Merge pull request #120 from xerrors/agent-update

Agent 部分更新（20%）

docker/api.Dockerfile

@@ -16,4 +16,5 @@ RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
 
 # 复制代码到容器中
 COPY ../src /app/src
+COPY ../server /app/server
 

docker/docker-compose.dev.yml

@@ -6,6 +6,7 @@ services:
     container_name: api-dev
     working_dir: /app
     volumes:
+      - ../server:/app/server
       - ../src:/app/src
       - ../saves_dev:/app/saves
       # - ${MODEL_DIR}:/models # 如果出现 undefined volume MODEL_DIR: invalid compose project，请添加此环境变量或者注释此行

server/db_manager.py

@@ -0,0 +1,40 @@
+import os
+import sqlite3
+import pathlib
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.ext.declarative import declarative_base
+
+from server.models.token_model import Base, AgentToken
+
+class DBManager:
+    """数据库管理器"""
+
+    def __init__(self):
+        self.db_path = os.path.join("saves", "data", "server.db")
+        self.ensure_db_dir()
+
+        # 创建SQLAlchemy引擎
+        self.engine = create_engine(f"sqlite:///{self.db_path}")
+
+        # 创建会话工厂
+        self.Session = sessionmaker(bind=self.engine)
+
+        # 确保表存在
+        self.create_tables()
+
+    def ensure_db_dir(self):
+        """确保数据库目录存在"""
+        db_dir = os.path.dirname(self.db_path)
+        pathlib.Path(db_dir).mkdir(parents=True, exist_ok=True)
+
+    def create_tables(self):
+        """创建数据库表"""
+        Base.metadata.create_all(self.engine)
+
+    def get_session(self):
+        """获取数据库会话"""
+        return self.Session()
+
+# 创建全局数据库管理器实例
+db_manager = DBManager()

server/main.py

@@ -2,7 +2,7 @@ import uvicorn
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from src.routers import router
+from server.routers import router
 from src.utils.logging_config import logger
 
 

server/models/token_model.py

@@ -0,0 +1,24 @@
+from sqlalchemy import Column, Integer, String, DateTime, ForeignKey
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.sql import func
+
+Base = declarative_base()
+
+class AgentToken(Base):
+    """智能体访问令牌模型"""
+    __tablename__ = 'agent_tokens'
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    agent_id = Column(String, nullable=False, index=True)  # 智能体ID
+    name = Column(String, nullable=False)  # 令牌名称
+    token = Column(String, nullable=False, unique=True)  # 令牌值
+    created_at = Column(DateTime, default=func.now())  # 创建时间
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "agent_id": self.agent_id,
+            "name": self.name,
+            "token": self.token,
+            "created_at": self.created_at.isoformat() if self.created_at else None
+        }

server/routers/__init__.py

@@ -0,0 +1,13 @@
+from fastapi import APIRouter
+from server.routers.chat_router import chat
+from server.routers.data_router import data
+from server.routers.base_router import base
+from server.routers.tool_router import tool
+from server.routers.admin_router import admin
+
+router = APIRouter()
+router.include_router(base)
+router.include_router(chat)
+router.include_router(data)
+router.include_router(tool)
+router.include_router(admin)

server/routers/admin_router.py

@@ -0,0 +1,103 @@
+import secrets
+import string
+from fastapi import APIRouter, Depends, HTTPException, Query
+from pydantic import BaseModel
+from typing import List, Optional
+from sqlalchemy.orm import Session
+
+from server.db_manager import db_manager
+from server.models.token_model import AgentToken
+
+admin = APIRouter(prefix="/admin", tags=["admin"])
+
+# 依赖项：获取数据库会话
+def get_db():
+    db = db_manager.get_session()
+    try:
+        yield db
+    finally:
+        db.close()
+
+# 请求和响应模型
+class TokenCreate(BaseModel):
+    agent_id: str
+    name: str
+
+class TokenVerify(BaseModel):
+    agent_id: str
+    token: str
+
+class TokenResponse(BaseModel):
+    id: int
+    agent_id: str
+    name: str
+    token: str
+    created_at: str
+
+# 生成随机token
+def generate_token(length=32):
+    alphabet = string.ascii_letters + string.digits
+    return ''.join(secrets.choice(alphabet) for _ in range(length))
+
+@admin.get("/tokens", response_model=List[TokenResponse])
+async def get_agent_tokens(
+    agent_id: Optional[str] = Query(None),
+    db: Session = Depends(get_db)
+):
+    """获取智能体的token列表"""
+    query = db.query(AgentToken)
+    if agent_id:
+        query = query.filter(AgentToken.agent_id == agent_id)
+    tokens = query.all()
+    return [token.to_dict() for token in tokens]
+
+@admin.post("/tokens", response_model=TokenResponse)
+async def create_token(
+    token_data: TokenCreate,
+    db: Session = Depends(get_db)
+):
+    """创建新的token"""
+    # 生成随机token
+    token_value = generate_token()
+
+    # 创建token记录
+    new_token = AgentToken(
+        agent_id=token_data.agent_id,
+        name=token_data.name,
+        token=token_value
+    )
+
+    # 保存到数据库
+    db.add(new_token)
+    db.commit()
+    db.refresh(new_token)
+
+    return new_token.to_dict()
+
+@admin.delete("/tokens/{token_id}", response_model=dict)
+async def delete_token(token_id: int, db: Session = Depends(get_db)):
+    """删除token"""
+    token = db.query(AgentToken).filter(AgentToken.id == token_id).first()
+    if not token:
+        raise HTTPException(status_code=404, detail="Token not found")
+
+    db.delete(token)
+    db.commit()
+
+    return {"success": True, "message": "Token deleted"}
+
+@admin.post("/verify_token")
+async def verify_agent_token(
+    token_data: TokenVerify,
+    db: Session = Depends(get_db)
+):
+    """验证智能体访问令牌"""
+    token = db.query(AgentToken).filter(
+        AgentToken.agent_id == token_data.agent_id,
+        AgentToken.token == token_data.token
+    ).first()
+
+    if not token:
+        raise HTTPException(status_code=401, detail="Invalid token")
+
+    return {"success": True, "message": "Token verified"}

server/routers/chat_router.py

@@ -1,8 +1,9 @@
+import os
 import json
 import asyncio
 import traceback
 import uuid
-from fastapi import APIRouter, Body
+from fastapi import APIRouter, Body, Depends, HTTPException
 from fastapi.responses import StreamingResponse
 from langchain_core.messages import AIMessageChunk
 
@@ -14,8 +15,6 @@ from src.utils.logging_config import logger
 
 chat = APIRouter(prefix="/chat")
 
-
-
 @chat.get("/")
 async def chat_get():
     return "Chat Get!"
@@ -123,60 +122,77 @@ async def call(query: str = Body(...), meta: dict = Body(None)):
 
 @chat.get("/agent")
 async def get_agent():
-    agents = [{
-        "name": agent.name,
-        "description": agent.description,
-        "config_schema": agent.config_schema
-    } for agent in agent_manager.agents.values()]
+    agents = [agent.get_info() for agent in agent_manager.agents.values()]
     return {"agents": agents}
 
 @chat.post("/agent/{agent_name}")
 def chat_agent(agent_name: str,
                query: str = Body(...),
-               meta: dict = Body({}),
                history: list = Body(...),
-               thread_id: str | None = Body(None)):
+               config: dict = Body({}),
+               meta: dict = Body({})):
 
-    meta["server_model_name"] = agent_name
-    agent = agent_manager.get_runnable_agent(agent_name)
-
-    history_manager = HistoryManager(history)
-    messages = history_manager.get_history_with_msg(query, max_rounds=meta.get('history_round'))
-    history_manager.add_user(query)  # 注意这里使用原始查询
-
-    runnable_config = {
-        "configurable": {
-            "thread_id": thread_id or str(uuid.uuid4()),
-            "use_web": meta.get("use_web", False),
-            "return_keys": []
-        }
-    }
+    meta.update({
+        "query": query,
+        "agent_name": agent_name,
+        "server_model_name": config.get("model", agent_name) ,
+        "thread_id": config.get("thread_id"),
+    })
 
+    # 将meta和thread_id整合到config中
     def make_chunk(content=None, **kwargs):
+
         return json.dumps({
+            "request_id": meta.get("request_id"),
             "response": content,
-            "model_name": agent_name,
-            "meta": meta,
             **kwargs
         }, ensure_ascii=False).encode('utf-8') + b"\n"
 
+    try:
+        agent = agent_manager.get_runnable_agent(agent_name)
+    except Exception as e:
+        logger.error(f"Error getting agent {agent_name}: {e}")
+        return StreamingResponse(make_chunk(message=f"Error getting agent {agent_name}: {e}", status="error"), media_type='application/json')
+
+    # 从config中获取history_round
+    history_round = config.get("history_round")
+    history_manager = HistoryManager(history)
+    messages = history_manager.get_history_with_msg(query, max_rounds=history_round)
+    history_manager.add_user(query)
+
+    # 如果没有thread_id则生成一个
+    if "thread_id" not in config or not config["thread_id"]:
+        config["thread_id"] = str(uuid.uuid4())
+
+    # 构造运行时配置
+    runnable_config = {
+        "configurable": {
+            **config
+        }
+    }
+
     def stream_messages():
         content = ""
-        yield make_chunk(status="waiting")
-        for msg, metadata in agent.stream_messages(messages, runnable_config):
-            # logger.debug(f">>>>>    msg: {msg.model_dump()}, >>>>>>>      {metadata=}")
-            if isinstance(msg, AIMessageChunk) and msg.content != "<tool_call>":
-                content += msg.content
-                yield make_chunk(content=msg.content,
-                                msg=msg.model_dump(),
-                                metadata=metadata,
-                                status="loading")
-            else:
-                yield make_chunk(msg=msg.model_dump(),
-                                metadata=metadata,
-                                status="loading")
+        yield make_chunk(status="init", meta=meta)
+        try:
+            for msg, metadata in agent.stream_messages(messages, config_schema=runnable_config):
+                if isinstance(msg, AIMessageChunk) and msg.content != "<tool_call>":
+                    content += msg.content
+                    yield make_chunk(content=msg.content,
+                                    msg=msg.model_dump(),
+                                    metadata=metadata,
+                                    status="loading")
+                else:
+                    yield make_chunk(msg=msg.model_dump(),
+                                    metadata=metadata,
+                                    status="loading")
 
-        yield make_chunk(status="finished", history=history_manager.update_ai(content))
+            yield make_chunk(status="finished",
+                            history=history_manager.update_ai(content),
+                            meta=meta)
+        except Exception as e:
+            logger.error(f"Error streaming messages: {e}")
+            yield make_chunk(message=f"Error streaming messages: {e}", status="error")
 
     return StreamingResponse(stream_messages(), media_type='application/json')
 

server/routers/data_router.py

@@ -72,7 +72,7 @@ async def create_document_by_file(db_id: str = Body(...), files: List[str] = Bod
 
 @data.post("/add-by-chunks")
 async def add_by_chunks(db_id: str = Body(...), file_chunks: dict = Body(...)):
-    logger.debug(f"Add chunks in {db_id}: {file_chunks}")
+    # logger.debug(f"Add chunks in {db_id}: {len(file_chunks)} chunks")
     try:
         loop = asyncio.get_event_loop()
         await loop.run_in_executor(

server/routers/tool_router.py

@@ -50,7 +50,7 @@ async def route_index():
                 description=agent.description,
                 url=f"/agent/{agent.name}",
                 method="POST",
-                metadata=agent.config_schema,
+                metadata=agent.config_schema.to_dict(),
             )
         )
 

src/agents/chatbot/configuration.py

@@ -1,36 +1,36 @@
 from dataclasses import dataclass, field
-from datetime import datetime, timezone
-
-from langchain_core.tools import tool
-from langchain_openai import ChatOpenAI
 
 from src.agents.registry import Configuration
 
-def get_default_requirements():
-    return ["TAVILY_API_KEY"]
-
-@tool
-def multiply(first_int: int, second_int: int) -> int:
-    """Multiply two integers together."""
-    return first_int * second_int
-
 @dataclass(kw_only=True)
 class ChatbotConfiguration(Configuration):
-    """Chatbot 的配置"""
+    """Chatbot 的配置
+
+    配置说明：
+
+    metadata 中 configurable 为 True 的配置项可以被用户配置，
+    configurable 为 False 的配置项不能被用户配置，只能由开发者预设。
+    """
 
     system_prompt: str = field(
-        default=f"You are a helpful assistant. Now is {datetime.now(tz=timezone.utc).isoformat()}",
+        default="You are a helpful assistant.",
         metadata={
-            "description": "The system prompt to use for the agent's interactions. "
-            "This prompt sets the context and behavior for the agent."
+            "name": "系统提示词",
+            "configurable": True,
+            "description": "用来描述智能体的角色和行为"
         },
     )
 
     model: str = field(
         default="zhipu/glm-4-plus",
         metadata={
-            "description": "The name of the language model to use for the agent's main interactions. "
-            "Should be in the form: provider/model-name."
+            "name": "智能体模型",
+            "configurable": True,
+            "options": [
+                "zhipu/glm-4-plus",
+                "siliconflow/Qwen/QwQ-32B",
+                "siliconflow/deepseek-ai/DeepSeek-V3",
+            ],
+            "description": "智能体的驱动模型"
         },
     )
-

src/agents/chatbot/graph.py

@@ -1,48 +1,62 @@
 import asyncio
 import uuid
 from typing import Any
-from datetime import datetime
+from datetime import datetime, timezone
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.graph import StateGraph, START, END
 from langgraph.prebuilt import ToolNode, tools_condition
-from langgraph.checkpoint.memory import MemorySaver
-from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.checkpoint.memory import MemorySaver # 实际上没有起作用
 
 
+from src.utils import logger
 from src.agents.registry import State, BaseAgent
-from src.agents.utils import load_chat_model
-from src.agents.chatbot.configuration import ChatbotConfiguration, multiply
+from src.agents.utils import load_chat_model, get_cur_time_with_utc
+from src.agents.chatbot.configuration import ChatbotConfiguration
+from src.agents.tools_factory import _TOOLS_REGISTRY
 
 class ChatbotAgent(BaseAgent):
     name = "chatbot"
     description = "A chatbot that can answer questions and help with tasks."
-    _graph_cache = None
-    config_schema = ChatbotConfiguration.to_dict()
+    requirements = ["TAVILY_API_KEY", "ZHIPUAI_API_KEY"]
+    all_tools = ["TavilySearchResults", "multiply", "add", "subtract", "divide"]
+    config_schema = ChatbotConfiguration
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
     def _get_tools(self, config_schema: RunnableConfig):
-        """根据配置获取工具"""
-        tools = [multiply, TavilySearchResults(max_results=10)]
-        return tools
+        """根据配置获取工具，如果配置为空，则使用所有工具，如果配置为列表，则使用列表中的工具，
+        如果配置为其他类型，则抛出错误"""
+        conf_tools = config_schema.get("tools")
+        if conf_tools == None:
+            tool_names = self.all_tools
+        elif isinstance(conf_tools, list):
+            tool_names = [tool for tool in self.all_tools if tool in conf_tools]
+        else:
+            raise ValueError(f"tools 配置错误: {conf_tools}")
+
+        logger.info(f"Tools: {tool_names}")
+        return [_TOOLS_REGISTRY[tool] for tool in tool_names]
 
     def llm_call(self, state: State, config: RunnableConfig = None) -> dict[str, Any]:
         """调用 llm 模型"""
         config_schema = config or {}
-        conf = ChatbotConfiguration.from_runnable_config(config_schema)
+        conf = self.config_schema.from_runnable_config(config_schema)
+
+        system_prompt = f"{conf.system_prompt} Now is {get_cur_time_with_utc()}"
         model = load_chat_model(conf.model)
         model_with_tools = model.bind_tools(self._get_tools(config_schema))
+        logger.info(f"llm_call with config: {conf}, {conf.model}")
 
         res = model_with_tools.invoke(
-            [{"role": "system", "content": conf.system_prompt}, *state["messages"]]
+            [{"role": "system", "content": system_prompt}, *state["messages"]]
         )
         return {"messages": [res]}
 
-    def get_graph(self, config_schema: RunnableConfig = None):
+    def get_graph(self, config_schema: RunnableConfig = None, **kwargs):
         """构建图"""
-        workflow = StateGraph(State, config_schema=ChatbotConfiguration)
+        workflow = StateGraph(State, config_schema=self.config_schema)
         workflow.add_node("chatbot", self.llm_call)
         workflow.add_node("tools", ToolNode(tools=self._get_tools(config_schema)))
         workflow.add_edge(START, "chatbot")
@@ -56,20 +70,6 @@ class ChatbotAgent(BaseAgent):
         graph = workflow.compile(checkpointer=MemorySaver())
         return graph
 
-    def stream_values(self, messages: list[str], config_schema: RunnableConfig = None):
-        graph = self.get_graph(config_schema)
-        for event in graph.stream({"messages": messages}, stream_mode="values", config=config_schema):
-            yield event["messages"]
-
-    def stream_messages(self, messages: list[str], config_schema: RunnableConfig = None):
-        graph = self.get_graph(config_schema)
-        conf = ChatbotConfiguration.from_runnable_config(config_schema)
-        for msg, metadata in graph.stream({"messages": messages}, stream_mode="messages", config=config_schema):
-            msg_type = msg.type
-
-            return_keys =conf.return_keys
-            if not return_keys or msg_type in return_keys:
-                yield msg, metadata
 
 def main():
     agent = ChatbotAgent(ChatbotConfiguration())

src/agents/react/graph.py

@@ -17,60 +17,12 @@ from src.agents.react.configuration import ReActConfiguration, multiply
 class ReActAgent(BaseAgent):
     name = "react"
     description = "A react agent that can answer questions and help with tasks."
-    _graph_cache = None
-    config_schema = ReActConfiguration.to_dict()
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def _get_tools(self, config_schema: RunnableConfig):
-        """根据配置获取工具"""
-        tools = [multiply, TavilySearchResults(max_results=10)]
-        return tools
-
-    def llm_call(self, state: State, config: RunnableConfig = None) -> dict[str, Any]:
-        """调用 llm 模型"""
-        config_schema = config or {}
-        conf = ReActConfiguration.from_runnable_config(config_schema)
-        model = load_chat_model(conf.model)
-        model_with_tools = model.bind_tools(self._get_tools(config_schema))
-
-        res = model_with_tools.invoke(
-            [{"role": "system", "content": conf.system_prompt}, *state["messages"]]
-        )
-        return {"messages": [res]}
-
-    def get_graph(self, config_schema: RunnableConfig = None):
+    def get_graph(self, **kwargs):
         """构建图"""
-        workflow = StateGraph(State, config_schema=ReActConfiguration)
-        workflow.add_node("react", self.llm_call)
-        workflow.add_node("tools", ToolNode(tools=self._get_tools(config_schema)))
-        workflow.add_edge(START, "react")
-        workflow.add_conditional_edges(
-            "react",
-            tools_condition,
-        )
-        workflow.add_edge("tools", "react")
-        workflow.add_edge("react", END)
-
-        graph = workflow.compile(checkpointer=MemorySaver())
+        from .workflows import graph
         return graph
 
-    def stream_values(self, messages: list[str], config_schema: RunnableConfig = None):
-        graph = self.get_graph(config_schema)
-        for event in graph.stream({"messages": messages}, stream_mode="values", config=config_schema):
-            yield event["messages"]
-
-    def stream_messages(self, messages: list[str], config_schema: RunnableConfig = None):
-        graph = self.get_graph(config_schema)
-        conf = ReActConfiguration.from_runnable_config(config_schema)
-        for msg, metadata in graph.stream({"messages": messages}, stream_mode="messages", config=config_schema):
-            msg_type = msg.type
-
-            return_keys =conf.return_keys
-            if not return_keys or msg_type in return_keys:
-                yield msg, metadata
-
 def main():
     agent = ReActAgent(ReActConfiguration())
 

src/agents/react/workflows.py

@@ -0,0 +1,36 @@
+import os
+
+from langchain_openai import ChatOpenAI
+
+model = ChatOpenAI(model="glm-4-plus",
+                   api_key=os.getenv("ZHIPUAI_API_KEY"),
+                   base_url="https://open.bigmodel.cn/api/paas/v4/",
+                   temperature=0)
+
+
+# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
+
+from typing import Literal
+
+from langchain_core.tools import tool
+
+
+@tool
+def get_weather(city: Literal["nyc", "sf"]):
+    """Use this to get weather information."""
+    if city == "nyc":
+        return "It might be cloudy in nyc"
+    elif city == "sf":
+        return "It's always sunny in sf"
+    else:
+        raise AssertionError("Unknown city")
+
+
+tools = [get_weather]
+
+
+# Define the graph
+
+from langgraph.prebuilt import create_react_agent
+
+graph = create_react_agent(model, tools=tools)

src/agents/registry.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import os
+
 from typing import Type, Annotated, Optional, TypedDict
 from abc import abstractmethod
 from dataclasses import dataclass, fields, field
@@ -10,7 +12,7 @@ from langgraph.graph.state import CompiledStateGraph
 from langgraph.graph.message import add_messages
 
 from src.config import SimpleConfig
-
+from src.utils import logger
 
 class State(TypedDict):
     """
@@ -24,7 +26,7 @@ class State(TypedDict):
 
 
 @dataclass(kw_only=True)
-class Configuration(SimpleConfig):
+class Configuration(dict):
     """
     定义一个基础 Configuration 供 各类 graph 继承
     """
@@ -40,15 +42,73 @@ class Configuration(SimpleConfig):
 
     @classmethod
     def to_dict(cls):
-        return {f.name: getattr(cls, f.name) for f in fields(cls) if f.init}
+        # 创建一个实例来处理 default_factory
+        instance = cls()
+        confs = {}
+        configurable_items = {}
+        for f in fields(cls):
+            if f.init and not f.metadata.get("hide", False):
+                value = getattr(instance, f.name)
+                if callable(value) and hasattr(value, "__call__"):
+                    confs[f.name] = value()
+                else:
+                    confs[f.name] = value
+
+                if f.metadata.get("configurable"):
+                    configurable_items[f.name] = {
+                        "type": f.type.__name__,
+                        "name": f.metadata.get("name", f.name),
+                        "options": f.metadata.get("options", []),
+                        "default": f.default,
+                        "description": f.metadata.get("description", ""),
+                    }
+        confs["configurable_items"] = configurable_items
+        return confs
 
 
 
 class BaseAgent():
 
+    """
+    定义一个基础 Agent 供 各类 graph 继承
+    """
+
+    name: str = field(default="base_agent")
+    description: str = field(default="base_agent")
+    config_schema: Configuration = Configuration
+    requirements: list[str]
+
     def __init__(self, **kwargs):
-        pass
+        self.check_requirements()
+
+    @classmethod
+    def get_info(cls):
+        return {
+            "name": cls.name,
+            "description": cls.description,
+            "config_schema": cls.config_schema.to_dict(),
+            "requirements": cls.requirements if hasattr(cls, "requirements") else [],
+            "all_tools": cls.all_tools if hasattr(cls, "all_tools") else [],
+        }
+
+    def check_requirements(self):
+        if not hasattr(self, "requirements") or not self.requirements:
+            return
+        for requirement in self.requirements:
+            if requirement not in os.environ:
+                raise ValueError(f"{requirement} is not set")
+
+    def stream_values(self, messages: list[str], config_schema: RunnableConfig = None, **kwargs):
+        graph = self.get_graph(config_schema=config_schema, **kwargs)
+        for event in graph.stream({"messages": messages}, stream_mode="values", config=config_schema):
+            yield event["messages"]
+
+    def stream_messages(self, messages: list[str], config_schema: RunnableConfig = None, **kwargs):
+        graph = self.get_graph(config_schema=config_schema, **kwargs)
+
+        for msg, metadata in graph.stream({"messages": messages}, stream_mode="messages", config=config_schema):
+            yield msg, metadata
 
     @abstractmethod
-    def get_graph(self) -> CompiledStateGraph:
+    def get_graph(self, **kwargs) -> CompiledStateGraph:
         pass

src/agents/tools_factory.py

@@ -5,11 +5,8 @@ from typing import Any, Callable, Optional, Type, Union
 
 
 from pydantic import BaseModel, Field
-from langchain.agents import tool
-from langchain_core.tools import Tool, BaseTool
-
-
-_TOOLS_REGISTRY = {}
+from langchain_core.tools import tool, BaseTool
+from langchain_community.tools.tavily_search import TavilySearchResults
 
 # refs https://github.com/chatchat-space/LangGraph-Chatchat chatchat-server/chatchat/server/agent/tools_factory/tools_registry.py
 def regist_tool(
@@ -95,3 +92,33 @@ class BaseToolOutput:
         else:
             return str(self.data)
 
+
+
+@tool
+def multiply(first_int: int, second_int: int) -> int:
+    """Multiply two integers together."""
+    return first_int * second_int
+
+@tool
+def add(first_int: int, second_int: int) -> int:
+    """Add two integers together."""
+    return first_int + second_int
+
+@tool
+def subtract(first_int: int, second_int: int) -> int:
+    """Subtract two integers."""
+    return first_int - second_int
+
+@tool
+def divide(first_int: int, second_int: int) -> int:
+    """Divide two integers."""
+    return first_int / second_int
+
+
+_TOOLS_REGISTRY = {
+    "multiply": multiply,
+    "add": add,
+    "subtract": subtract,
+    "divide": divide,
+    "TavilySearchResults": TavilySearchResults(max_results=10),
+}

src/agents/utils.py

@@ -1,3 +1,5 @@
+from datetime import datetime, timezone
+
 from src.models import select_model
 from src.agents.registry import BaseAgent
 from langchain_core.language_models import BaseChatModel
@@ -7,14 +9,17 @@ from langchain_core.messages import AIMessageChunk, ToolMessage
 
 
 
-def load_chat_model(fully_specified_name: str) -> BaseChatModel:
+def load_chat_model(fully_specified_name: str, **kwargs) -> BaseChatModel:
     """Load a chat model from a fully specified name.
 
     Args:
         fully_specified_name (str): String in the format 'provider/model'.
+        **kwargs: Additional parameters to pass to the model.
     """
     provider, model = fully_specified_name.split("/", maxsplit=1)
-    return select_model(model_name=model, model_provider=provider).chat_open_ai
+    model_instance = select_model(model_name=model, model_provider=provider)
+
+    return model_instance.chat_open_ai
 
 
 def agent_cli(agent: BaseAgent, config: RunnableConfig = None):
@@ -49,3 +54,7 @@ def agent_cli(agent: BaseAgent, config: RunnableConfig = None):
 
             if isinstance(msg, ToolMessage):
                 print(f"Tool: {msg.content}")
+
+def get_cur_time_with_utc():
+    return datetime.now(tz=timezone.utc).isoformat()
+

src/config/__init__.py

@@ -28,7 +28,7 @@ class SimpleConfig(dict):
         return self.get(self.__key(key))
 
     def __getitem__(self, key):
-        return super().get(self.__key(key))
+        return self.get(self.__key(key))
 
     def __setitem__(self, key, value):
         return super().__setitem__(self.__key(key), value)

src/core/retriever.py

@@ -231,7 +231,7 @@ class Retriever:
         return formatted_results
 
     def format_query_results(self, results):
-        logger.debug(f"Graph Query Results: {results}")
+        # logger.debug(f"Graph Query Results: {results}")
         formatted_results = {"nodes": [], "edges": []}
         node_dict = {}
 

src/models/chat_model.py

@@ -11,7 +11,8 @@ class OpenAIBase():
         self.model_name = model_name
         self.chat_open_ai = ChatOpenAI(model=model_name,
                                        api_key=api_key,
-                                       base_url=base_url)
+                                       base_url=base_url,
+                                       temperature=0.7)
 
     def predict(self, message, stream=False):
         if isinstance(message, str):

src/routers/__init__.py

@@ -1,11 +0,0 @@
-from fastapi import APIRouter
-from src.routers.chat_router import chat
-from src.routers.data_router import data
-from src.routers.base_router import base
-from src.routers.tool_router import tool
-
-router = APIRouter()
-router.include_router(base)
-router.include_router(chat)
-router.include_router(data)
-router.include_router(tool)

web/src/assets/markdown.css

@@ -10,12 +10,17 @@
     padding: 1rem;
 }
 
-.message-md pre:has(code.hljs) {
+.message-md pre:has(code) {
     padding: 0;
 }
 
-.message-md pre code.hljs {
-    font-size: 0.8rem;
+.message-md pre code {
+    font-size: 13px;
+    font-family: 'Menlo', 'Monaco', 'Consolas', 'PingFang SC', 'Microsoft YaHei', 'Hiragino Sans GB', 'Source Han Sans CN', 'Courier New', monospace;
+    line-height: 1.5;
+    letter-spacing: 0.025em;
+    tab-size: 4;
+    -moz-tab-size: 4;
     background-color: var(--gray-100);
 }
 

web/src/components/MessageComponent.vue

@@ -6,6 +6,9 @@
     <!-- 助手消息 -->
     <div v-else-if="message.role === 'assistant' || message.role === 'received'" class="assistant-message">
       <!-- 推理过程 (ChatComponent特有) -->
+      <p v-if="debugMode">
+        {{ message.status }}
+      </p>
       <div v-if="message.reasoning_content" class="reasoning-box">
         <a-collapse v-model:activeKey="reasoningActiveKey" :bordered="false">
           <template #expandIcon="{ isActive }">
@@ -50,7 +53,7 @@
       <slot name="tool-calls"></slot>
 
       <div v-if="(message.role=='received' || message.role=='assistant') && message.status=='finished' && showRefs">
-        <RefsComponent :message="message" @retry="emit('retry')" />
+        <RefsComponent :message="message" :show-refs="showRefs" @retry="emit('retry')" />
       </div>
       <!-- 错误消息 -->
     </div>
@@ -77,11 +80,6 @@ const props = defineProps({
     type: Object,
     required: true
   },
-  // 已渲染的HTML内容
-  contentHtml: {
-    type: String,
-    default: ''
-  },
   // 是否正在处理中
   isProcessing: {
     type: Boolean,
@@ -94,9 +92,13 @@ const props = defineProps({
   },
   // 是否显示推理过程
   showRefs: {
+    type: [Array, Boolean],
+    default: () => false
+  },
+  debugMode: {
     type: Boolean,
     default: false
-  }
+  },
 });
 
 const statusDefination = {

web/src/components/RefsComponent.vue

@@ -4,19 +4,23 @@
       <!-- <span class="item btn" @click="likeThisResponse(msg)"><LikeOutlined /></span> -->
       <!-- <span class="item btn" @click="dislikeThisResponse(msg)"><DislikeOutlined /></span> -->
       <span v-if="msg.meta?.server_model_name" class="item"><BulbOutlined /> {{ msg.meta.server_model_name }}</span>
-      <span class="item btn" @click="copyText(msg.content)" title="复制"><CopyOutlined /></span>
-      <span class="item btn" @click="regenerateMessage()" title="重新生成"><ReloadOutlined /></span>
       <span
+        v-if="showKey('copy')"
+        class="item btn" @click="copyText(msg.content)" title="复制"><CopyOutlined /></span>
+      <span
+        v-if="showKey('regenerate')"
+        class="item btn" @click="regenerateMessage()" title="重新生成"><ReloadOutlined /></span>
+      <span
+        v-if="showKey('subGraph') && hasSubGraphData(msg)"
         class="item btn"
         @click="openSubGraph(msg)"
-        v-if="hasSubGraphData(msg)"
       >
         <DeploymentUnitOutlined /> 关系图
       </span>
       <span
         class="item btn"
         @click="showWebResult(msg)"
-        v-if="msg.refs?.web_search.results.length > 0"
+        v-if="showKey('webSearch') && msg.refs?.web_search.results.length > 0"
       >
         <GlobalOutlined /> 网页搜索 {{ msg.refs.web_search?.results.length }}
       </span>
@@ -120,6 +124,10 @@ import GraphContainer from './GraphContainer.vue'  // 导入 GraphContainer 组
 const emit = defineEmits(['retry']);
 const props = defineProps({
   message: Object,
+  showRefs: {
+    type: [Array, Boolean],
+    default: () => false
+  }
 })
 
 const msg = ref(props.message)
@@ -127,6 +135,13 @@ const msg = ref(props.message)
 // 使用 useClipboard 实现复制功能
 const { copy, isSupported } = useClipboard()
 
+const showKey = (key) => {
+  if (props.showRefs === true) {
+    return true
+  }
+  return props.showRefs.includes(key)
+}
+
 // 定义 copy 方法
 const copyText = async (text) => {
   if (isSupported) {

web/src/components/TokenManagerComponent.vue

@@ -0,0 +1,258 @@
+<template>
+  <div class="token-manager">
+    <div class="token-tools">
+      <a-button type="primary" size="small" @click="showAddTokenModal">
+        <PlusOutlined /> 创建 Token
+      </a-button>
+    </div>
+    <!-- 令牌列表 -->
+    <div class="token-list" v-if="tokens.length > 0">
+      <a-spin :spinning="loading">
+        <a-list size="small">
+          <a-list-item v-for="token in tokens" :key="token.id">
+            <div class="token-item">
+              <div class="token-info">
+                <div class="token-name">{{ token.name }}</div>
+                <div class="token-value">
+                  <code>{{ token.token }}</code>
+                  <a-button type="link" size="small" @click="copyToken(token.token)">
+                    <CopyOutlined />
+                  </a-button>
+                </div>
+                <div class="token-time">创建时间: {{ formatDate(token.created_at) }}</div>
+              </div>
+              <div class="token-actions">
+                <a-popconfirm
+                  title="确定要删除这个令牌吗?"
+                  ok-text="确定"
+                  cancel-text="取消"
+                  @confirm="deleteToken(token.id)"
+                >
+                  <a-button type="text" danger size="small">
+                    <DeleteOutlined />
+                  </a-button>
+                </a-popconfirm>
+              </div>
+            </div>
+          </a-list-item>
+        </a-list>
+      </a-spin>
+    </div>
+    <a-empty v-else description="暂无访问令牌" :image="Empty.PRESENTED_IMAGE_SIMPLE" />
+
+    <!-- 添加令牌弹窗 -->
+    <a-modal
+      v-model:open="addTokenModalVisible"
+      title="添加访问令牌"
+      ok-text="创建"
+      cancel-text="取消"
+      @ok="createToken"
+    >
+      <a-form :model="newToken" layout="vertical">
+        <a-form-item label="令牌名称" name="name">
+          <a-input v-model:value="newToken.name" placeholder="请输入令牌名称" />
+        </a-form-item>
+      </a-form>
+    </a-modal>
+  </div>
+</template>
+
+<script setup>
+import { ref, onMounted, watch } from 'vue';
+import { message, Empty } from 'ant-design-vue';
+import { PlusOutlined, DeleteOutlined, CopyOutlined } from '@ant-design/icons-vue';
+
+const props = defineProps({
+  agentId: {
+    type: String,
+    required: true
+  }
+});
+
+// 状态
+const tokens = ref([]);
+const loading = ref(false);
+const addTokenModalVisible = ref(false);
+const newToken = ref({
+  name: ''
+});
+
+// 获取令牌列表
+const fetchTokens = async () => {
+  loading.value = true;
+  try {
+    const response = await fetch(`/api/admin/tokens?agent_id=${props.agentId}`);
+    if (response.ok) {
+      const data = await response.json();
+      tokens.value = data;
+    } else {
+      message.error('获取令牌列表失败');
+    }
+  } catch (error) {
+    console.error('获取令牌列表出错:', error);
+    message.error('获取令牌列表出错');
+  } finally {
+    loading.value = false;
+  }
+};
+
+// 创建新令牌
+const createToken = async () => {
+  if (!newToken.value.name.trim()) {
+    message.warning('请输入令牌名称');
+    return;
+  }
+
+  try {
+    const response = await fetch('/api/admin/tokens', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        agent_id: props.agentId,
+        name: newToken.value.name
+      })
+    });
+
+    if (response.ok) {
+      const data = await response.json();
+      tokens.value.push(data);
+      message.success('令牌创建成功');
+      addTokenModalVisible.value = false;
+      newToken.value.name = '';
+    } else {
+      message.error('创建令牌失败');
+    }
+  } catch (error) {
+    console.error('创建令牌出错:', error);
+    message.error('创建令牌出错');
+  }
+};
+
+// 删除令牌
+const deleteToken = async (tokenId) => {
+  try {
+    const response = await fetch(`/api/admin/tokens/${tokenId}`, {
+      method: 'DELETE'
+    });
+
+    if (response.ok) {
+      tokens.value = tokens.value.filter(token => token.id !== tokenId);
+      message.success('令牌已删除');
+    } else {
+      message.error('删除令牌失败');
+    }
+  } catch (error) {
+    console.error('删除令牌出错:', error);
+    message.error('删除令牌出错');
+  }
+};
+
+// 复制令牌到剪贴板
+const copyToken = (token) => {
+  navigator.clipboard.writeText(token).then(() => {
+    message.success('令牌已复制到剪贴板');
+  });
+};
+
+// 显示添加令牌弹窗
+const showAddTokenModal = () => {
+  newToken.value.name = '';
+  addTokenModalVisible.value = true;
+};
+
+// 格式化日期
+const formatDate = (dateString) => {
+  if (!dateString) return '';
+  const date = new Date(dateString);
+  return date.toLocaleString();
+};
+
+// 监听agentId变化
+watch(() => props.agentId, (newAgentId) => {
+  if (newAgentId) {
+    fetchTokens();
+  } else {
+    tokens.value = [];
+  }
+}, { immediate: true });
+
+// 组件挂载时获取令牌列表
+onMounted(() => {
+  if (props.agentId) {
+    fetchTokens();
+  }
+});
+</script>
+
+<style lang="less" scoped>
+.token-manager {
+  margin-top: 1rem;
+  // padding: 0 0.5rem;
+}
+
+.manager-title {
+  font-size: 1rem;
+  margin-bottom: 1rem;
+}
+
+.token-tools {
+  margin-bottom: 1rem;
+  display: flex;
+  justify-content: flex-end;
+}
+
+.token-list {
+  max-height: calc(100vh - 400px);
+  overflow-y: auto;
+  li.ant-list-item {
+    background-color: var(--gray-100);
+    border-radius: 0.5rem;
+    padding: 0.5rem;
+    margin-bottom: 0.5rem;
+  }
+}
+
+.token-item {
+  display: flex;
+  justify-content: space-between;
+  width: 100%;
+}
+
+.token-info {
+  flex: 1;
+}
+
+.token-name {
+  font-weight: 500;
+  margin-bottom: 0.25rem;
+}
+
+.token-value {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  font-size: 0.8rem;
+  background-color: var(--main-light-4);
+  padding: 0.25rem 0.5rem;
+  border-radius: 4px;
+  margin-bottom: 0.25rem;
+  overflow-x: auto;
+  user-select: all;
+
+  code {
+    flex: 1;
+  }
+}
+
+.token-time {
+  font-size: 0.75rem;
+  color: var(--gray-500);
+}
+
+.token-actions {
+  display: flex;
+  align-items: center;
+}
+</style>

web/src/router/index.js

@@ -31,29 +31,29 @@ const router = createRouter({
         }
       ]
     },
-    {
-      path: '/agent',
-      name: 'agent',
-      component: AppLayout,
-      children: [
-        {
-          path: '',
-          name: 'AgentMain',
-          component: () => import('../views/AgentView.vue'),
-          meta: { keepAlive: true }
-        },
-        {
-          path: ':agent_id',
-          name: 'AgentSinglePage',
-          component: () => import('../components/AgentSingleViewComponent.vue'),
-          meta: { keepAlive: false }
-        }
-      ]
-    },
+    // {
+    //   path: '/agent',
+    //   name: 'agent',
+    //   component: AppLayout,
+    //   children: [
+    //     {
+    //       path: '',
+    //       name: 'AgentMain',
+    //       component: () => import('../views/AgentView.vue'),
+    //       meta: { keepAlive: true }
+    //     },
+    //     {
+    //       path: ':agent_id',
+    //       name: 'AgentSinglePage',
+    //       component: () => import('../components/AgentSingleViewComponent.vue'),
+    //       meta: { keepAlive: false }
+    //     }
+    //   ]
+    // },
     {
       path: '/agent/:agent_id',
       name: 'AgentSinglePage',
-      component: () => import('../views/AgentView.vue'),
+      component: () => import('../views/AgentSingleView.vue'),
     },
     {
       path: '/graph',

web/src/views/AgentSingleView.vue

@@ -0,0 +1,163 @@
+<template>
+  <div class="agent-single-view">
+    <!-- Token验证弹窗 -->
+    <a-modal
+      v-model:open="tokenModalVisible"
+      title="访问验证"
+      :closable="false"
+      :maskClosable="false"
+      :keyboard="false"
+      :footer="null"
+      width="500px"
+    >
+      <div class="token-verify-form">
+        <p>需要输入访问令牌才能使用该智能体</p>
+        <a-input-password
+          v-model:value="tokenInput"
+          placeholder="请输入访问令牌"
+          @pressEnter="verifyToken"
+        />
+        <div class="error-message" v-if="errorMessage">{{ errorMessage }}</div>
+        <div class="token-actions">
+          <a-button type="primary" :loading="verifying" @click="verifyToken">验证</a-button>
+        </div>
+      </div>
+    </a-modal>
+
+    <!-- 智能体聊天界面 -->
+    <AgentChatComponent v-if="isVerified" :agent-id="agentId" />
+  </div>
+</template>
+
+<script setup>
+import { ref, computed, onMounted } from 'vue';
+import { useRoute, useRouter } from 'vue-router';
+import AgentChatComponent from '@/components/AgentChatComponent.vue';
+import { message } from 'ant-design-vue';
+
+const route = useRoute();
+const router = useRouter();
+const agentId = computed(() => route.params.agent_id);
+
+// Token验证相关状态
+const tokenModalVisible = ref(false);
+const tokenInput = ref('');
+const isVerified = ref(false);
+const verifying = ref(false);
+const errorMessage = ref('');
+
+// 验证Token
+const verifyToken = async () => {
+  if (!tokenInput.value) {
+    errorMessage.value = '请输入访问令牌';
+    return;
+  }
+
+  verifying.value = true;
+  errorMessage.value = '';
+
+  try {
+    const response = await fetch('/api/admin/verify_token', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        agent_id: agentId.value,
+        token: tokenInput.value
+      })
+    });
+
+    if (response.ok) {
+      // 验证成功
+      isVerified.value = true;
+      tokenModalVisible.value = false;
+
+      // 保存令牌到localStorage
+      localStorage.setItem(`agent-token-${agentId.value}`, tokenInput.value);
+    } else {
+      // 验证失败
+      const data = await response.json();
+      errorMessage.value = data.detail || '令牌验证失败';
+    }
+  } catch (error) {
+    console.error('验证令牌出错:', error);
+    errorMessage.value = '验证令牌时发生错误';
+  } finally {
+    verifying.value = false;
+  }
+};
+
+// 检查是否已经验证
+const checkVerification = async () => {
+  const savedToken = localStorage.getItem(`agent-token-${agentId.value}`);
+
+  if (savedToken) {
+    // 即使有保存的令牌，也要重新验证其有效性
+    verifying.value = true;
+
+    try {
+      const response = await fetch('/api/admin/verify_token', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json'
+        },
+        body: JSON.stringify({
+          agent_id: agentId.value,
+          token: savedToken
+        })
+      });
+
+      if (response.ok) {
+        // 验证成功
+        isVerified.value = true;
+        tokenInput.value = savedToken; // 保存已验证的令牌到输入框
+      } else {
+        // 令牌无效，清除本地存储并显示输入框
+        localStorage.removeItem(`agent-token-${agentId.value}`);
+        tokenModalVisible.value = true;
+      }
+    } catch (error) {
+      console.error('验证令牌出错:', error);
+      tokenModalVisible.value = true;
+    } finally {
+      verifying.value = false;
+    }
+  } else {
+    // 没有保存的令牌，显示输入框
+    tokenModalVisible.value = true;
+  }
+};
+
+// 组件挂载时检查验证状态
+onMounted(() => {
+  checkVerification();
+});
+</script>
+
+<style lang="less" scoped>
+.agent-single-view {
+  width: 100%;
+  height: 100vh;
+  overflow: hidden;
+}
+
+.token-verify-form {
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+
+  .error-message {
+    color: #ff4d4f;
+    font-size: 0.85rem;
+  }
+
+  .token-actions {
+    display: flex;
+    justify-content: space-between;
+    margin-top: 0.5rem;
+  }
+}
+</style>
+
+