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).
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.