原文出处：Hugging Face Agents Course · units/zh-CN/unit3/agentic-rag/tools.mdx 原作者：Hugging Face · 许可证：Apache-2.0 License 本篇为课程官方中文译文（Hugging Face 社区翻译），诸葛AI学院仅做格式整理（去除图片与交互组件），版权归原作者所有。

构建并集成智能体工具

节将为 Alfred 赋予网络访问能力，使其能够获取实时新闻与全球资讯。 同时还将集成天气数据和 Hugging Face Hub 模型下载统计功能，帮助其进行时效性话题交流。

赋予智能体网络访问能力

请记住，我们希望 Alfred 能够展现出一位真正的文艺复兴主持人的风采，并对世界有着深刻的了解。

为此，我们需要确保 Alfred 能够获取有关世界的最新新闻和信息。

让我们从为 Alfred 创建一个网络搜索工具开始吧！

```python from smolagents import DuckDuckGoSearchTool

初始化 DuckDuckGo 搜索工具

search_tool = DuckDuckGoSearchTool()

示例用法

results = search_tool("Who's the current President of France?") print(results) ```

预期输出:

法国现任总统为 Emmanuel Macron。

```python from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec from llama_index.core.tools import FunctionTool

初始化 DuckDuckGo 搜索工具

tool_spec = DuckDuckGoSearchToolSpec()

search_tool = FunctionTool.from_defaults(tool_spec.duckduckgo_full_search)

示例用法

response = search_tool("Who's the current President of France?") print(response.raw_output[-1]['body']) ```

预期输出:

法兰西共和国总统是法国的国家元首。现任总统是 Emmanuel Macron，于2017年5月14日就任，并在2017年5月7日举行的总统选举第二轮中击败 Marine Le Pen。法国第五共和国总统名单 编号 肖像 姓名 ...

```python from langchain_community.tools import DuckDuckGoSearchRun

search_tool = DuckDuckGoSearchRun() results = search_tool.invoke("Who's the current President of France?") print(results) ```

预期输出:

Emmanuel Macron （1977年12月21日生于亚眠）法国政治家，2017年当选总统...

创建天气信息工具（烟花调度）

完美的庆典应该在晴朗的天空下燃放烟花，我们需要确保烟花不会因为恶劣天气而取消。

让我们创建一个自定义工具，用于调用外部天气 API 并获取指定位置的天气信息。

为了简单起见，我们在本例中使用了一个虚拟的天气 API。如果您想使用真实的天气 API，您可以实现一个使用 OpenWeatherMap API 的天气工具，就像Unit 1中提到的那样。

```python from smolagents import Tool import random

class WeatherInfoTool(Tool): name = "weather_info" description = "Fetches dummy weather information for a given location." inputs = { "location": { "type": "string", "description": "The location to get weather information for." } } output_type = "string"

def forward(self, location: str):
    # 虚拟天气数据
    weather_conditions = [
        {"condition": "Rainy", "temp_c": 15},
        {"condition": "Clear", "temp_c": 25},
        {"condition": "Windy", "temp_c": 20}
    ]
    # 随机选择一种天气状况
    data = random.choice(weather_conditions)
    return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"

初始化工具

weather_info_tool = WeatherInfoTool() ```

```python import random from llama_index.core.tools import FunctionTool

def get_weather_info(location: str) -> str: """Fetches dummy weather information for a given location.""" # 虚拟天气数据 weather_conditions = [ {"condition": "Rainy", "temp_c": 15}, {"condition": "Clear", "temp_c": 25}, {"condition": "Windy", "temp_c": 20} ] # 随机选择一种天气状况 data = random.choice(weather_conditions) return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"

初始化工具

weather_info_tool = FunctionTool.from_defaults(get_weather_info) ```

```python from langchain_core.tools import Tool import random

def get_weather_info(location: str) -> str: """Fetches dummy weather information for a given location.""" # 虚拟天气数据 weather_conditions = [ {"condition": "Rainy", "temp_c": 15}, {"condition": "Clear", "temp_c": 25}, {"condition": "Windy", "temp_c": 20} ] # 随机选择一种天气状况 data = random.choice(weather_conditions) return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"

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

出席此次盛会的都是 AI 开发者的精英。Alfred 希望通过讨论他们最受欢迎的模型、数据集和空间来给他们留下深刻印象。我们将创建一个工具，根据用户名从 Hugging Face Hub 获取模型统计数据。

```python from smolagents import Tool from huggingface_hub import list_models

class HubStatsTool(Tool): name = "hub_stats" description = "Fetches the most downloaded model from a specific author on the Hugging Face Hub." inputs = { "author": { "type": "string", "description": "The username of the model author/organization to find models from." } } output_type = "string"

def forward(self, author: str):
    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 = HubStatsTool()

示例用法

print(hub_stats_tool("facebook")) # Example: Get the most downloaded model by Facebook ```

预期输出:

Facebook 下载次数最多的模型是 facebook/esmfold_v1，下载次数为 12,544,550 次。

```python import random from llama_index.core.tools import FunctionTool from huggingface_hub import list_models

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 = FunctionTool.from_defaults(get_hub_stats)

示例用法

print(hub_stats_tool("facebook")) # Example: Get the most downloaded model by Facebook ```

预期输出:

Facebook 下载次数最多的模型是 facebook/esmfold_v1，下载次数为 12,544,550 次。

```python from langchain_core.tools import Tool from huggingface_hub import list_models

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." )

示例用法

print(hub_stats_tool.invoke("facebook")) # Example: Get the most downloaded model by Facebook ```

预期输出:

Facebook 下载次数最多的模型是 facebook/esmfold_v1，下载次数为 13,109,861 次。

借助 Hub Stats 工具，Alfred 现在可以通过讨论他们最受欢迎的模型来打动有影响力的 AI 开发者。

将工具与 Alfred 集成

现在我们已经拥有了所有工具，让我们将它们集成到 Alfred 的智能体中：

```python from smolagents import CodeAgent, InferenceClientModel

初始化 Hugging Face 模型

model = InferenceClientModel()

使用所有工具创建 Alfred

alfred = CodeAgent( tools=[search_tool, weather_info_tool, hub_stats_tool], model=model )

Alfred 在庆典期间可能会收到的示例查询

response = alfred.run("What is Facebook and what's their most popular model?")

print("🎩 Alfred's Response:") print(response) ```

预期输出:

🎩 Alfred's Response: Facebook 是一个社交网站，用户可以在这里互相联系、分享信息并互动。Facebook 在 Hugging Face Hub 上下载次数最多的模型是 ESMFold_v1。

```python from llama_index.core.agent.workflow import AgentWorkflow from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI

初始化 Hugging Face 模型

llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")

使用所有工具创建 Alfred

alfred = AgentWorkflow.from_tools_or_functions( [search_tool, weather_info_tool, hub_stats_tool], llm=llm )

Alfred 在庆典期间可能会收到的示例查询

response = await alfred.run("What is Facebook and what's their most popular model?")

print("🎩 Alfred's Response:") print(response) ```

预期输出:

🎩 Alfred's Response: Facebook 是一家总部位于加利福尼亚州门洛帕克的社交网络服务和科技公司。它由马克·扎克伯格创立，允许用户创建个人资料、与亲朋好友联系、分享照片和视频，以及加入基于共同兴趣的群组。Facebook 在 Hugging Face Hub 上最受欢迎的模型是“facebook/esmfold_v1”，下载量达 13,109,861 次。

```python 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

生成聊天界面，包括工具

llm = HuggingFaceEndpoint( repo_id="Qwen/Qwen2.5-Coder-32B-Instruct", huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, )

chat = ChatHuggingFace(llm=llm, verbose=True) tools = [search_tool, weather_info_tool, hub_stats_tool] chat_with_tools = chat.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：这些节点完成工作

builder.add_node("assistant", assistant) builder.add_node("tools", ToolNode(tools))

定义边：这些决定了控制流如何移动

builder.add_edge(START, "assistant") builder.add_conditional_edges( "assistant", # If the latest message requires a tool, route to tools # Otherwise, provide a direct response tools_condition, ) builder.add_edge("tools", "assistant") alfred = builder.compile()

messages = [HumanMessage(content="Who is Facebook and what's their most popular model?")] response = alfred.invoke({"messages": messages})

print("🎩 Alfred's Response:") print(response['messages'][-1].content) ```

预期输出:

🎩 Alfred's Response: Facebook 是一家社交媒体公司，以其社交网站 Facebook 以及 Instagram 和 WhatsApp 等其他服务而闻名。Facebook 在 Hugging Face Hub 上下载次数最多的模型是 facebook/esmfold_v1，下载量达 13,202,321 次。

结论

通过集成这些工具，Alfred 现在可以处理各种任务，从网页搜索到天气更新和模型统计。这确保他始终是晚会上最了解情况、最有魅力的主持人。

尝试实现一个可用于获取特定主题最新消息的工具。

完成后，在 tools.py 文件中实现您的自定义工具。