initial version

.gitignore

+.venv
+.env
+*.db
+*.md

01.basic-chatbot.ipynb

+%% Cell type:markdown id: tags:
+
+# Use this to design AI agent as a POC
+<h2>初始化</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 读取环境变量
+from dotenv import load_dotenv
+load_dotenv()  # 加载 .env 文件
+```
+
+%% Cell type:markdown id: tags:
+
+<hr>
+<h2>使用通义千问作为LLM</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 使用通义千问
+from langchain_community.llms import Tongyi
+
+import os
+
+# 初始化通义模型（以qwen-max为例）
+llm_tongyi = Tongyi(
+    model_name="qwen-turbo",
+    dashscope_api_key=os.getenv("DASHSCOPE_API_KEY")
+)
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>定义智能体工作流</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+from typing import Annotated
+
+from typing_extensions import TypedDict
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+class State(TypedDict):
+    # Messages have the type "list". The `add_messages` function
+    # in the annotation defines how this state key should be updated
+    # (in this case, it appends messages to the list, rather than overwriting them)
+    messages: Annotated[list, add_messages]
+
+graph_builder = StateGraph(State)
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>添加智能体节点</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+def chatbot(state: State):
+    return {"messages": [llm_tongyi.invoke(state["messages"])]}
+
+# The first argument is the unique node name
+# The second argument is the function or object that will be called whenever
+# the node is used.
+graph_builder.add_node("chatbot", chatbot)
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>添加工作流起点和终点</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 定义起点
+graph_builder.add_edge(START, "chatbot")
+# 定义终点
+graph_builder.add_edge("chatbot", END)
+
+# 完成编辑
+graph = graph_builder.compile()
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>展示工作流</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.display import Image, display
+
+try:
+    display(Image(graph.get_graph().draw_mermaid_png()))
+except Exception:
+    # This requires some extra dependencies and is optional
+    pass
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>执行工作流</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 流式交互函数（适配通义模型）
+def stream_graph_updates(user_input: str):
+    state = {"messages": [{"role": "user", "content": user_input}]}
+
+    for event in graph.stream(state):
+        for value in event.values():
+            last_message = value["messages"][-1]
+            print(f"Assistant: {last_message}")
+
+while True:
+    try:
+        user_input = input("User: ")
+        print("User: ", user_input)
+        if user_input.lower() in ["quit", "exit", "q"]:
+            print("Goodbye!")
+            break
+        stream_graph_updates(user_input)
+    except:
+        # fallback if input() is not available
+        user_input = "What do you know about LangGraph?"
+        print("User: " + user_input)
+        stream_graph_updates(user_input)
+        break
+```

02.search-enhanced-chatbot.ipynb

+%% Cell type:markdown id: tags:
+
+# Use this to design AI agent as a POC
+<h2>初始化</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 读取环境变量
+from dotenv import load_dotenv
+load_dotenv()  # 加载 .env 文件
+```
+
+%% Cell type:markdown id: tags:
+
+<hr>
+<h2>定义使用搜索引擎</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+from langchain_community.tools.tavily_search import TavilySearchResults
+
+tool = TavilySearchResults(max_results=3)
+tools = [tool]
+# tool.invoke("What's a 'node' in LangGraph?")
+```
+
+%% Cell type:markdown id: tags:
+
+<hr>
+<h2>使用通义千问作为LLM</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 使用通义千问
+from langchain_community.llms import Tongyi
+
+import os
+
+# 初始化通义模型（以qwen-max为例）
+llm_tongyi = Tongyi(
+    model_name="qwen-turbo",
+    dashscope_api_key=os.getenv("DASHSCOPE_API_KEY")
+)
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>手动实现通义的工具调用流程</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+from typing import List, Dict, Any
+from langchain_core.messages import HumanMessage
+
+def manual_tool_invocation(llm: Tongyi, tools: List, query: str) -> str:
+
+    # 0. 调用搜索引擎
+    tool.invoke(query)
+
+    # 1. 构建工具描述
+    tool_descs = "\n".join([
+        f"{tool.name}: {tool.description}\n参数: {tool.args}"
+        for tool in tools
+    ])
+    
+    # 2. 构造特殊 prompt
+    prompt = f"""请根据问题决定是否需要使用工具。可用工具：
+    {tool_descs}
+    
+    问题：{query}
+    
+    如果需要使用工具，请严格按以下格式回复：
+    ```json
+    {{"tool": "工具名", "args": {{"参数名": "参数值"}}}}
+    ```
+    如果不需要工具，请直接回答问题。"""
+    
+    # 3. 调用模型
+    response = llm.invoke(prompt)
+    
+    # 4. 解析工具调用
+    if "```json" in response:
+        try:
+            import json
+            tool_call = json.loads(response.split("```json")[1].split("```")[0].strip())
+            selected_tool = next(t for t in tools if t.name == tool_call["tool"])
+            return selected_tool.invoke(tool_call["args"])
+        except:
+            return f"工具调用失败：{response}"
+    
+    return response
+
+# 使用示例
+# result = manual_tool_invocation(llm_tongyi, tools, "What's a 'node' in LangGraph?")
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>定义智能体工作流</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+from typing import Annotated
+
+from typing_extensions import TypedDict
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+class State(TypedDict):
+    # Messages have the type "list". The `add_messages` function
+    # in the annotation defines how this state key should be updated
+    # (in this case, it appends messages to the list, rather than overwriting them)
+    messages: Annotated[list, add_messages]
+
+graph_builder = StateGraph(State)
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>添加智能体节点</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+def chatbot(state: State):
+    # return {"messages": [llm_tongyi.invoke(state["messages"])]}
+    return {"messages": [manual_tool_invocation(llm_tongyi, tools, "What's a 'node' in LangGraph?")]}
+
+# The first argument is the unique node name
+# The second argument is the function or object that will be called whenever
+# the node is used.
+graph_builder.add_node("chatbot", chatbot)
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>添加工作流起点和终点</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 定义起点
+graph_builder.add_edge(START, "chatbot")
+# 定义终点
+graph_builder.add_edge("chatbot", END)
+
+# 完成编辑
+graph = graph_builder.compile()
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>展示工作流</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.display import Image, display
+
+try:
+    display(Image(graph.get_graph().draw_mermaid_png()))
+except Exception:
+    # This requires some extra dependencies and is optional
+    pass
+```
+
+%% Cell type:markdown id: tags:
+
+<h2>执行工作流</h2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 流式交互函数（适配通义模型）
+def stream_graph_updates(user_input: str):
+    state = {"messages": [{"role": "user", "content": user_input}]}
+
+    for event in graph.stream(state):
+        for value in event.values():
+            last_message = value["messages"][-1]
+            print(f"Assistant: {last_message}")
+
+while True:
+    try:
+        user_input = input("User: ")
+        print("User: ", user_input)
+        if user_input.lower() in ["quit", "exit", "q"]:
+            print("Goodbye!")
+            break
+        stream_graph_updates(user_input)
+    except:
+        # fallback if input() is not available
+        user_input = "What do you know about LangGraph?"
+        print("User: " + user_input)
+        stream_graph_updates(user_input)
+        break
+```

03.structured-output.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# 读取环境变量
+from dotenv import load_dotenv
+load_dotenv()  # 加载 .env 文件
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# 使用通义千问
+from langchain_community.llms import Tongyi
+
+import os
+
+# 初始化通义模型（以qwen-max为例）
+llm_tongyi = Tongyi(
+    model_name="qwen-turbo",
+    dashscope_api_key=os.getenv("DASHSCOPE_API_KEY")
+)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# 构造提示，告诉模型用 JSON 输出
+prompt = """
+请根据以下用户请求，生成一个适合用于搜索的查询，并说明理由。
+用户请求: “{0}”
+
+请按照以下 JSON 格式返回：
+{{
+    "search_query": "...",
+    "justification": "..."
+}}
+"""
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+# Invoke the augmented LLM
+output = llm_tongyi.invoke(prompt.format("How does Calcium CT score relate to high cholesterol?"))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print(output)
+```

04.tool-call.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# 读取环境变量
+from dotenv import load_dotenv
+load_dotenv()  # 加载 .env 文件
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from langsmith import traceable
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# 使用通义千问
+from langchain_community.llms import Tongyi
+
+import os
+
+# 初始化通义模型（以qwen-max为例）
+llm_tongyi = Tongyi(
+    model_name="qwen-turbo",
+    dashscope_api_key=os.getenv("DASHSCOPE_API_KEY")
+)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from typing import Callable, List, Dict, Any
+import inspect
+
+@traceable
+def build_tool_prompt(tools: List[Callable], user_input: str) -> str:
+    """
+    构造多工具调用提示，用于引导模型调用正确工具。
+    """
+    tool_descriptions = []
+    for tool in tools:
+        sig = inspect.signature(tool)
+        params = ", ".join(f"{name}: {param.annotation.__name__}"
+                           for name, param in sig.parameters.items())
+        doc = inspect.getdoc(tool) or "无描述"
+        tool_descriptions.append(f"""工具名：{tool.__name__}
+描述：{doc}
+参数：{params}
+""")
+    
+    tool_block = "\n\n".join(tool_descriptions)
+    prompt = f"""
+你是一个助手，有以下工具可以调用：
+
+{tool_block}
+
+请根据用户请求，选择最合适的工具，并以如下格式返回调用指令（请使用英文括号）：
+调用：<工具名>(参数名1=值1, 参数名2=值2)
+
+如果不需要调用任何工具，请回复：无需调用工具
+
+用户请求：{user_input}
+"""
+    return prompt
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# 示例工具
+@traceable
+def multiply(a: int, b: int) -> int:
+    """两个整数相乘"""
+    return a * b
+
+@traceable
+def add(a: int, b: int) -> int:
+    """两个整数相加"""
+    return a + b
+
+TOOLS = [multiply, add]
+TOOL_MAP = {fn.__name__: fn for fn in TOOLS}
+
+# 用户输入
+user_input = "23和34的和是多少"
+
+# 构造提示并调用模型
+prompt = build_tool_prompt(TOOLS, user_input)
+response = llm_tongyi.invoke(prompt)
+
+print("🔍 模型响应：", response)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+import re
+
+@traceable
+def extract_tool_call(response: str) -> Dict[str, Any]:
+    """
+    从模型响应中提取工具调用命令。
+    返回格式：{ "tool_name": str, "args": dict }
+    """
+    pattern = r"调用：(\w+)\((.*?)\)"
+    match = re.search(pattern, response)
+    if not match:
+        return {}
+    
+    tool_name, args_str = match.groups()
+    args = {}
+    for part in args_str.split(","):
+        if "=" in part:
+            key, value = part.split("=")
+            key = key.strip()
+            value = value.strip()
+            try:
+                value = eval(value)  # 小心执行不可信输入
+            except:
+                pass
+            args[key] = value
+    return {"tool_name": tool_name, "args": args}
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# 解析调用
+tool_call = extract_tool_call(response)
+if tool_call:
+    tool_fn = TOOL_MAP.get(tool_call["tool_name"])
+    if tool_fn:
+        result = tool_fn(**tool_call["args"])
+        print("✅ 工具执行结果：", result)
+    else:
+        print("⚠️ 未知工具：", tool_call["tool_name"])
+else:
+    print("ℹ️ 模型未调用任何工具")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print(tool_call)
+```

05.database-query.ipynb

+%% Cell type:markdown id: tags:
+
+<H1>数据库查询</H1>
+<hr>
+<H2>设置环境</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 读取环境变量
+from dotenv import load_dotenv
+load_dotenv()  # 加载 .env 文件
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>定义LLM</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+# 使用通义千问
+# from langchain_community.llms import Tongyi
+# 使用封装后的ChatTongYi代替Tongyi。ChatTongYi封装了对工具的绑定功能bind_tools。
+# from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_openai import ChatOpenAI
+
+import os
+
+# 初始化通义模型（以qwen-max为例）
+# llm_chat_tongyi = ChatTongyi(
+#     model_name="qwen-plus",
+#     dashscope_api_key=os.getenv("DASHSCOPE_API_KEY")
+# )
+
+llm_chat_tongyi = ChatOpenAI(
+    api_key=os.getenv("DASHSCOPE_API_KEY"),
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    model_name="qwen-plus"
+)
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>下载Sqlite到本地</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+import requests
+
+url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db"
+
+response = requests.get(url)
+
+if response.status_code == 200:
+    # Open a local file in binary write mode
+    with open("Chinook.db", "wb") as file:
+        # Write the content of the response (the file) to the local file
+        file.write(response.content)
+    print("File downloaded and saved as Chinook.db")
+else:
+    print(f"Failed to download the file. Status code: {response.status_code}")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+from langchain_community.utilities import SQLDatabase
+
+db = SQLDatabase.from_uri("sqlite:///Chinook.db")
+
+# 单步调试
+# print(db.dialect)
+# print(db.get_usable_table_names())
+# db.run("SELECT * FROM Artist LIMIT 10;")
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>定义节点异常处理工具</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+from typing import Any
+
+from langchain_core.messages import ToolMessage
+from langchain_core.runnables import RunnableLambda, RunnableWithFallbacks
+from langgraph.prebuilt import ToolNode
+
+
+def create_tool_node_with_fallback(tools: list) -> RunnableWithFallbacks[Any, dict]:
+    """
+    Create a ToolNode with a fallback to handle errors and surface them to the agent.
+    """
+    return ToolNode(tools).with_fallbacks(
+        [RunnableLambda(handle_tool_error)], exception_key="error"
+    )
+
+
+def handle_tool_error(state) -> dict:
+    error = state.get("error")
+    tool_calls = state["messages"][-1].tool_calls
+    return {
+        "messages": [
+            ToolMessage(
+                content=f"Error: {repr(error)}\n please fix your mistakes.",
+                tool_call_id=tc["id"],
+            )
+            for tc in tool_calls
+        ]
+    }
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>引用Sqlite表定义查询工具</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+
+toolkit = SQLDatabaseToolkit(db=db, llm=llm_chat_tongyi)
+tools = toolkit.get_tools()
+
+list_tables_tool = next(tool for tool in tools if tool.name == "sql_db_list_tables")
+get_schema_tool = next(tool for tool in tools if tool.name == "sql_db_schema")
+
+# 单步调试
+# print(list_tables_tool.invoke(""))
+# print(get_schema_tool.invoke("Artist,Customer"))
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>定义Sqlite查询工具</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+from langchain_core.tools import tool
+
+
+@tool
+def db_query_tool(query: str) -> str:
+    """
+    Execute a SQL query against the database and get back the result.
+    If the query is not correct, an error message will be returned.
+    If an error is returned, rewrite the query, check the query, and try again.
+    """
+    result = db.run_no_throw(query)
+    if not result:
+        return "Error: Query failed. Please rewrite your query and try again."
+    return result
+
+# 单步调试
+# print(db_query_tool.invoke("SELECT * FROM Artist LIMIT 10;"))
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>定义LLM的Sqlite的SQL检查提示模板</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+from langchain_core.prompts import ChatPromptTemplate
+
+query_check_system = """You are a SQL expert with a strong attention to detail.
+Double check the SQLite query for common mistakes, including:
+- Using NOT IN with NULL values
+- Using UNION when UNION ALL should have been used
+- Using BETWEEN for exclusive ranges
+- Data type mismatch in predicates
+- Properly quoting identifiers
+- Using the correct number of arguments for functions
+- Casting to the correct data type
+- Using the proper columns for joins
+
+If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.
+
+You will call the appropriate tool to execute the query after running this check."""
+
+query_check_prompt = ChatPromptTemplate.from_messages(
+    [("system", query_check_system), ("placeholder", "{messages}")]
+)
+query_check = query_check_prompt | llm_chat_tongyi.bind_tools(
+    [db_query_tool], tool_choice="required"
+)
+
+# 单步调试
+# query_check.invoke({"messages": [("user", "SELECT * FROM Artist LIMIT 10;")]})
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>定义工作流</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+from typing import Annotated, Literal
+
+from langchain_core.messages import AIMessage
+from langchain_openai import ChatOpenAI
+
+from pydantic import BaseModel, Field
+from typing_extensions import TypedDict
+
+from langgraph.graph import END, StateGraph, START
+from langgraph.graph.message import AnyMessage, add_messages
+
+
+# Define the state for the agent
+class State(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+
+# Define a new graph
+workflow = StateGraph(State)
+
+
+# Add a node for the first tool call
+def first_tool_call(state: State) -> dict[str, list[AIMessage]]:
+    return {
+        "messages": [
+            AIMessage(
+                content="",
+                tool_calls=[
+                    {
+                        "name": "sql_db_list_tables",
+                        "args": {},
+                        "id": "tool_abcd123",
+                    }
+                ],
+            )
+        ]
+    }
+
+
+def model_check_query(state: State) -> dict[str, list[AIMessage]]:
+    """
+    Use this tool to double-check if your query is correct before executing it.
+    """
+    return {"messages": [query_check.invoke({"messages": [state["messages"][-1]]})]}
+
+
+workflow.add_node("first_tool_call", first_tool_call)
+
+# Add nodes for the first two tools
+workflow.add_node(
+    "list_tables_tool", create_tool_node_with_fallback([list_tables_tool])
+)
+workflow.add_node("get_schema_tool", create_tool_node_with_fallback([get_schema_tool]))
+
+# Add a node for a model to choose the relevant tables based on the question and available tables
+model_get_schema = llm_chat_tongyi.bind_tools(
+    [get_schema_tool]
+)
+workflow.add_node(
+    "model_get_schema",
+    lambda state: {
+        "messages": [model_get_schema.invoke(state["messages"])],
+    },
+)
+
+# Describe a tool to represent the end state
+class SubmitFinalAnswer(BaseModel):
+    """Submit the final answer to the user based on the query results."""
+
+    final_answer: str = Field(..., description="The final answer to the user")
+
+
+# Add a node for a model to generate a query based on the question and schema
+query_gen_system = """You are a SQL expert with a strong attention to detail.
+
+Given an input question, output a syntactically correct SQLite query to run, then look at the results of the query and return the answer.
+
+DO NOT call any tool besides SubmitFinalAnswer to submit the final answer.
+
+When generating the query:
+
+Output the SQL query that answers the input question without a tool call.
+
+Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
+You can order the results by a relevant column to return the most interesting examples in the database.
+Never query for all the columns from a specific table, only ask for the relevant columns given the question.
+
+If you get an error while executing a query, rewrite the query and try again.
+
+If you get an empty result set, you should try to rewrite the query to get a non-empty result set. 
+NEVER make stuff up if you don't have enough information to answer the query... just say you don't have enough information.
+
+If you have enough information to answer the input question, simply invoke the appropriate tool to submit the final answer to the user.
+
+DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database."""
+query_gen_prompt = ChatPromptTemplate.from_messages(
+    [("system", query_gen_system), ("placeholder", "{messages}")]
+)
+query_gen = query_gen_prompt | llm_chat_tongyi.bind_tools(
+    [SubmitFinalAnswer]
+)
+
+
+def query_gen_node(state: State):
+    message = query_gen.invoke(state)
+
+    # Sometimes, the LLM will hallucinate and call the wrong tool. We need to catch this and return an error message.
+    tool_messages = []
+    if message.tool_calls:
+        for tc in message.tool_calls:
+            if tc["name"] != "SubmitFinalAnswer":
+                tool_messages.append(
+                    ToolMessage(
+                        content=f"Error: The wrong tool was called: {tc['name']}. Please fix your mistakes. Remember to only call SubmitFinalAnswer to submit the final answer. Generated queries should be outputted WITHOUT a tool call.",
+                        tool_call_id=tc["id"],
+                    )
+                )
+    else:
+        tool_messages = []
+    return {"messages": [message] + tool_messages}
+
+
+workflow.add_node("query_gen", query_gen_node)
+
+# Add a node for the model to check the query before executing it
+workflow.add_node("correct_query", model_check_query)
+
+# Add node for executing the query
+workflow.add_node("execute_query", create_tool_node_with_fallback([db_query_tool]))
+
+
+# Define a conditional edge to decide whether to continue or end the workflow
+def should_continue(state: State) -> Literal[END, "correct_query", "query_gen"]:
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If there is a tool call, then we finish
+    if getattr(last_message, "tool_calls", None):
+        return END
+    if last_message.content.startswith("Error:"):
+        return "query_gen"
+    else:
+        return "correct_query"
+
+
+# Specify the edges between the nodes
+workflow.add_edge(START, "first_tool_call")
+workflow.add_edge("first_tool_call", "list_tables_tool")
+workflow.add_edge("list_tables_tool", "model_get_schema")
+workflow.add_edge("model_get_schema", "get_schema_tool")
+workflow.add_edge("get_schema_tool", "query_gen")
+workflow.add_conditional_edges(
+    "query_gen",
+    should_continue,
+)
+workflow.add_edge("correct_query", "execute_query")
+workflow.add_edge("execute_query", "query_gen")
+
+# Compile the workflow into a runnable
+app = workflow.compile()
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>展示工作流</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+from IPython.display import Image, display
+from langchain_core.runnables.graph import MermaidDrawMethod
+
+display(
+    Image(
+        app.get_graph().draw_mermaid_png(
+            draw_method=MermaidDrawMethod.API,
+        )
+    )
+)
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>单步执行</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+messages = app.invoke(
+    {"messages": [("user", "Which sales agent made the most in sales in 2009?")]}
+)
+json_str = messages["messages"][-1].tool_calls[0]["args"]["final_answer"]
+json_str
+```
+
+%% Cell type:markdown id: tags:
+
+<H2>多步执行</H2>
+
+%% Cell type:code id: tags:
+
+``` python
+# for event in app.stream(
+#     {"messages": [("user", "Which sales agent made the most in sales in 2009?")]}
+# ):
+#     print(event)
+```