AI Agents Tutorial For Beginners

Summary

This video tutorial walks viewers through the process of building AI agents using the open-source Llama 3.3 model and the F data framework. It covers setting up a local environment, creating agents for tasks like financial analysis, and integrating tools such as Yahoo Finance for real-time data. The tutorial also discusses improving model reliability, especially when using external APIs, and highlights the benefits of a UI for interacting with agents locally. By the end, users will understand how to create a team of agents to collaborate and respond to complex queries efficiently.

Takeaways

• 😀 Learn how to build an AI agent using the open-source Lama 3.3 model and the F-Data framework for stock analysis.
• 😀 The tutorial focuses on developing a Finance agent that compares stock fundamentals and analyst recommendations for Tesla and Nvidia.
• 😀 Gro Cloud is used to run the Lama 3.3 model, offering free cloud hosting for large-scale models like Lama 3.3.
• 😀 To start, you need to set up Gro Cloud and create an API key, along with installing the F-Data Python module via 'pip install FData'.
• 😀 The Finance agent can fetch live data using Yahoo Finance tools to compare stock prices, analyst recommendations, and company fundamentals.
• 😀 Customizing your agent is possible by using different tools, such as switching from Yahoo Finance to Bloomberg for financial data retrieval.
• 😀 You can create a team of agents, with different agents handling specific tasks like web searches and financial analysis, coordinated by a team lead agent.
• 😀 Using tools like the Yahoo Finance API, agents can intelligently pull the latest stock data, including analyst recommendations and market fundamentals.
• 😀 The agent’s output can be formatted in tables with markdown, ensuring clean and readable responses for stock comparisons.
• 😀 A local UI can be used to test your agent, which stores interactions in a local SQL database, providing a safe and private environment for agent development.

Q & A

What is the main focus of this video tutorial?

The video focuses on building an AI agent using the open-source LLM Lama 3.3 and the F data framework. The agent will compare two stocks (Tesla and Nvidia) based on analyst recommendations and company fundamentals.

Why is F data chosen over other frameworks like Langchain or Microsoft Autogen?

F data is chosen because it has a clean and minimal API, which makes it easier to work with. Additionally, it has gained significant popularity, with over 2.8 million agents created by November 2024, making it a promising tool for building AI agents.

What is the setup process for building the AI agent in this tutorial?

The setup involves creating a Gro Cloud account, generating an API key, and installing the F data Python module using 'pip install fdata'. Additionally, an environment file with API keys must be created to enable proper communication between the agent and the models.

What is the primary functionality of the first AI agent created in the video?

The first AI agent is a basic test agent that interacts with the Lama 3.3 model on Gro Cloud to validate the setup. It responds to simple prompts like generating a poem about two Indian delicacies, Dosa and Samosa.

How does the Finance agent compare stocks like Tesla and Nvidia?

The Finance agent compares stocks by retrieving data on analyst recommendations and company fundamentals from Yahoo Finance. It uses F data's finance tools to fetch the latest stock prices, analyst recommendations, and company information, presenting this data in a summary or table format.

Why is it important to provide a tool for fetching up-to-date data in this agent setup?

Since the Lama 3.3 model contains static knowledge (up to December 2023), it cannot provide the most current stock recommendations or financial data. Therefore, the agent uses external tools like Yahoo Finance to retrieve real-time data, ensuring the agent provides accurate, up-to-date comparisons.

What role do docstrings play in the functionality of the tools used by the agent?

Docstrings provide essential documentation for the functions in the tools, explaining their purpose and the required arguments. These docstrings help the agent map user queries (like 'analyst recommendations for Tesla and Nvidia') to the correct function calls, enabling the agent to retrieve and process the data effectively.

How do different models impact the agent's performance?

The performance of the agent depends heavily on the underlying model. For instance, Lama 3.3 might not handle certain queries as effectively as GPT-4. Using a more advanced model like OpenAI's GPT can improve the agent's ability to handle complex queries and fetch data accurately, as shown when GPT-4 successfully resolved the company symbol for F data.

What is the purpose of the 'team of agents' setup introduced in the video?

The 'team of agents' setup involves multiple agents working together to complete a task. In this example, a team lead coordinates between a web search agent (to find the latest news) and a finance agent (to retrieve stock recommendations and fundamentals). This setup demonstrates how agents can work in collaboration to handle more complex tasks.

How does the agent UI (User Interface) enhance the development and testing process?

The agent UI provides a local environment where developers can interact with their agents directly. By running a Python file and launching the UI, users can test their agents' responses to various queries. It also supports memory and stores interactions in a local SQL database, which can be useful for testing and debugging agents.