: {name}")
+    
+    @classmethod
+    def get_provider(cls, name: str) -> Optional[LLMProvider]:
+        """获取 Provider"""
+        entry = ProviderRegistry.get_entry(name)
+        if entry:
+            return entry.instance
+        return None
+    
+    @classmethod
+    def create(
+        cls,
+        provider: Optional[str] = None,
+        config: Optional[Dict[str, Any]] = None,
+        model_section: Optional[str] = None,
+        use_cache: bool = True,
+    ) -> BaseChatModel:
+        """
+        创建 LLM 实例
+        
+        Args:
+            provider: Provider 名称，默认使用 openai_compatible
+            config: 模型配置
+            model_section: 配置文件中的模型段名
+            use_cache: 是否使用缓存
+            
+        Returns:
+            BaseChatModel 实例
+        """
+        provider_name = provider or cls._default_provider
+        
+        if model_section:
+            config = Config.get_section(model_section)
+            cache_key = f"{provider_name}:{model_section}"
+        else:
+            config = config or {}
+            cache_key = f"{provider_name}:{hash(frozenset(config.items()))}"
+        
+        if use_cache and cache_key in cls._instances:
+            return cls._instances[cache_key]
+        
+        provider_instance = cls.get_provider(provider_name)
+        
+        if provider_instance is None:
+            provider_instance = OpenAICompatibleProvider()
+        
+        model = provider_instance.create_model(config)
+        
+        if use_cache:
+            cls._instances[cache_key] = model
+        
+        return model
+    
+    @classmethod
+    def clear_cache(cls) -> None:
+        """清空缓存"""
+        cls._instances.clear()
+    
+    @classmethod
+    def list_providers(cls) -> List[str]:
+        """列出所有注册的 Provider"""
+        return ProviderRegistry.list_names()
+
+
+OpenAICompatibleProvider()
+ProviderRegistry._entries["openai_compatible"] = type(
+    "RegistryEntry",
+    (),
+    {"instance": OpenAICompatibleProvider(), "metadata": {}}
+)()
+
+
+def create_chat_model(model_section: Optional[str] = None) -> BaseChatModel:
+    """
+    创建聊天模型（兼容现有代码）
+    
+    这是现有 llm_factory.create_chat_model 的替代实现，
+    使用新的 Provider 架构但保持接口兼容
+    """
+    return LLMFactory.create(model_section=model_section)

core/registry.py

@@ -0,0 +1,222 @@
+"""
+核心注册机制模块
+
+提供统一的注册器模式，支持动态扩展：
+- NodeRegistry: 节点注册器
+- ToolRegistry: 工具注册器  
+- WorkflowRegistry: 工作流注册器
+- ProviderRegistry: LLM Provider 注册器
+"""
+
+from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Protocol, runtime_checkable
+from dataclasses import dataclass, field
+from enum import Enum
+import time
+
+
+T = TypeVar("T")
+
+
+@runtime_checkable
+class Registerable(Protocol[T]):
+    """可注册对象的协议"""
+    name: str
+    description: str = ""
+
+
+@dataclass
+class RegistryEntry:
+    """注册条目"""
+    name: str
+    instance: Any
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    registered_at: float = field(default_factory=time.time)
+
+
+class BaseRegistry:
+    """基础注册器"""
+    
+    _entries: Dict[str, RegistryEntry] = {}
+    
+    @classmethod
+    def register(cls, name: str, metadata: Optional[Dict[str, Any]] = None):
+        """
+        注册装饰器
+        
+        Usage:
+            @NodeRegistry.register("my_node", metadata={"category": "processing"})
+            def my_node(state: AgentState) -> AgentState:
+                ...
+        """
+        def decorator(obj: T) -> T:
+            entry = RegistryEntry(
+                name=name,
+                instance=obj,
+                metadata=metadata or {},
+            )
+            cls._entries[name] = entry
+            return obj
+        return decorator
+    
+    @classmethod
+    def get(cls, name: str) -> Optional[Any]:
+        """获取注册的对象"""
+        entry = cls._entries.get(name)
+        return entry.instance if entry else None
+    
+    @classmethod
+    def get_entry(cls, name: str) -> Optional[RegistryEntry]:
+        """获取注册条目（包含元数据）"""
+        return cls._entries.get(name)
+    
+    @classmethod
+    def list_names(cls) -> List[str]:
+        """列出所有注册名称"""
+        return list(cls._entries.keys())
+    
+    @classmethod
+    def list_entries(cls) -> List[RegistryEntry]:
+        """列出所有注册条目"""
+        return list(cls._entries.values())
+    
+    @classmethod
+    def unregister(cls, name: str) -> bool:
+        """注销注册"""
+        if name in cls._entries:
+            del cls._entries[name]
+            return True
+        return False
+    
+    @classmethod
+    def clear(cls):
+        """清空注册表"""
+        cls._entries.clear()
+
+
+class NodeRegistry(BaseRegistry):
+    """节点注册器 - 用于注册 Agent 工作流节点"""
+    _entries: Dict[str, RegistryEntry] = {}
+
+
+class ToolRegistry(BaseRegistry):
+    """工具注册器 - 用于注册工具"""
+    _entries: Dict[str, RegistryEntry] = {}
+
+
+class WorkflowRegistry(BaseRegistry):
+    """工作流注册器 - 用于注册工作流类型"""
+    _entries: Dict[str, RegistryEntry] = {}
+
+
+class ProviderRegistry(BaseRegistry):
+    """LLM Provider 注册器"""
+    _entries: Dict[str, RegistryEntry] = {}
+
+
+@dataclass
+class ToolMetadata:
+    """工具元数据"""
+    name: str
+    description: str
+    version: str = "1.0.0"
+    timeout: int = 30
+    retry: int = 0
+    parameters_schema: Optional[Dict[str, Any]] = None
+    requires_auth: bool = False
+    tags: List[str] = field(default_factory=list)
+
+
+@dataclass  
+class WorkflowMetadata:
+    """工作流元数据"""
+    name: str
+    description: str
+    version: str = "1.0.0"
+    agent_class: Optional[Type] = None
+    default_model: Optional[str] = None
+    supported_features: List[str] = field(default_factory=list)
+
+
+@dataclass
+class ProviderMetadata:
+    """LLM Provider 元数据"""
+    name: str
+    description: str
+    provider_type: str
+    supported_models: List[str] = field(default_factory=list)
+    config_schema: Optional[Dict[str, Any]] = None
+
+
+def register_tool(
+    name: str,
+    description: str = "",
+    version: str = "1.0.0",
+    timeout: int = 30,
+    retry: int = 0,
+    tags: Optional[List[str]] = None,
+):
+    """
+    工具注册装饰器
+    
+    Usage:
+        @register_tool("calculator", "数学计算", timeout=10)
+        class CalculatorTool(BaseTool):
+            ...
+    """
+    metadata = ToolMetadata(
+        name=name,
+        description=description,
+        version=version,
+        timeout=timeout,
+        retry=retry,
+        tags=tags or [],
+    )
+    return ToolRegistry.register(name, {"tool_metadata": metadata})
+
+
+def register_workflow(
+    name: str,
+    description: str = "",
+    version: str = "1.0.0",
+    default_model: Optional[str] = None,
+    supported_features: Optional[List[str]] = None,
+):
+    """
+    工作流注册装饰器
+    
+    Usage:
+        @register_workflow("data_query", "数据查询工作流")
+        class DataQueryAgent(BaseAgent):
+            ...
+    """
+    metadata = WorkflowMetadata(
+        name=name,
+        description=description,
+        version=version,
+        default_model=default_model,
+        supported_features=supported_features or [],
+    )
+    return WorkflowRegistry.register(name, {"workflow_metadata": metadata})
+
+
+def register_provider(
+    name: str,
+    provider_type: str,
+    description: str = "",
+    supported_models: Optional[List[str]] = None,
+):
+    """
+    LLM Provider 注册装饰器
+    
+    Usage:
+        @register_provider("openai", "openai", supported_models=["gpt-4", "gpt-3.5"])
+        class OpenAIProvider:
+            ...
+    """
+    metadata = ProviderMetadata(
+        name=name,
+        description=description,
+        provider_type=provider_type,
+        supported_models=supported_models or [],
+    )
+    return ProviderRegistry.register(name, {"provider_metadata": metadata})

core/response.py

@@ -0,0 +1,248 @@
+"""
+统一响应格式模块
+
+提供标准化的 API 响应和流式事件格式
+"""
+
+from typing import Any, Dict, Generic, List, Optional, TypeVar, Literal
+from pydantic import BaseModel, Field
+import time
+import uuid
+
+
+T = TypeVar("T")
+
+
+class ApiResponse(BaseModel, Generic[T]):
+    """
+    统一 API 响应格式
+    
+    所有 API 响应都使用这个格式，提供一致的响应结构
+    
+    Usage:
+        @router.get("/users/{user_id}")
+        async def get_user(user_id: str) -> ApiResponse[User]:
+            user = await user_service.get(user_id)
+            return ApiResponse.success(data=user)
+    """
+    
+    code: str = Field(default="success", description="响应代码")
+    message: str = Field(default="", description="响应消息")
+    data: Optional[T] = Field(default=None, description="响应数据")
+    trace_id: Optional[str] = Field(default=None, description="追踪ID")
+    timestamp: int = Field(
+        default_factory=lambda: int(time.time() * 1000),
+        description="时间戳(毫秒)"
+    )
+    
+    @classmethod
+    def success(cls, data: T = None, message: str = "", trace_id: Optional[str] = None) -> "ApiResponse[T]":
+        """创建成功响应"""
+        return cls(
+            code="success",
+            message=message,
+            data=data,
+            trace_id=trace_id or uuid.uuid4().hex,
+        )
+    
+    @classmethod
+    def error(
+        cls,
+        code: str = "error",
+        message: str = "",
+        data: T = None,
+        trace_id: Optional[str] = None,
+    ) -> "ApiResponse[T]":
+        """创建错误响应"""
+        return cls(
+            code=code,
+            message=message,
+            data=data,
+            trace_id=trace_id or uuid.uuid4().hex,
+        )
+    
+    @classmethod
+    def from_exception(cls, exc: Exception, trace_id: Optional[str] = None) -> "ApiResponse[None]":
+        """从异常创建错误响应"""
+        return cls.error(
+            code="internal_error",
+            message=str(exc),
+            trace_id=trace_id,
+        )
+    
+    def is_success(self) -> bool:
+        """判断是否成功"""
+        return self.code == "success"
+
+
+class PagedResponse(BaseModel, Generic[T]):
+    """
+    分页响应格式
+    
+    用于返回分页数据
+    """
+    
+    items: List[T] = Field(default_factory=list, description="数据列表")
+    total: int = Field(default=0, description="总数")
+    page: int = Field(default=1, description="当前页")
+    page_size: int = Field(default=20, description="每页大小")
+    total_pages: int = Field(default=0, description="总页数")
+    
+    @classmethod
+    def create(
+        cls,
+        items: List[T],
+        total: int,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> "PagedResponse[T]":
+        """创建分页响应"""
+        total_pages = (total + page_size - 1) // page_size if page_size > 0 else 0
+        return cls(
+            items=items,
+            total=total,
+            page=page,
+            page_size=page_size,
+            total_pages=total_pages,
+        )
+
+
+class StreamEvent(BaseModel):
+    """
+    流式响应事件
+    
+    用于 SSE (Server-Sent Events) 流式响应
+    
+    Usage:
+        async def event_stream():
+            yield StreamEvent(event="start", data="Processing started")
+            # ... 处理逻辑
+            yield StreamEvent(event="result", data=json.dumps(result))
+            yield StreamEvent(event="done", data="")
+    """
+    
+    event: str = Field(..., description="事件类型")
+    data: str = Field(default="", description="事件数据")
+    event_id: Optional[str] = Field(default=None, description="事件ID")
+    retry: Optional[int] = Field(default=None, description="重试间隔(毫秒)")
+    
+    def to_sse(self) -> str:
+        """转换为 SSE 格式字符串"""
+        lines = [f"event: {self.event}"]
+        if self.event_id:
+            lines.append(f"id: {self.event_id}")
+        if self.retry:
+            lines.append(f"retry: {self.retry}")
+        lines.append(f"data: {self.data}")
+        lines.append("")
+        lines.append("")
+        return "\n".join(lines)
+    
+    @classmethod
+    def message(cls, data: str, event_id: Optional[str] = None) -> "StreamEvent":
+        """创建消息事件"""
+        return cls(event="message", data=data, event_id=event_id)
+    
+    @classmethod
+    def done(cls) -> "StreamEvent":
+        """创建完成事件"""
+        return cls(event="done", data="[DONE]")
+    
+    @classmethod
+    def error(cls, message: str) -> "StreamEvent":
+        """创建错误事件"""
+        return cls(event="error", data=message)
+
+
+class WorkflowEvent(BaseModel):
+    """
+    工作流事件
+    
+    用于工作流执行过程中的状态通知
+    """
+    
+    workflow_id: str = Field(..., description="工作流ID")
+    event_type: Literal[
+        "started",
+        "node_started",
+        "node_completed",
+        "node_failed",
+        "completed",
+        "failed",
+    ] = Field(..., description="事件类型")
+    node_name: Optional[str] = Field(None, description="节点名称")
+    data: Optional[Dict[str, Any]] = Field(None, description="事件数据")
+    error: Optional[str] = Field(None, description="错误信息")
+    timestamp: int = Field(
+        default_factory=lambda: int(time.time() * 1000),
+        description="时间戳"
+    )
+    
+    @classmethod
+    def started(cls, workflow_id: str) -> "WorkflowEvent":
+        """创建开始事件"""
+        return cls(workflow_id=workflow_id, event_type="started")
+    
+    @classmethod
+    def node_started(cls, workflow_id: str, node_name: str) -> "WorkflowEvent":
+        """创建节点开始事件"""
+        return cls(
+            workflow_id=workflow_id,
+            event_type="node_started",
+            node_name=node_name,
+        )
+    
+    @classmethod
+    def node_completed(
+        cls,
+        workflow_id: str,
+        node_name: str,
+        data: Optional[Dict[str, Any]] = None,
+    ) -> "WorkflowEvent":
+        """创建节点完成事件"""
+        return cls(
+            workflow_id=workflow_id,
+            event_type="node_completed",
+            node_name=node_name,
+            data=data,
+        )
+    
+    @classmethod
+    def completed(
+        cls,
+        workflow_id: str,
+        data: Optional[Dict[str, Any]] = None,
+    ) -> "WorkflowEvent":
+        """创建完成事件"""
+        return cls(workflow_id=workflow_id, event_type="completed", data=data)
+    
+    @classmethod
+    def failed(
+        cls,
+        workflow_id: str,
+        error: str,
+        node_name: Optional[str] = None,
+    ) -> "WorkflowEvent":
+        """创建失败事件"""
+        return cls(
+            workflow_id=workflow_id,
+            event_type="failed",
+            node_name=node_name,
+            error=error,
+        )
+
+
+class ErrorCode:
+    """错误代码常量"""
+    
+    SUCCESS = "success"
+    UNKNOWN_ERROR = "unknown_error"
+    INVALID_REQUEST = "invalid_request"
+    INVALID_WORKFLOW_TYPE = "invalid_workflow_type"
+    SQL_GENERATION_FAILED = "sql_generation_failed"
+    TOOL_NOT_FOUND = "tool_not_found"
+    TOOL_EXECUTION_FAILED = "tool_execution_failed"
+    INTERNAL_ERROR = "internal_error"
+    TIMEOUT = "timeout"
+    RATE_LIMITED = "rate_limited"
+    UNAUTHORIZED = "unauthorized"

core/state.py

@@ -0,0 +1,199 @@
+"""
+增强的状态管理模块
+
+使用 Pydantic 提供类型安全和验证
+"""
+
+from typing import Any, Dict, List, Optional, Literal
+from pydantic import BaseModel, Field, field_validator
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
+
+
+class StateContext(BaseModel):
+    """状态上下文 - 存储工作流执行过程中的数据"""
+    
+    original_input: Optional[str] = Field(None, description="用户原始输入")
+    normalized_input: Optional[str] = Field(None, description="规范化后的输入")
+    intent: Optional[str] = Field(None, description="识别的意图")
+    table_match: Optional[Dict[str, Any]] = Field(None, description="表名匹配结果")
+    final_sql: Optional[str] = Field(None, description="生成的 SQL")
+    sr_api_result: Optional[Any] = Field(None, description="API 执行结果")
+    
+    class Config:
+        extra = "allow"
+    
+    def get(self, key: str, default: Any = None) -> Any:
+        """获取上下文值"""
+        return getattr(self, key, default)
+    
+    def set(self, key: str, value: Any) -> None:
+        """设置上下文值"""
+        setattr(self, key, value)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return self.model_dump(exclude_none=True)
+
+
+class AgentState(BaseModel):
+    """
+    Agent 工作流状态定义
+    
+    使用 Pydantic 提供类型安全和验证
+    """
+    
+    messages: List[BaseMessage] = Field(default_factory=list, description="消息历史")
+    current_step: str = Field(default="start", description="当前步骤")
+    context: StateContext = Field(default_factory=StateContext, description="上下文数据")
+    
+    model_config = {
+        "arbitrary_types_allowed": True,
+        "extra": "forbid",
+    }
+    
+    @field_validator("messages", mode="before")
+    @classmethod
+    def validate_messages(cls, v):
+        """验证并转换消息列表"""
+        if not isinstance(v, list):
+            return []
+        
+        result = []
+        for msg in v:
+            if isinstance(msg, BaseMessage):
+                result.append(msg)
+            elif isinstance(msg, dict):
+                msg_type = msg.get("type", "human")
+                content = msg.get("content", "")
+                if msg_type == "human":
+                    result.append(HumanMessage(content=content))
+                elif msg_type == "ai":
+                    result.append(AIMessage(content=content))
+                elif msg_type == "system":
+                    result.append(SystemMessage(content=content))
+        return result
+    
+    def add_message(self, message: BaseMessage) -> "AgentState":
+        """添加消息并返回新状态"""
+        return AgentState(
+            messages=[*self.messages, message],
+            current_step=self.current_step,
+            context=self.context,
+        )
+    
+    def add_human_message(self, content: str) -> "AgentState":
+        """添加用户消息"""
+        return self.add_message(HumanMessage(content=content))
+    
+    def add_ai_message(self, content: str) -> "AgentState":
+        """添加 AI 消息"""
+        return self.add_message(AIMessage(content=content))
+    
+    def update_step(self, step: str) -> "AgentState":
+        """更新当前步骤"""
+        return AgentState(
+            messages=self.messages,
+            current_step=step,
+            context=self.context,
+        )
+    
+    def update_context(self, **kwargs) -> "AgentState":
+        """更新上下文"""
+        new_context = self.context.model_copy()
+        for key, value in kwargs.items():
+            new_context.set(key, value)
+        return AgentState(
+            messages=self.messages,
+            current_step=self.current_step,
+            context=new_context,
+        )
+    
+    def get_last_message(self) -> Optional[BaseMessage]:
+        """获取最后一条消息"""
+        return self.messages[-1] if self.messages else None
+    
+    def get_context(self, key: str, default: Any = None) -> Any:
+        """获取上下文值"""
+        return self.context.get(key, default)
+    
+    def to_legacy_format(self) -> Dict[str, Any]:
+        """
+        转换为旧格式（兼容现有代码）
+        
+        现有代码期望 state 是一个可修改的对象，
+        这个方法返回一个兼容的字典格式
+        """
+        return {
+            "messages": self.messages,
+            "current_step": self.current_step,
+            "context": self.context.to_dict(),
+        }
+    
+    @classmethod
+    def from_legacy_format(cls, data: Dict[str, Any]) -> "AgentState":
+        """从旧格式创建"""
+        context_data = data.get("context", {})
+        if isinstance(context_data, StateContext):
+            context = context_data
+        else:
+            context = StateContext(**context_data) if context_data else StateContext()
+        
+        return cls(
+            messages=data.get("messages", []),
+            current_step=data.get("current_step", "start"),
+            context=context,
+        )
+
+
+class MutableAgentState:
+    """
+    可变的 Agent 状态包装器
+    
+    用于兼容现有代码中直接修改 state 的模式
+    """
+    
+    def __init__(self, state: Optional[AgentState] = None):
+        self._state = state or AgentState()
+        self._context_overrides: Dict[str, Any] = {}
+    
+    @property
+    def messages(self) -> List[BaseMessage]:
+        return self._state.messages
+    
+    @messages.setter
+    def messages(self, value: List[BaseMessage]):
+        self._state = AgentState(
+            messages=value,
+            current_step=self._state.current_step,
+            context=self._state.context,
+        )
+    
+    @property
+    def current_step(self) -> str:
+        return self._state.current_step
+    
+    @current_step.setter
+    def current_step(self, value: str):
+        self._state = AgentState(
+            messages=self._state.messages,
+            current_step=value,
+            context=self._state.context,
+        )
+    
+    @property
+    def context(self) -> Dict[str, Any]:
+        """返回可修改的上下文字典"""
+        result = self._state.context.to_dict()
+        result.update(self._context_overrides)
+        return result
+    
+    def to_immutable(self) -> AgentState:
+        """转换为不可变状态"""
+        context = self._state.context.model_copy()
+        for key, value in self._context_overrides.items():
+            context.set(key, value)
+        return AgentState(
+            messages=self._state.messages,
+            current_step=self._state.current_step,
+            context=context,
+        )

requirements.txt

@@ -8,5 +8,3 @@ uvicorn>=0.30.0
 nacos-sdk-python==2.0.9
 httpx>=0.27.0
 pyyaml>=6.0.1
-redis>=5.0.0
-pymysql>=1.1.1

schemas/super_agent.py

@@ -0,0 +1,37 @@
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class SuperAgentRequest(BaseModel):
+    """Super Agent 请求模型"""
+
+    model_config = ConfigDict(extra="forbid")
+
+    query: str = Field(..., desc ription="用户查询")
+    conversation_id: Optional[str] = Field(None, description="会话ID")
+    user_id: Optional[str] = Field(None, description="用户ID")
+    workflow_type: str = Field(default="conversation", description="工作流类型")
+    context: Dict[str, str] = Field(default_factory=dict, description="上下文信息")
+    timeout_seconds: int = Field(default=30, description="超时时间(秒)")
+
+
+class SuperAgentResponse(BaseModel):
+    """Super Agent 响应模型"""
+
+    conversation_id: str = Field(..., description="会话ID")
+    workflow_type: str = Field(..., description="工作流类型")
+    status: str = Field(default="success", description="状态: success/error")
+    sql: Optional[str] = Field(None, description="生成的SQL")
+    result: Optional[str] = Field(None, description="查询结果")
+    error: Optional[str] = Field(None, description="错误信息")
+    metadata: Dict[str, str] = Field(default_factory=dict, description="元数据")
+
+
+class SuperAgentStreamEvent(BaseModel):
+    """Super Agent 流式响应事件"""
+
+    conversation_id: str = Field(..., description="会话ID")
+    event: str = Field(..., description="事件类型: sql_generated/sql_executing/result/error/done")
+    data: str = Field(..., description="事件数据")
+    timestamp: int = Field(..., description="时间戳(毫秒)")

services/cache.py

@@ -2,11 +2,6 @@ from __future__ import annotations
 
 from typing import Optional
 
-try:
-    import redis
-except Exception:
-    redis = None
-
 
 class CacheBase:
     """缓存接口"""
@@ -26,20 +21,3 @@ class NoopCache(CacheBase):
 
     def set(self, key: str, value: str, ttl: int) -> None:
         return None
-
-
-class RedisCache(CacheBase):
-    """Redis 缓存实现"""
-
-    def __init__(self, url: str, db: int = 0):
-        if redis is None:
-            raise ImportError("未安装 redis 依赖")
-        self._client = redis.Redis.from_url(url, db=db, decode_responses=True)
-
-    def get(self, key: str) -> Optional[str]:
-        return self._client.get(key)
-
-    def set(self, key: str, value: str, ttl: int) -> None:
-        self._client.set(key, value, ex=ttl)
-
-

services/sql_prompt_manager.py

@@ -2,9 +2,6 @@ import json
 import os
 from typing import Any, Dict, Optional
 
-from config import Config
-from services.cache import NoopCache, RedisCache
-
 
 class SqlPromptManager:
     """按表名读取 SQL 提示词"""
@@ -12,51 +9,11 @@ class SqlPromptManager:
     def __init__(self, base_dir: Optional[str] = None):
         root_dir = os.path.dirname(os.path.dirname(__file__))
         self._base_dir = base_dir or os.path.join(root_dir, "config", "sql_gen_prompts")
-        self._cache = self._init_cache()
-        self._cache_ttl = self._get_cache_ttl()
-
-    @staticmethod
-    def _get_cache_ttl() -> int:
-        redis_cfg = Config.get_section("redis")
-        try:
-            return int(redis_cfg.get("sql_prompt_ttl", 600))
-        except Exception:
-            return 600
-
-    @staticmethod
-    def _init_cache():
-        redis_cfg = Config.get_section("redis")
-        enabled = str(redis_cfg.get("enabled", "false")).lower() in ("1", "true", "yes")
-        if not enabled:
-            return NoopCache()
-
-        # 优先使用完整 URL；否则使用 host/port/password/database 拼接
-        url = redis_cfg.get("url")
-        db = int(redis_cfg.get("db", redis_cfg.get("database", 0)))
-        if not url:
-            host = redis_cfg.get("host")
-            port = redis_cfg.get("port", "6379")
-            password = redis_cfg.get("password", "")
-            database = redis_cfg.get("database", str(db))
-            if host:
-                auth = f":{password}@" if password else ""
-                url = f"redis://{auth}{host}:{port}/{database}"
-
-        if not url:
-            return NoopCache()
-        try:
-            return RedisCache(url=url, db=db)
-        except Exception:
-            return NoopCache()
 
     @staticmethod
     def _safe_filename(name: str) -> str:
         return name.replace("..", "").replace("/", "_").replace("\\", "_")
 
-    @staticmethod
-    def _cache_key(table_name: str, mtime: float) -> str:
-        return f"sql_prompt:{table_name}:{int(mtime)}"
-
     def get_prompt(self, table_name: str) -> Optional[Dict[str, Any]]:
         """读取指定表的提示词 JSON"""
         if not table_name:
@@ -67,19 +24,9 @@ class SqlPromptManager:
         if not os.path.exists(path):
             return None
 
-        mtime = os.path.getmtime(path)
-        key = self._cache_key(safe_name, mtime)
-        cached = self._cache.get(key)
-        if cached:
-            try:
-                return json.loads(cached)
-            except Exception:
-                pass
-
         with open(path, "r", encoding="utf-8") as f:
             prompt = json.load(f)
 
-        self._cache.set(key, json.dumps(prompt, ensure_ascii=False), self._cache_ttl)
         return prompt
 
 

services/structured_logger.py

@@ -4,58 +4,10 @@ import json
 from datetime import datetime
 from typing import Any, Dict, Optional
 
-import pymysql
-
-from config import Config
-
 
 class StructuredLogger:
     def __init__(self):
-        cfg = Config.get_section("logging_mysql")
+        pass
-        self.enabled = str(cfg.get("enabled", "false")).lower() in ("1", "true", "yes")
-        self.host = cfg.get("host", "127.0.0.1")
-        self.port = int(cfg.get("port", 3306))
-        self.user = cfg.get("user", "root")
-        self.password = cfg.get("password", "")
-        self.database = cfg.get("database", "more_dots")
-        self.table = cfg.get("table", "structured_logs")
-        self.connect_timeout = int(cfg.get("connect_timeout", 5))
-        self._inited = False
-
-    def _get_conn(self):
-        return pymysql.connect(
-            host=self.host,
-            port=self.port,
-            user=self.user,
-            password=self.password,
-            database=self.database,
-            charset="utf8mb4",
-            autocommit=True,
-            connect_timeout=self.connect_timeout,
-        )
-
-    def _ensure_table(self) -> None:
-        if self._inited or not self.enabled:
-            return
-        sql = f"""
-        CREATE TABLE IF NOT EXISTS {self.table} (
-            id BIGINT PRIMARY KEY AUTO_INCREMENT,
-            trace_id VARCHAR(64) NOT NULL,
-            level VARCHAR(16) NOT NULL,
-            event VARCHAR(128) NOT NULL,
-            error_code VARCHAR(64) NULL,
-            payload JSON NULL,
-            created_at DATETIME NOT NULL
-        ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
-        """
-        try:
-            with self._get_conn() as conn:
-                with conn.cursor() as cur:
-                    cur.execute(sql)
-            self._inited = True
-        except Exception:
-            # 开发阶段容错，避免日志失败影响主流程
-            self.enabled = False
 
     def log(self, level: str, event: str, trace_id: str, payload: Optional[Dict[str, Any]] = None, error_code: Optional[str] = None) -> None:
         print(json.dumps({
@@ -67,32 +19,6 @@ class StructuredLogger:
             "created_at": datetime.now().isoformat(),
         }, ensure_ascii=False))
 
-        if not self.enabled:
-            return
-
-        self._ensure_table()
-        if not self.enabled:
-            return
-
-        insert_sql = f"INSERT INTO {self.table}(trace_id, level, event, error_code, payload, created_at) VALUES(%s,%s,%s,%s,%s,%s)"
-        try:
-            with self._get_conn() as conn:
-                with conn.cursor() as cur:
-                    cur.execute(
-                        insert_sql,
-                        (
-                            trace_id,
-                            level,
-                            event,
-                            error_code,
-                            json.dumps(payload or {}, ensure_ascii=False),
-                            datetime.now(),
-                        ),
-                    )
-        except Exception:
-            # 开发阶段容错，避免日志失败影响主流程
-            return
-
 
 _GLOBAL_STRUCTURED_LOGGER: Optional[StructuredLogger] = None
 

services/tool_router.py

@@ -1,5 +1,13 @@
+"""
+工具路由器模块
+
+支持动态注册和管理工具
+"""
+
 import json
-from typing import Any, Dict, Optional
+import logging
+import time
+from typing import Any, Callable, Dict, List, Optional, Type
 
 from langchain_core.tools import BaseTool
 
@@ -7,26 +15,159 @@ from tools.calculator import CalculatorTool
 from tools.web_search import WebSearchTool
 from tools.rest_api_tool import RestApiTool
 from tools.sr_api_tool import SrApiQueryTool
+from core.registry import ToolRegistry, ToolMetadata
+
+logger = logging.getLogger(__name__)
 
 
 class ToolRouter:
-    """工具路由器：统一调用入口"""
+    """
+    工具路由器：统一调用入口
     
-    def __init__(self, tools: Optional[list[BaseTool]] = None):
+    支持特性：
-        if tools is None:
+    - 动态注册工具
-            tools = [CalculatorTool(), WebSearchTool(), RestApiTool(), SrApiQueryTool()]
+    - 工具元数据管理
-        self._tools: Dict[str, BaseTool] = {tool.name: tool for tool in tools}
+    - 执行监控
+    """
     
-    def list_tools(self) -> list[str]:
+    def __init__(self, tools: Optional[List[BaseTool]] = None):
+        self._tools: Dict[str, BaseTool] = {}
+        self._tool_metadata: Dict[str, ToolMetadata] = {}
+        self._execution_stats: Dict[str, Dict[str, Any]] = {}
+        
+        if tools is not None:
+            for tool in tools:
+                self.register_tool(tool)
+        else:
+            self._register_default_tools()
+    
+    def _register_default_tools(self) -> None:
+        """注册默认工具"""
+        default_tools = [
+            CalculatorTool(),
+            WebSearchTool(),
+            RestApiTool(),
+            SrApiQueryTool(),
+        ]
+        for tool in default_tools:
+            self.register_tool(tool)
+    
+    def register_tool(
+        self,
+        tool: BaseTool,
+        description: str = "",
+        version: str = "1.0.0",
+        timeout: int = 30,
+        retry: int = 0,
+        tags: Optional[List[str]] = None,
+    ) -> None:
+        """
+        注册工具
+        
+        Args:
+            tool: 工具实例
+            description: 描述（默认使用 tool.description）
+            version: 版本
+            timeout: 超时时间
+            retry: 重试次数
+            tags: 标签
+        """
+        name = tool.name
+        metadata = ToolMetadata(
+            name=name,
+            description=description or tool.description,
+            version=version,
+            timeout=timeout,
+            retry=retry,
+            tags=tags or [],
+        )
+        
+        self._tools[name] = tool
+        self._tool_metadata[name] = metadata
+        self._execution_stats[name] = {
+            "total_calls": 0,
+            "success_calls": 0,
+            "failed_calls": 0,
+            "total_time_ms": 0,
+        }
+        
+        ToolRegistry._entries[name] = type(
+            "RegistryEntry",
+            (),
+            {"instance": tool, "metadata": {"tool_metadata": metadata}}
+        )()
+        
+        logger.info(f"Registered tool: {name} (v{version})")
+    
+    def unregister_tool(self, name: str) -> bool:
+        """
+        注销工具
+        
+        Args:
+            name: 工具名称
+            
+        Returns:
+            是否成功注销
+        """
+        if name in self._tools:
+            del self._tools[name]
+            del self._tool_metadata[name]
+            del self._execution_stats[name]
+            ToolRegistry.unregister(name)
+            logger.info(f"Unregistered tool: {name}")
+            return True
+        return False
+    
+    def get_tool(self, name: str) -> Optional[BaseTool]:
+        """获取工具实例"""
+        return self._tools.get(name)
+    
+    def get_tool_metadata(self, name: str) -> Optional[ToolMetadata]:
+        """获取工具元数据"""
+        return self._tool_metadata.get(name)
+    
+    def list_tools(self) -> List[str]:
         """列出可用工具名称"""
         return list(self._tools.keys())
     
+    def get_tool_info(self, name: str) -> Optional[Dict[str, Any]]:
+        """获取工具详细信息"""
+        if name not in self._tools:
+            return None
+        
+        tool = self._tools[name]
+        metadata = self._tool_metadata.get(name)
+        stats = self._execution_stats.get(name, {})
+        
+        return {
+            "name": name,
+            "description": metadata.description if metadata else tool.description,
+            "version": metadata.version if metadata else "unknown",
+            "timeout": metadata.timeout if metadata else 30,
+            "tags": metadata.tags if metadata else [],
+            "stats": {
+                "total_calls": stats.get("total_calls", 0),
+                "success_rate": self._calculate_success_rate(name),
+            },
+        }
+    
     def call(self, tool_name: str, payload: Any) -> Dict[str, Any]:
-        """调用工具并返回标准化结果"""
+        """
+        调用工具并返回标准化结果
+        
+        Args:
+            tool_name: 工具名称
+            payload: 输入参数
+            
+        Returns:
+            标准化结果 {ok, data, error}
+        """
         tool = self._tools.get(tool_name)
         if not tool:
             return {"ok": False, "data": None, "error": f"工具不存在: {tool_name}"}
         
+        start_time = time.time()
+        
         try:
             if isinstance(payload, (dict, list)):
                 input_value = json.dumps(payload, ensure_ascii=False)
@@ -36,6 +177,96 @@ class ToolRouter:
                 input_value = str(payload)
             
             result = tool.run(input_value)
+            
+            self._record_success(tool_name, time.time() - start_time)
+            
             return {"ok": True, "data": result, "error": None}
+        
         except Exception as e:
+            self._record_failure(tool_name, time.time() - start_time)
             return {"ok": False, "data": None, "error": str(e)}
+    
+    def call_with_metadata(
+        self,
+        tool_name: str,
+        payload: Any,
+    ) -> Dict[str, Any]:
+        """
+        调用工具并返回包含元数据的结果
+        
+        Args:
+            tool_name: 工具名称
+            payload: 输入参数
+            
+        Returns:
+            包含元数据的结果
+        """
+        result = self.call(tool_name, payload)
+        metadata = self.get_tool_metadata(tool_name)
+        
+        return {
+            **result,
+            "tool_name": tool_name,
+            "tool_version": metadata.version if metadata else "unknown",
+            "execution_time_ms": self._execution_stats.get(tool_name, {}).get("last_time_ms", 0),
+        }
+    
+    def _record_success(self, tool_name: str, elapsed: float) -> None:
+        """记录成功执行"""
+        if tool_name in self._execution_stats:
+            stats = self._execution_stats[tool_name]
+            stats["total_calls"] += 1
+            stats["success_calls"] += 1
+            stats["total_time_ms"] += elapsed * 1000
+            stats["last_time_ms"] = elapsed * 1000
+    
+    def _record_failure(self, tool_name: str, elapsed: float) -> None:
+        """记录失败执行"""
+        if tool_name in self._execution_stats:
+            stats = self._execution_stats[tool_name]
+            stats["total_calls"] += 1
+            stats["failed_calls"] += 1
+            stats["total_time_ms"] += elapsed * 1000
+            stats["last_time_ms"] = elapsed * 1000
+    
+    def _calculate_success_rate(self, tool_name: str) -> float:
+        """计算成功率"""
+        stats = self._execution_stats.get(tool_name)
+        if not stats or stats["total_calls"] == 0:
+            return 0.0
+        return stats["success_calls"] / stats["total_calls"]
+    
+    def get_all_stats(self) -> Dict[str, Dict[str, Any]]:
+        """获取所有工具的执行统计"""
+        result = {}
+        for name in self._tools:
+            result[name] = {
+                **self._execution_stats.get(name, {}),
+                "success_rate": self._calculate_success_rate(name),
+            }
+        return result
+    
+    def register_function(
+        self,
+        name: str,
+        func: Callable,
+        description: str = "",
+        timeout: int = 30,
+    ) -> None:
+        """
+        将普通函数注册为工具
+        
+        Args:
+            name: 工具名称
+            func: 函数
+            description: 描述
+            timeout: 超时时间
+        """
+        from langchain_core.tools import Tool
+        
+        tool = Tool(
+            name=name,
+            description=description,
+            func=func,
+        )
+        self.register_tool(tool, description=description, timeout=timeout)

workflows/workflow_manager.py

@@ -1,7 +1,19 @@
-from typing import Dict, Any, Optional, List
+"""
+工作流管理器模块
+
+支持动态注册和管理工作流类型
+"""
+
+from typing import Any, Callable, Dict, List, Optional, Type, Union
 from enum import Enum
+from datetime import datetime, timedelta
+import logging
+
 from agent.conversation import ConversationAgent
 from agent.tool import ToolAgent
+from core.registry import WorkflowRegistry, WorkflowMetadata
+
+logger = logging.getLogger(__name__)
 
 
 class WorkflowType(Enum):
@@ -11,64 +23,189 @@ class WorkflowType(Enum):
 
 
 class WorkflowManager:
-    """管理不同工作流类型及其执行"""
+    """
+    管理不同工作流类型及其执行
+    
+    支持特性：
+    - 动态注册工作流
+    - 会话管理
+    - 工作流元数据
+    """
     
     def __init__(self, default_model_section: Optional[str] = None):
-        self.workflows = {
+        self._default_model_section = default_model_section
-            WorkflowType.CONVERSATION: ConversationAgent(model_section=default_model_section),
+        self._workflows: Dict[str, Any] = {}
-            WorkflowType.TOOL_USING: ToolAgent(model_section=default_model_section)
+        self._workflow_metadata: Dict[str, WorkflowMetadata] = {}
-        }
         self.active_sessions: Dict[str, Any] = {}
         
-    def get_workflow(self, workflow_type: WorkflowType):
+        self._register_default_workflows()
-        """获取工作流实例"""
-        return self.workflows.get(workflow_type)
     
-    def execute_workflow(self, workflow_type: WorkflowType, user_input: str, 
+    def _register_default_workflows(self) -> None:
-                        session_id: Optional[str] = None, **kwargs) -> Dict[str, Any]:
+        """注册默认工作流"""
-        """执行指定工作流"""
+        self.register_workflow(
-        workflow = self.get_workflow(workflow_type)
+            name=WorkflowType.CONVERSATION.value,
+            agent=ConversationAgent(model_section=self._default_model_section),
+            description="多轮对话工作流",
+            version="1.0.0",
+        )
+        self.register_workflow(
+            name=WorkflowType.TOOL_USING.value,
+            agent=ToolAgent(model_section=self._default_model_section),
+            description="工具调用工作流",
+            version="1.0.0",
+        )
+    
+    def register_workflow(
+        self,
+        name: str,
+        agent: Any,
+        description: str = "",
+        version: str = "1.0.0",
+        default_model: Optional[str] = None,
+        supported_features: Optional[List[str]] = None,
+    ) -> None:
+        """
+        注册工作流
+        
+        Args:
+            name: 工作流名称
+            agent: Agent 实例
+            description: 描述
+            version: 版本
+            default_model: 默认模型
+            supported_features: 支持的特性列表
+        """
+        metadata = WorkflowMetadata(
+            name=name,
+            description=description,
+            version=version,
+            default_model=default_model,
+            supported_features=supported_features or [],
+        )
+        
+        self._workflows[name] = agent
+        self._workflow_metadata[name] = metadata
+        
+        WorkflowRegistry._entries[name] = type(
+            "RegistryEntry",
+            (),
+            {"instance": agent, "metadata": {"workflow_metadata": metadata}}
+        )()
+        
+        logger.info(f"Registered workflow: {name} (v{version})")
+    
+    def unregister_workflow(self, name: str) -> bool:
+        """
+        注销工作流
+        
+        Args:
+            name: 工作流名称
+            
+        Returns:
+            是否成功注销
+        """
+        if name in self._workflows:
+            del self._workflows[name]
+            del self._workflow_metadata[name]
+            WorkflowRegistry.unregister(name)
+            logger.info(f"Unregistered workflow: {name}")
+            return True
+        return False
+    
+    def get_workflow(self, workflow_type: Union[WorkflowType, str]) -> Optional[Any]:
+        """
+        获取工作流实例
+        
+        Args:
+            workflow_type: 工作流类型（枚举或字符串）
+            
+        Returns:
+            Agent 实例
+        """
+        name = workflow_type.value if isinstance(workflow_type, WorkflowType) else workflow_type
+        return self._workflows.get(name)
+    
+    def get_workflow_metadata(self, name: str) -> Optional[WorkflowMetadata]:
+        """获取工作流元数据"""
+        return self._workflow_metadata.get(name)
+    
+    def execute_workflow(
+        self,
+        workflow_type: Union[WorkflowType, str],
+        user_input: str,
+        session_id: Optional[str] = None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        执行指定工作流
+        
+        Args:
+            workflow_type: 工作流类型
+            user_input: 用户输入
+            session_id: 会话ID
+            **kwargs: 额外参数
+            
+        Returns:
+            执行结果
+        """
+        name = workflow_type.value if isinstance(workflow_type, WorkflowType) else workflow_type
+        workflow = self.get_workflow(name)
         
         if not workflow:
-            return {"error": f"Workflow {workflow_type.value} not found"}
+            return {"error": f"Workflow {name} not found"}
         
-        # 未提供会话 ID 时生成
         if not session_id:
             session_id = f"session_{len(self.active_sessions) + 1}"
         
-        # 执行工作流
         result = workflow.run(user_input, **kwargs)
         
-        # 存储会话数据
         self.active_sessions[session_id] = {
-            "workflow_type": workflow_type,
+            "workflow_type": name,
             "last_result": result,
             "timestamp": self._get_timestamp()
         }
         
         return {
             "session_id": session_id,
-            "workflow_type": workflow_type.value,
+            "workflow_type": name,
             "result": result
         }
     
     def get_available_workflows(self) -> List[str]:
         """获取可用工作流列表"""
-        return [workflow.value for workflow in WorkflowType]
+        return list(self._workflows.keys())
+    
+    def get_workflow_info(self, name: str) -> Optional[Dict[str, Any]]:
+        """获取工作流详细信息"""
+        if name not in self._workflows:
+            return None
+        
+        metadata = self._workflow_metadata.get(name)
+        return {
+            "name": name,
+            "description": metadata.description if metadata else "",
+            "version": metadata.version if metadata else "unknown",
+            "supported_features": metadata.supported_features if metadata else [],
+        }
     
     def _get_timestamp(self) -> str:
         """获取当前时间戳"""
-        from datetime import datetime
         return datetime.now().isoformat()
     
     def get_session_info(self, session_id: str) -> Optional[Dict[str, Any]]:
         """获取会话信息"""
         return self.active_sessions.get(session_id)
     
-    def cleanup_sessions(self, older_than_hours: int = 24):
+    def cleanup_sessions(self, older_than_hours: int = 24) -> int:
-        """清理过期会话"""
+        """
-        from datetime import datetime, timedelta
+        清理过期会话
         
+        Args:
+            older_than_hours: 超过多少小时的会话将被清理
+            
+        Returns:
+            清理的会话数量
+        """
         cutoff_time = datetime.now() - timedelta(hours=older_than_hours)
         
         sessions_to_remove = []
@@ -80,4 +217,31 @@ class WorkflowManager:
         for session_id in sessions_to_remove:
             del self.active_sessions[session_id]
         
+        if sessions_to_remove:
+            logger.info(f"Cleaned up {len(sessions_to_remove)} expired sessions")
+        
         return len(sessions_to_remove)
+    
+    def create_agent_instance(
+        self,
+        workflow_name: str,
+        agent_class: Type,
+        model_section: Optional[str] = None,
+        **kwargs
+    ) -> Any:
+        """
+        创建并注册新的 Agent 实例
+        
+        Args:
+            workflow_name: 工作流名称
+            agent_class: Agent 类
+            model_section: 模型配置段
+            **kwargs: Agent 构造参数
+            
+        Returns:
+            Agent 实例
+        """
+        model = model_section or self._default_model_section
+        agent = agent_class(model_section=model, **kwargs)
+        self.register_workflow(name=workflow_name, agent=agent)
+        return agent