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app.py

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+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+from tools import search_tool, weather_info_tool, hub_stats_tool, guest_info_tool
+from retriever import docs
+
+from langchain_ollama import ChatOllama
+
+
+# 生成聊天界面，包括工具
+llm = ChatOllama(model="gpt-oss:20b", request_timeout=120.0)
+
+tools = [guest_info_tool, search_tool, weather_info_tool, hub_stats_tool]
+
+chat_with_tools = llm.bind_tools(tools)
+# 生成 AgentState 和 Agent 图
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+def assistant(state: AgentState):
+    return {
+        "messages": [chat_with_tools.invoke(state["messages"])],
+    }
+
+##  构建流程图
+builder = StateGraph(AgentState)
+
+# 定义节点：执行具体工作
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# 定义边：控制流程走向
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # 如果最新消息需要工具调用，则路由到 tools 节点
+    # 否则直接响应
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+alfred = builder.compile()
+
+#示例1
+# response = alfred.invoke({"messages": "Tell me about 'Lady Ada Lovelace' and translate output to chinese."})
+
+# print("🎩 Alfred's Response:")
+# print(response['messages'][-1].content)
+
+#示例2
+# response = alfred.invoke({"messages": "What's the weather like in Tokyo tonight? Will it be suitable for our fireworks display?and translate output to chinese."})
+
+# print("🎩 Alfred's Response:")
+# print(response['messages'][-1].content)
+
+#示例 3：给 AI 研究者留下深刻印象
+# response = alfred.invoke({"messages": "One of our guests is from Qwen. What can you tell me about their most popular model?请用中文回答"})
+
+# print("🎩 Alfred's Response:")
+# print(response['messages'][-1].content)
+
+#示例 4：组合多工具应用
+# response = alfred.invoke({"messages":"我需要与“尼古拉·特斯拉博士”讨论最近在无线能源方面的进展。你能帮我为这次对话做准备吗？"})
+
+# print("🎩 Alfred's Response:")
+# print(response['messages'][-1].content)
+
+#高级功能：对话记忆
+
+# 首次交互
+response = alfred.invoke({"messages": [HumanMessage(content="Tell me about 'Lady Ada Lovelace'. What's her background and how is she related to me?请用中文回答")]})
+
+
+print("🎩 Alfred's Response:")
+print(response['messages'][-1].content)
+print("以下是第二次对话内容")
+
+# 二次交互（引用首次内容）
+response = alfred.invoke({"messages": response["messages"] + [HumanMessage(content="What projects is she currently working on?请用中文回答")]})
+
+print("🎩 Alfred's Response:")
+print(response['messages'][-1].content)

retriever.py

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+import datasets
+from langchain_core.documents import Document
+
+# 加载数据集
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# 转换为 Document 对象
+docs = [
+    Document(
+        page_content="\n".join([
+            f"Name: {guest['name']}",
+            f"Relation: {guest['relation']}",
+            f"Description: {guest['description']}",
+            f"Email: {guest['email']}"
+        ]),
+        metadata={"name": guest["name"]}
+    )
+    for guest in guest_dataset
+]

tools.py

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+from langchain_community.retrievers import BM25Retriever
+from langchain.tools import Tool
+from langchain_community.tools import DuckDuckGoSearchRun
+from huggingface_hub import list_models
+import random
+from retriever import docs
+import requests
+
+#知识库检索工具
+bm25_retriever = BM25Retriever.from_documents(docs)
+
+def extract_text(query: str) -> str:
+    """Retrieves detailed information about gala guests based on their name or relation."""
+    results = bm25_retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results[:3]])
+    else:
+        return "No matching guest information found."
+
+guest_info_tool = Tool(
+    name="guest_info_retriever",
+    func=extract_text,
+    description="Retrieves detailed information about gala guests based on their name or relation."
+)
+
+
+#网络搜索工具
+search_tool = DuckDuckGoSearchRun()
+
+#天气查询工具
+def get_weather_info(location: str) -> str:
+    """Fetches weather information from wttr.in for a given location."""
+    url = f"https://wttr.in/{location}?format=3"  # 简洁格式：City: +天气 +温度
+    try:
+        response = requests.get(url, timeout=10)
+        return response.text
+    except Exception as e:
+        return f"Error fetching weather: {str(e)}"
+
+# 初始化工具
+weather_info_tool = Tool(
+    name="get_weather_info",
+    func=get_weather_info,
+    description="Fetches dummy weather information for a given location."
+)
+
+#为有影响力的 AI 开发者创建 Hub 统计工具
+
+def get_hub_stats(author: str) -> str:
+    """Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
+    try:
+        # 列出指定作者的模型，按下载次数排序
+        models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
+
+        if models:
+            model = models[0]
+            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+        else:
+            return f"No models found for author {author}."
+    except Exception as e:
+        return f"Error fetching models for {author}: {str(e)}"
+
+# 初始化工具
+hub_stats_tool = Tool(
+    name="get_hub_stats",
+    func=get_hub_stats,
+    description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
+)