Gists
Abstract Google Sheets’ new Tables feature enhances data organization but lacks direct management via Apps Script. This report proposes a workaround solution using Apps Script until native support arrives.
Introduction Google Sheets recently introduced a new feature called Tables. Ref Tables offer a powerful way to organize and manage your data by transforming unformatted ranges into structured datasets with automatic headers, filtering options, and data validation capabilities. This not only improves the readability and maintainability of your spreadsheets but also allows for seamless integration with existing Google Sheets functions.
Gists
Abstract Google Apps Script offers Document service for basic document tasks and Google Docs API for advanced control, requiring more technical expertise. This report bridges the gap with sample scripts to unlock the API’s potential.
Introduction Google Apps Script provides two powerful tools for managing Google Documents: the Document service (DocumentApp) and the Google Docs API. Ref, Ref While the Document service offers a user-friendly interface for common document manipulation tasks within Apps Script, it has limitations.
Gists
Abstract This report showcases a practical application of Google Apps Script, demonstrating how new JavaScript methods can be used to create a script that automatically transfers selected rows between sheets in a Google Sheet.
Introduction JavaScript, a fundamental pillar of contemporary web development, has experienced a significant rise in popularity due to its versatility and widespread adoption. As JavaScript’s influence has expanded, so too has Google Apps Script, a cloud-based scripting language constructed on the V8 JavaScript engine.
Gists
Abstract Gemini excels at text generation with RAG for large datasets, but smaller ones benefit from prompting or data upload. This report explores using Gemini 1.5 Flash/Pro with RAG on medium-sized, Google Spreadsheet-stored datasets for improved accuracy and effectiveness.
Introduction Gemini’s text generation capabilities have seen significant advancements with the Retrieval-Augmented Generation (RAG). This approach excels for large datasets, where embedding data and querying the model leads to high-quality answers.
Gists
Abstract This research explores “pseudo function calling” in Gemini API using prompt engineering with JSON schema, bypassing model dependency limitations.
Introduction Large Language Models (LLMs) like Gemini and ChatGPT offer powerful functionalities, but their capabilities can be further extended through function calling. This feature allows the LLM to execute pre-defined functions with arguments generated based on the user’s prompt. This unlocks a wide range of applications, as demonstrated in these resources (see References).