Introduction to Hugging Face’s SmolAgents Framework
Hugging Face’s SmolAgents framework offers a simple and efficient method for creating AI agents that utilize tools such as web search and code execution. This guide illustrates how to develop an AI-powered research assistant capable of autonomously searching the web and summarizing articles using SmolAgents. The implementation is straightforward, requiring minimal setup, and demonstrates the potential of AI agents in automating tasks like research, summarization, and information retrieval.
Installation of Required Libraries
To begin, install the necessary libraries:
!pip install smolagents beautifulsoup4This command installs smolagents and beautifulsoup4, enabling AI agents to perform web searches and execute code, as well as parse HTML and extract text from web pages.
Secure API Token Storage
Next, securely input and store your Hugging Face API token:
import os
from getpass import getpass
os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass("Enter your Hugging Face API token: ")This code uses getpass() to prompt users for their token without displaying it, ensuring security. The token is stored as an environment variable, allowing authenticated access to Hugging Face’s Inference API for running AI models.
Creating the AI Agent
Now, initialize the AI agent using the SmolAgents framework:
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
model = HfApiModel()
tools = [DuckDuckGoSearchTool()]
agent = CodeAgent(tools=tools, model=model, additional_authorized_imports=["requests", "bs4"])This setup loads a Hugging Face API-based language model and automatically detects the stored API token for authentication. The agent is equipped with DuckDuckGoSearchTool() for web searches and is instantiated with necessary tool access and authorized imports.
Running the Agent
To utilize the agent, input a query:
query = "Summarize the main points of the Wikipedia article on Hugging Face (the company)."
result = agent.run(query)
print("Agent's final answer:", result)This code sends a query to the AI agent, prompting it to summarize the main points of the specified Wikipedia article. The agent performs a web search, retrieves relevant content, and generates a summary, which is then displayed.
Conclusion
By following this guide, you have successfully built an AI-powered research assistant using Hugging Face SmolAgents. This implementation showcases the capabilities of AI agents in automating research tasks, facilitating efficient information retrieval and processing. Beyond web search and summarization, SmolAgents can be adapted for various applications, including automated coding assistants, personal task managers, and AI-driven chatbots.
Next Steps
Explore how artificial intelligence can enhance your business operations:
- Identify processes that can be automated and areas where AI can add value in customer interactions.
- Establish key performance indicators (KPIs) to measure the impact of your AI investments.
- Select tools that align with your business needs and allow for customization.
- Start with a small project, evaluate its effectiveness, and gradually expand your AI initiatives.
If you require assistance in managing AI in your business, please contact us at hello@itinai.ru or connect with us on Telegram, X, and LinkedIn.
