Who wouldn't love to create their own agent like Jarvis? (well, Jarvis is more of an advanced AGI, but you get my point) I recently dove into Hugging Face's AI Agents Course, and it's been quite the journey into the realm of AI agents. The first unit of the course has just been released and it's structured to guide learners from foundational concepts to practical applications, covering topics such as agent fundamentals, frameworks, and real-world use cases.
Here are a few fundamentals that I could summarize from the course:
What is an Agent?
In simple terms, an agent is a system that uses an AI model, often a Large Language Model (LLM), to:
Understand natural language: Interpret and respond to human instructions in a meaningful way.
Reason and plan: Analyze information, make decisions, and devise strategies to solve problems.
Interact with its environment: Gather information, take actions, and observe the results of those actions.
This combination allows agents to perform complex tasks autonomously.
What is a Large Language Model (LLM)?
LLMs are AI models trained on vast amounts of text data, enabling them to understand and generate human-like language. Built upon the Transformer architecture, LLMs consist of millions to billions of parameters and excel in tasks such as text generation, translation, and summarization. They serve as the "brain" of an agent, processing inputs and determining appropriate responses or actions.
The Thought-Action-Observation Cycle
Agents operate through a continuous loop known as the Thought-Action-Observation cycle:
Thought: The agent decides on the next step based on its reasoning.
Action: The agent performs an action, often by utilizing tools or interacting with its environment.
Observation: The agent reflects on the outcome of the action, gathering new information to inform subsequent decisions.
This iterative process allows agents to adapt and refine their behavior to achieve specific objectives.
My Project: NSE Stock Price Retrieval Agent
Inspired by the course and as a part of the final assignment in Unit 1, I developed an AI agent that retrieves stock prices from the National Stock Exchange (NSE) of India. Leveraging the yfinance library, the agent fetches real-time stock data based on user input. This project involved creating a custom tool within the agent to access and process stock information, demonstrating the practical application of integrating external tools to enhance agent capabilities.
Participating in Hugging Face's AI Agents Course has been an enriching experience for me so far, the rest of the curriculum does look very promising!
If you're curious to see the agent I built in action, you can try it here: https://huggingface.co/spaces/iamnamas/nse_stockprice_agent
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