🔑**安装与配置**: 安装smolagents beautifulsoup4，前者使AI Agent能使用网络搜索和代码执行等工具，后者是一个Python库，用于解析HTML和从网页中提取文本。

🌐**API密钥安全**: 使用getpass()安全地输入并存储Hugging Face API Token作为环境变量，确保在访问Hugging Face的推理API时进行身份验证。

🤖**Agent初始化**: 使用SmolAgents框架初始化AI Agent，配备DuckDuckGoSearchTool()进行网络搜索，并实例化CodeAgent()，授权导入requests和bs4等库，以便进行网页请求和解析HTML内容。

🔍**查询与总结**: 向AI Agent发送查询，要求其总结Hugging Face维基百科文章的要点。Agent将执行网络搜索，检索相关内容，并使用语言模型生成摘要。

Hugging Face’s SmolAgents framework provides a lightweight and efficient way to build AI agents that leverage tools like web search and code execution. In this tutorial, we demonstrate how to build an AI-powered research assistant that can autonomously search the web and summarize articles using SmolAgents. This implementation runs seamlessly, requiring minimal setup, and showcases the power of AI agents in automating real-world tasks such as research, summarization, and information retrieval.

!pip install smolagents beautifulsoup4

First, we install smolagents beautifulsoup4, which enables AI agents to use tools like web search and code execution, and BeautifulSoup4, a Python library for parsing HTML and extracting text from web pages.

import osfrom getpass import getpass# Securely input and store the Hugging Face API tokenos.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass("Enter your Hugging Face API token: ")

Now, we securely input and store the Hugging Face API token as an environment variable. It uses getpass() to prompt users to enter their token without displaying it for security. The token is then stored in os.environ[“HUGGINGFACEHUB_API_TOKEN”], allowing authenticated access to Hugging Face’s Inference API for running AI models.

from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel# Initialize the model WITHOUT passing hf_token directlymodel = HfApiModel()# Define tools (DuckDuckGo for web search)tools = [DuckDuckGoSearchTool()]# Create the agentagent = CodeAgent(tools=tools, model=model, additional_authorized_imports=["requests", "bs4"])

Now, we initialize an AI-powered agent using the SmolAgents framework. It sets up HfApiModel() to load a Hugging Face API-based language model, automatically detecting the stored API token for authentication. The agent is equipped with DuckDuckGoSearchTool() to perform web searches. Also, CodeAgent() is instantiated with tool access and authorized imports, such as requests for making web requests and bs4 for parsing HTML content.

# Example query to the agent:query = "Summarize the main points of the Wikipedia article on Hugging Face (the company)."# Run the agent with the queryresult = agent.run(query)print("\nAgent's final answer:\n", result)

Finally, we send a query to the AI agent, asking it to summarize the main points of the Wikipedia article on Hugging Face. The agent.run(query) command triggers the agent to perform a web search, retrieve relevant content, and generate a summary using the language model. Finally, the print() function displays the agent’s final answer, concisely summarizing the requested topic.

Following this tutorial, we have successfully built an AI-powered research assistant using Hugging Face SmolAgents that can autonomously search the web and summarize articles. This implementation highlights the power of AI agents in automating research tasks, making it easier to retrieve and process large amounts of information efficiently. Beyond web search and summarization, SmolAgents can be extended to various real-world applications, including automated coding assistants, personal task managers, and AI-driven chatbots.

Here is the Colab Notebook for the above project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 80k+ ML SubReddit.

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The post Step by Step Guide to Build an AI Research Assistant with Hugging Face SmolAgents: Automating Web Search and Article Summarization Using LLM-Powered Autonomous Agents appeared first on MarkTechPost.