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:

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:

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. While this indicates a significant improvement in AI performance, it’s crucial to note that AI-generated scripts may not always be optimized for processing costs, especially when the prompt fails to provide sufficient context.

UtlApp was updated to v1.0.7.

• v1.0.7 (September 4, 2024)

  1. Following 3 methods were added.
  • snake_caseToCamelCase: This method is used for converting a string of the snake case to the camel case.
  • camelCaseTosnake_case: This method is used for converting a string of the camel case to the snake case.
  • createFormDataObject: This method is used for creating the form data to HTTP request from an object.

You can see the detail information here https://github.com/tanaikech/UtlApp