Gists

Description

Recently, I reported on a workaround for effectively working with Google Sheets tables using Google Apps Script: Ref. This approach addressed limitations in directly retrieving table data and ranges within Apps Script. In this follow-up report, I’m excited to provide a sample script that leverages this workaround to export your valuable Google Sheets tables directly as PDF files. This functionality empowers you to easily share and distribute your data in a clear and universally accessible format.

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. The Google Docs API, on the other hand, grants finer-grained control over document elements and functionalities not readily available through the Document service.

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. This evolution has led to the introduction of novel methods and features, thereby expanding the capabilities of developers working within the Google Workspace ecosystem.

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. However, for smaller datasets, directly including data in the prompt or an uploaded file can be more efficient. Ref

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).

GeminiWithFiles was updated to v2.0.2

• v2.0.2 (September 26, 2024)

  1. 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. Ref
• January 30, 2024: Exploring label selection through both semantic search and function calls. Ref

This report introduces a new method for Gmail label selection using a JSON schema with response_mime_type: “application/json”. Thanks to Gemini’s advancements, content generation speed has significantly improved with the introduction of Gemini 1.5 Flash. Additionally, JSON schema allows for greater control over the output format. Recent research Ref suggests that this combination outperforms the previous approach using response_mime_type and response_schema separately.

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:

1. Create a Template Spreadsheet: This spreadsheet serves as a blueprint, containing essential elements like custom functions implemented using Google Apps Script.
2. Develop a Dashboard Spreadsheet: This centralized hub provides an overview of all user spreadsheets.
3. Clone and Share Spreadsheets: For each user, a copy of the template spreadsheet is created and shared with them. The URLs of these individual spreadsheets are then recorded in the dashboard.
4. Import Data Using IMPORTRANGE: The dashboard utilizes the IMPORTRANGE function to fetch data from individual user spreadsheets. This enables real-time updates on the dashboard as users edit their respective sheets.

While this manual process can be effective, it can become cumbersome and time-consuming, especially as the number of users and spreadsheets grows. To streamline this workflow and enhance efficiency, I’ve developed a Google Apps Script solution that automates many of these steps.