Phidata: Easily Build Autonomous AI Agents with GPT-4o!
TLDRThe video tutorial introduces Phidata, a framework that simplifies the creation of autonomous AI assistants using function calling with GPT-4o. It demonstrates building various agents, such as a web search agent and a finance assistant, by integrating tools like internet search and email capabilities. The creator guides viewers through setting up the environment, adding functionalities, and customizing agents with tools and local models. The tutorial also covers creating custom tools for internal APIs and showcases the process with examples like a hacker news API and a data analysis assistant using DuckDB. The video concludes with a research assistant example that generates a formatted markdown article, highlighting the framework's extendability and ease of building personalized AI systems.
Takeaways
- ๐ Phidata is a framework that simplifies the creation of autonomous AI assistants with function calling.
- ๐ The tutorial covers how to build AI systems with long-term memory, contextual knowledge, and action-taking capabilities using GPT 40.
- ๐ ๏ธ The video demonstrates building various agents, such as a web search agent and a finance agent, from scratch.
- ๐ง To get started, one needs to clone the Phidata repository and set up a Python virtual environment.
- ๐ Basic assistants can be created by integrating an LLM with descriptions and instructions, allowing them to respond to queries.
- ๐ Adding tools like web search capabilities can enhance the functionality of an assistant, enabling it to perform tasks like fetching news and creating content.
- ๐ฐ The finance assistant example showcases how to integrate tools like Yahoo Finance to retrieve stock prices and write comparisons.
- ๐ ๏ธ Custom tools can be created for specific tasks, such as accessing internal APIs, by defining functions and their arguments within Phidata.
- ๐ The data analysis assistant uses DuckDB to perform operations on data in CSV and Parquet files, demonstrating complex data handling.
- ๐ A research assistant example is given, which uses EXA to search the web, compile information, and generate a report in a specified format.
- ๐ The script also covers how to format responses in Markdown, handle debug mode for detailed logs, and save outputs to files.
Q & A
What is Phidata and how does it simplify the process of creating autonomous AI assistants?
-Phidata is a framework that simplifies the creation of autonomous AI assistants by allowing users to integrate any large language model (LLM) with memory, knowledge, and tools. It enables the building of intricate systems that can perform tasks such as web searches, send emails, and query cloud APIs, thus providing solutions to various problems.
What are the new upgrades in the Phidata framework that are discussed in the video?
-The new upgrades in the Phidata framework allow for the creation of autonomous AI systems with long-term memory, contextual knowledge, and the ability to take actions from function calling. The video demonstrates how to build agents with GPT 40 from scratch, including web search agents, finance agents, and others.
How does one get started with building an assistant using Phidata?
-To get started with building an assistant using Phidata, one needs to fork and clone the repository from the F data repo. Then, open the repo in a code editor and set up a Python virtual environment. The basic steps are outlined in the readme file of the repository.
What is the role of tools in transforming a basic assistant into an autonomous agent?
-Tools are integral to making an assistant autonomous. They provide the assistant with capabilities such as searching the internet, sending emails, and querying cloud APIs. These tools, when combined with an LLM and memory, turn a basic assistant into a fully functional autonomous agent.
Can you provide an example of how a basic assistant works without any tools?
-A basic assistant without tools takes an LLM, a description, and instructions, and can be asked to print a response to the command line interface (CLI) or run a query if it's part of an application. For example, the assistant built in the video uses the GPT 4 model to provide a simple breakfast recipe.
How does the web search functionality work in the Phidata framework?
-The web search functionality is achieved by importing the 'Duco' tool from Fire Tools and adding it to the assistant. The assistant is then given tasks, such as searching for news from France and writing a short poem about it. The assistant uses the Duco tool to fetch the news and generate the poem.
What is the purpose of the Yahoo Finance tool in the context of building a finance assistant?
-The Yahoo Finance tool is used to build a finance assistant that can perform tasks such as getting stock prices, company information, fundamentals, and company news. It provides the assistant with the ability to access and analyze financial data, which can then be used to generate reports or answer queries.
How can one build their own tools for use with Phidata?
-To build custom tools for use with Phidata, one needs to write their own Python code and describe what the function does, specify the arguments, and define the return values. Phidata will then format this information in a way that the LLM can understand, allowing the assistant to use these custom tools to perform tasks.
What is the significance of the debug mode in Phidata?
-The debug mode in Phidata is significant as it prints all the debug logs, which is very handy when one wants to see what's happening behind the scenes. It allows developers to understand the process flow and troubleshoot any issues that may arise during the execution of the assistant's tasks.
Can you explain how the research assistant works in the Phidata framework?
-The research assistant in the Phidata framework uses EXA to search the web, gather information, and generate a report in a specified format. It can also save the output to a file. For example, the assistant can be tasked with writing a report on a specific topic, following a given format, and saving the output to a markdown file.
Outlines
๐ค Introduction to Building Autonomous AI Systems with F-Data
This paragraph introduces the concept of F-Data, a framework for creating autonomous AI assistance with function calling. The speaker recommends watching a previous video for a deeper understanding and then proceeds to demonstrate new upgrades of the framework. The upgrades allow for the construction of AI systems with long-term memory, contextual knowledge, and the ability to take actions. The video includes a demo by the creator of F-Data, showcasing the creation of various agents using GPT 40, such as a web search agent and a finance agent. The speaker guides viewers on how to clone the F-Data repository, set up Python virtual environments, and build a basic assistant using GPT 40. The assistant can respond to commands and integrate with different models, including local ones, to provide responses or run queries as part of an application.
๐ ๏ธ Enhancing AI Assistants with Tools and Functionality
The second paragraph delves into the process of enhancing basic AI assistants with additional tools and functionality. The speaker explains how to integrate tools such as web search capabilities using the Duco tool, which is free to use. The assistant is then given tasks to perform, such as searching for news from France and writing a poem about it. The paragraph also covers the creation of a finance assistant with the Yahoo Finance tool, enabling it to access stock prices, company information, and other financial data. The speaker demonstrates how to use these tools to perform complex tasks, such as comparing Nvidia and AMD using various functions. Additionally, the paragraph touches on the topic of creating custom tools for internal APIs, using the Hacker News API as an example, and how to format responses in Markdown.
๐ Advanced AI Tools for Data Analysis and Research
The third paragraph focuses on more advanced uses of AI assistants, particularly for data analysis and research. The speaker introduces the DuctDB assistant, which utilizes DuctDB for analyzing data in CSV and Parquet files. The assistant is shown to be capable of performing tasks such as calculating the average movie rating and generating a histogram of ratings. Furthermore, the paragraph discusses the creation of a research assistant that uses EXA to search the web, gather information, and prepare a formatted report. The assistant is also tasked with saving the output to a markdown file. The speaker emphasizes the extendability of the F-Data framework, allowing users to build upon the base examples and incorporate personalized features and code.
Mindmap
Keywords
๐กPhidata
๐กAutonomous AI Systems
๐กFunction Calling
๐กGPT-40
๐กLong-term Memory
๐กContextual Knowledge
๐กWeb Search Agent
๐กFinance Agent
๐กDuct Tape (duco tool)
๐กYahoo Finance Tool
๐กCustom Tools
๐กDebug Mode
๐กCSV Files
๐กResearch Assistant
Highlights
Introduction to Phidata, a framework for building autonomous AI agents with function calling.
Demonstration of new upgrades in Phidata for creating AI systems with long-term memory and contextual knowledge.
Building agents with GPT 40 from scratch, including web search and finance agents.
Instructions on forking and cloning the Phidata repository and setting up the environment.
Creating a basic AI assistant using LLM and adding tools to make it autonomous.
Using the gp4 model for a fast and efficient assistant that can provide simple responses.
Importing a local model and integrating it with the assistant to provide customized responses.
Adding web search functionality to the assistant using the Duco tool.
Creating a finance assistant with Yahoo Finance tool and accessing various financial data.
Building a custom tool for accessing internal APIs and demonstrating its integration with the assistant.
Using DuckDB for data analysis in CSV and Parquet files with the assistant.
Creating a research assistant that uses EXA to search the web and generate reports.
Saving the output of the research assistant to a markdown file.
Extensibility of the Phidata framework allowing for personalized AI agent development.
Invitation to join Patreon for access to AI tool subscriptions and community benefits.
Encouragement to follow on Twitter for the latest AI news and subscribe for updates.
Closing remarks and call to action to explore Phidata and its capabilities further.