In the video, the creator builds a research agent using Pydantic AI, demonstrating how to set it up in a Jupyter notebook and implement asynchronous searches with Duck Duck Go and Tavily. The agent is designed to generate relevant search queries and provide organized results, showcasing its flexibility through tests on various research topics like Sam Altman’s biography and the latest AI news.
In the latest video, the creator continues exploring Pydantic AI by building a research agent, which is designed to facilitate research queries similar to platforms like Perplexity. The video aims to demonstrate how Pydantic AI simplifies the process of creating such agents. The setup is done in a Jupyter notebook to allow viewers to easily replicate the process without needing to configure their own environments. The creator mentions the installation of various components and the necessity of restarting the notebook after installation.
The video introduces two search engines: Duck Duck Go and Tavily. While Duck Duck Go is typically favored, it currently faces rate limiting issues, prompting the creator to opt for Tavily, which offers a generous free tier of 1,000 API calls per month. The creator explains how to implement asynchronous calls for both search engines, showcasing how to perform basic searches and context searches that yield more detailed results. This flexibility allows users to choose the search engine that best suits their needs.
The creator outlines the structure of the research agent, which includes defining a search data class for managing maximum results and a result type using Pydantic data classes. The result type specifies the desired output format, including a research title, main content, and bullet-point summaries. The agent is set up to utilize OpenAI’s GPT-4o model, with the ability to switch to other models easily. The system prompt guides the agent in generating relevant search queries based on user input, allowing it to determine how many searches to conduct.
As the video progresses, the creator demonstrates how to set up the search agent tool, detailing the process of passing in dependencies such as maximum results and the search query. The agent is designed to execute multiple asynchronous searches based on the keywords it generates. The creator emphasizes the importance of structuring the output to ensure that the final results are organized and useful. The agent’s ability to adapt its search strategy based on the complexity of the query is highlighted, showcasing its flexibility in handling different types of research requests.
In the latter part of the video, the creator tests the research agent by querying for a detailed biography of Sam Altman and later for the latest AI news. By injecting the current date into the system prompt, the agent successfully retrieves relevant and timely information. The video concludes with a discussion on the potential for further enhancements, such as incorporating additional tools for different types of searches and refining the output structure. The creator invites viewers to engage with comments and questions, promising to explore related topics in future videos.