GeminiWithFiles was updated to v2.0.2 v2.0.2 (September 26, 2024) As the option for generationConfig, the properties response_schema and temperature were added. You can see the detail information here https://github.com/tanaikech/GeminiWithFiles
Gists Abstract This report presents a method to train AI to effectively generate content from smaller, structured datasets using Python. Gemini’s token processing capabilities are leveraged to effectively utilize limited data, while techniques for interpreting CSV and JSON formats are explored. Introduction In the era of rapidly advancing artificial intelligence (AI), the ability to analyze and leverage large datasets is paramount. While RAG (Retrieval Augmented Generation) environments are often ideal for such tasks, there are scenarios where content generation needs to be achieved with smaller datasets.
Gists Abstract This report improves Gmail email labeling with Gemini API using JSON schema and leverages advancements in Gemini 1.5 Flash for faster processing. Introduction As Gemini continues to evolve, existing scripts utilizing its capabilities can be revisited to improve efficiency and accuracy. This includes the process of flexible labeling for Gmail emails using the Gemini API. I have previously explored this topic in two reports: December 19, 2023: Demonstrating Gmail label selection based solely on prompts.
Abstract This post introduces a Google Apps Script solution that automates the creation, sharing, and monitoring of multiple Google Spreadsheets, providing a more efficient and streamlined approach to managing user data. Introduction I’ve often encountered requests from clients who need to manage multiple Google Spreadsheets for various users, often by copying a template spreadsheet. In these situations, I typically propose the following approach: Create a Template Spreadsheet: This spreadsheet serves as a blueprint, containing essential elements like custom functions implemented using Google Apps Script.
Gists Abstract This report presents a method to optimize AI-generated scripts for processing costs using Gemini and Google Apps Script. By incorporating external knowledge from sources like StackOverflow, we demonstrate the effective generation of efficient scripts that minimize overhead while maintaining desired outcomes. This approach can be considered a dynamic pseudo-RAG technique. Introduction The proliferation of generative AI, exemplified by Google Gemini, has led to a surge in AI-generated scripts. This trend is evident in the growing number of questions on platforms like StackOverflow that involve AI-generated scripts.