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.
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.