The video presents the development of a file search application using OpenAI’s API, highlighting the automatic conversation state management feature and the use of a vector store for storing and retrieving files. The presenter demonstrates the application by uploading text files, querying historical information, and showcasing advanced features like metadata filtering to enhance user experience and search efficiency.
In the video, the presenter discusses the development of a file search application using OpenAI’s API, specifically focusing on the new responses API that can manage conversation state automatically. The presenter explains that while the API can handle conversation management, developers have the option to manage it manually. The goal is to create a file search application that utilizes a vector store for storing and retrieving files, which can be done through the API or directly via the dashboard.
The process begins with creating text files, such as “1.txt” and “2.txt,” which contain content from Wikipedia articles. The presenter demonstrates how to upload these files to a vector store, naming it “test files” and setting an expiration policy based on inactivity. This is important as the storage incurs costs, albeit low, depending on the number of documents stored. The presenter emphasizes the need to manage the vector store effectively to avoid unnecessary charges.
Next, the presenter outlines how to set up the file search tool by copying relevant documentation and configuring the conversation state management. The video showcases the creation of a chat application that utilizes user input from the terminal, with the file search tool enabled. The presenter highlights the importance of adhering to the documentation to ensure the model functions correctly, especially since this is a new feature.
The demonstration progresses as the presenter tests the chat application, querying about historical events and figures related to the uploaded files. The application successfully retrieves relevant information from the vector store, showcasing its ability to cite sources accurately. The presenter notes that the model can distinguish between general knowledge and specific file content, which enhances the user experience by providing contextually relevant answers.
Finally, the video touches on advanced features such as metadata filtering, which allows users to narrow down search results based on specific criteria. The presenter suggests that this feature will be particularly useful when dealing with a larger number of documents. The video concludes with an invitation for viewers to explore the documentation further and hints at future discussions on more complex filtering options, emphasizing the potential of the file search tool in building robust applications.