The video demonstrates how to generate structured JSON outputs using Google’s Gemini model, highlighting two methods for obtaining JSON responses from text prompts and incorporating images for data extraction. The presenter also showcases the ease of use of Gemini for multimodal applications, including extracting information from articles and images, while emphasizing the potential for creative projects.
In the video, the presenter demonstrates how to obtain structured JSON outputs using Google’s Gemini model, similar to a previous tutorial on OpenAI’s model. The focus is on how to generate JSON responses from text prompts and how to incorporate images into the process to extract relevant data. The presenter emphasizes the ease of use of Gemini and its potential for multimodal applications, which combine text and images.
The tutorial begins with the installation of necessary libraries, including Newspaper3K for article extraction and the generative AI SDK for Gemini. The presenter explains that there are two methods to generate JSON outputs in Gemini. The first method, which works with both the Flash and Pro versions of Gemini, involves setting the response MIME type to application/JSON in the generation configuration. This allows users to input a standard text prompt and receive a JSON-formatted response.
The second method, exclusive to the Gemini 1.5 Pro version, allows users to define a response schema that specifies the structure of the expected JSON output. The presenter illustrates this by creating a class that includes both recipe names and ingredients, demonstrating how to retrieve a list of cookie recipes in a structured format. Although the output is still a string, the presenter shows how to convert it into a dictionary for easier access to the data.
The video also covers how to extract information from articles using Pydantic classes to define the expected output schema. The presenter explains how to convert the JSON response back into Pydantic models for structured data handling. This process allows users to access specific attributes from the JSON output, such as names and product types, similar to the previous OpenAI example.
Finally, the presenter explores the capabilities of Gemini in processing images, showcasing how to extract flight information from an image of a flight timetable. By using both the Pro and Flash models, the presenter demonstrates that users can achieve accurate results in extracting data from images while maintaining cost-effectiveness. The video concludes with a call to action for viewers to consider the creative applications of these multimodal capabilities in their own projects.
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Github:
GitHub - samwit/langchain-tutorials: A set of LangChain Tutorials from my youtube channel (updated)
GitHub - samwit/llm-tutorials: A set of LLM Tutorials from my youtube channel
Time Stamps:
00:00 Intro
00:09 Generate JSON Output with the Gemini API
00:26 Demo
00:53 Gemini JSON Structured Output
03:37 Pydantic Classes
07:06 Structured Output with Images
08:07 Using Gemini Flash