Gists
Abstract
This report details an MCP network using Google Apps Script for both server and client, enabling automated, secure Gmail processing to boost efficiency.
Introduction
Recently, I published a report titled “Building Model Context Protocol (MCP) Server with Google Apps Script,” which you can find here. In that initial report, I demonstrated the feasibility of creating an MCP server using Google Apps Script, with Claude Desktop serving as the client.
Gists
Abstract
This report investigates how Gemini handles current time information, particularly when using the Gemini API. We found that while the Gemini web interface knows the current time, the Gemini API does not inherently. Therefore, applications must explicitly provide current time information in API calls for accurate time-sensitive responses.
Introduction
The rapidly advancing field of generative AI is enabling increasingly complex tasks, particularly through the use of open protocols like the Model Context Protocol (MCP) and Agent2Agent (A2A) Protocol. These protocols facilitate sophisticated operations that often require accurate and dynamic information, including time-sensitive data. For instance, applications that manage schedules or coordinate events critically depend on precise time information.
Gists
Abstract
This report details transferring image data via Model Context Protocol (MCP) from Google Apps Script server to a Python/Gemini client, extending capabilities for multimodal applications beyond text.
Introduction
Following up on my previous report, “Building Model Context Protocol (MCP) Server with Google Apps Script” (Ref), which detailed the transfer of text data between the MCP server and client, this new report focuses on extending the protocol to handle image data. It introduces a practical method for transferring image data efficiently from the Google Apps Script-based MCP server to an MCP client. In this implementation, the MCP client was built using Python and integrated with the Gemini model, allowing for the processing and utilization of the transferred image data alongside text, thereby enabling more complex, multimodal applications within the MCP framework.
Gists
Abstract
Generative AI faces limits in processing massive datasets due to context windows. Current methods can’t analyze entire data lakes. This report presents a Gemini API approach for comprehensive big data analysis beyond typical model limits.
Introduction
The rapid advancement and widespread adoption of generative AI have been remarkable. High expectations are placed on these technologies, particularly regarding processing speed and the capacity to handle vast amounts of data. While AI processing speed continues to increase with technological progress, effectively managing and analyzing truly large datasets presents significant challenges. The current practical limits on the amount of data that can be processed or held within a model’s context window simultaneously, sometimes around a million tokens or less, depending on the model and task, restrict direct comprehensive analysis of massive data lakes.
Gitst
Abstract
This report builds on prior work using Gemini 1.0 Pro to expand Google Apps Script error messages. It highlights how the script’s execution time limit created a bottleneck, but the introduction of Gemini 1.5 Flash eliminates this issue.
Introduction
After the release of the Gemini API, I previously reported on “Expanding Error Messages of Google Apps Script using Gemini Pro API with Google Apps Script”. Ref In that report, I utilized the Gemini 1.0 Pro model. While expanding error messages proved valuable for understanding script errors in detail, Google Apps Script currently has a maximum execution time of 6 minutes. Ref This meant that processing time for content generation by the Gemini API significantly impacted the total process time when dealing with large scripts, creating a bottleneck.
Gists
Abstract
The Gemini API traditionally required specific prompts for desired output formats. This report explores two new GenerationConfig properties: “response_mime_type” and “response_schema”. These allow developers to directly specify formats like JSON, enhancing control and predictability. We analyze and compare the effectiveness of both properties for controlling Gemini API output formats.
Introduction
One of the key challenges when working with the Gemini API is ensuring the output data is delivered in the format your application requires. Traditionally, the response format heavily relied on the specific prompt you provided. For example, retrieving data as a structured JSON object necessitated including a “Return JSON” prompt within your input text. This approach could be cumbersome and error-prone if the desired format wasn’t explicitly requested.
Gists
Abstract
This report examines leveraging Gemini 1.5 API with Google Apps Script to automate sample input creation during script reverse engineering. Traditionally, this process is manual and time-consuming, especially for functions with numerous test cases. Gemini 1.5 API’s potential to streamline development by automating input generation is explored through applying reverse engineering techniques to Google Apps Script samples.
Introduction
With the release of Gemini 1.5 API, users gained the ability to process more complex data, opening doors for various application developments. This report explores the potential of using Gemini 1.5 API in conjunction with Google Apps Script to achieve reverse engineering for script development and improvement.
Gists
Overview
These are sample scripts in Python and Node.js for controlling the output format of the Gemini API using JSON schemas.
Description
In a previous report, “Taming the Wild Output: Effective Control of Gemini API Response Formats with response_mime_type,” I presented sample scripts created with Google Apps Script. Ref Following its publication, I received requests for sample scripts using Python and Node.js. This report addresses those requests by providing sample scripts in both languages.
Gists
Abstract
This report explores controlling output formats for the Gemini API. Traditionally, prompts dictated the format. A new property, “response_mime_type”, allows specifying the format (e.g., JSON) directly. Testing confirms this property improves control over output format, especially for complex JSON schemas. The recommended approach is to combine a detailed JSON schema with “response_mime_type” for clear and consistent outputs.
Introduction
One of the key challenges when working with the Gemini API is ensuring the output data is in the format your application requires. Traditionally, the response format heavily relied on the specific prompt you provided. For example, retrieving data as a JSON object necessitated including a “Return JSON” prompt within your input text. This approach could be cumbersome and introduce potential errors if the desired format wasn’t explicitly requested.
Gists
Abstract
A new Google Apps Script library, “GeminiWithFiles”, simplifies using the powerful Gemini 1.5 AI model. It lets users directly upload files for content generation or create descriptions for many images at once, making it much faster than prior methods. This is helpful for tasks involving large amounts of text or images.
Introduction
Recently, Gemini, a family of Google’s most capable AI models, has revolutionized various tasks by allowing unstructured data to be used as structured data. This breakthrough is particularly impactful for tasks involving large amounts of text or images.
Gists
Abstract
The Gemini API generates different outputs depending on the prompts. This report explains how to use function calling in the new Gemini 1.5 API to control the output format (string, number, etc.) within a script during a chat session. This allows for more flexibility in using the Gemini API’s results.
Introduction
The appearance of Gemini has already brought a wave of innovation to various fields. When the Gemini API returns a response, the format of the response is highly dependent on the input text provided as a prompt. For instance, to retrieve the output value as a JSON object, you need to explicitly include a prompt like “Return JSON” within your input. However, there can be situations where the API doesn’t return the data in the desired format.
Gists
Abstract
This report explores using Gemini, a new AI model, to parse invoices in Gmail attachments. Traditional text searching proved unreliable due to invoice format variations. Gemini’s capabilities can potentially overcome this inconsistency and improve invoice data extraction.
Introduction
After Gemini, a large language model from Google AI, has been released, it has the potential to be used for modifying various situations, including information extraction from documents. In my specific case, I work with invoices in PDF format. Until now, I relied on the direct search by a Google Apps Script to achieve this task. The script’s process involved:
Gists
Abstract
A new large language model (LLM) called Gemini with an API is now available, allowing developers to analyze vast amounts of data. This report explores trends in Google Apps Script by using the Gemini 1.5 API to analyze questions on Stack Overflow.
Introduction
The release of the LLM model Gemini as an API on Vertex AI and Google AI Studio has opened a world of possibilities. Ref The Gemini API significantly expands the potential of various scripting languages, paving the way for diverse applications. Additionally, Gemini 1.5 has recently been released in AI Studio. Ref We can expect the Gemini 1.5 API to follow suit soon.
Gists
Abstract
The Gemini API allows the generating of text from uploaded files using Google Apps Script. It expands the potential of various scripting languages for diverse applications.
Introduction
With the release of the LLM model Gemini as an API on Vertex AI and Google AI Studio, a world of possibilities has opened up. Ref The Gemini API significantly expands the potential of various scripting languages and paves the way for diverse applications. Also, recently, Gemini 1.5 in AI Studio has been released. Ref In the near future, Gemini 1.5 API will be also released soon.
Gists
Abstract
The Gemini API unlocks potential for diverse applications but requires consistent output formatting. This report proposes a method using question phrasing and API calls to craft a bespoke output, enabling seamless integration with user applications. Examples include data categorization and obtaining multiple response options.
Introduction
With the release of the LLM model Gemini as an API on Vertex AI and Google AI Studio, a world of possibilities has opened up. Ref The Gemini API significantly expands the potential of various scripting languages and paves the way for diverse applications. However, leveraging the Gemini API smoothly requires consistent output formatting, which can be tricky due to its dependence on the specific question asked.
Gists
Abstract
Gemini API on Vertex AI/Studio unlocks new applications with data retrieval and content generation through function calls. This report explores using the API for reverse engineering with a sample interpreter in Google Apps Script.
Introduction
The recent release of the LLM model Gemini as an API on Vertex AI and Google AI Studio unlocks a vast potential for new applications and methodologies. It significantly expands capabilities across diverse situations, paving the way for groundbreaking applications. Notably, the Gemini API allows data retrieval and content generation through function calls. In my recent report, “Guide to Function Calling with Gemini and Google Apps Script”, I explore function calls as a launchpad for various applications. This report showcases reverse engineering using the Gemini API, with a sample interpreter for creating sample values from a given regex using Google Apps Script.
Gists
Abstract
The Gemini API can now do semantic searches, going beyond content generation. This means it can understand the meaning of your search and provide better results, even if your words don’t exactly match the data. This report introduces the enhanced search capabilities of the Gemini API.
Introduction
The Gemini API expands its potential beyond content generation to encompass powerful semantic search capabilities. Searching existing data is crucial in various situations. However, before the introduction of generative AI, traditional search methods relied solely on keyword matching. Recent advancements in semantic search have introduced similarity search, allowing for a more nuanced understanding of queries. Combining this with the generative power of the Gemini API can significantly enhance the search results of existing data. This report explores the possibilities of enhanced search using the Gemini API.
CorporaApp was updated to v1.0.3.
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v1.0.3 (March 6, 2024)
- New method of getChunk was added. When this method is used, you can retrieve a single chunk using the resource name of chunk.
You can see the detail information here https://github.com/tanaikech/CorporaApp
CorporaApp was updated to v1.0.2.
You can see the detail information here https://github.com/tanaikech/CorporaApp
Gists
Abstract
New Gemini API opens doors for developers to integrate its AI power into apps, potentially impacting education, healthcare, and business. The latest Gemini 1.5 brings even more features. This report showcases an image bot using Gemini as one example of its diverse applications. Showcasing its diverse application potential across various fields.
Introduction
The recent release of Gemini as an accessible API on Vertex AI and Google AI Studio empowers developers to integrate its vast capabilities into their applications, potentially revolutionizing fields like education, healthcare, and business. Adding even more powerful features with the recently announced Gemini 1.5, this tool promises even greater impact. Ref and Ref I believe Gemini significantly expands the potential for diverse applications across various fields. To showcase its potential, this report introduces an image bot using Gemini with Google Apps Script and Google Drive. This is just one example of the many compelling use cases developers can build with Gemini.
Gists
Abstract
Powerful AI tool Gemini’s API release (Vertex AI & Google AI Studio) opens doors for diverse applications. Its recent upgrade to version 1.5 boosts capabilities. This report demonstrates using simple Google Apps Script function calls to leverage Gemini’s power for both data retrieval and content generation.
Introduction
The recent release of the LLM model Gemini as an API on Vertex AI and Google AI Studio unlocks a world of possibilities. Ref Excitingly, Gemini 1.5 was just announced, further expanding its capabilities. Ref I believe Gemini significantly expands the potential in various situations and paves the way for diverse applications. Notably, the Gemini API can retrieve new data and generate content through function calls. In this report, I introduce the basic flow of function calling in the Gemini API using a simple Google Apps Script.
CorporaApp was updated to v1.0.1.
You can see the detail information here https://github.com/tanaikech/CorporaApp
Gists
Abstract
New “semantic search” features in Gemini API help find desired information within its corpora. While using these features with Google Apps Script was complex, a new library simplifies the process. This report proposes using this library with Gemini-generated content to automate template processes in Google Docs and Slides, creating a more flexible workflow.
Introduction
The semantic search opens up a new wind for finding the expected values. Recently, the APIs for managing corpora have been added to Gemini API. Ref When the corpora of Gemini API is used, the semantic search can be effectively achieved. Ref However, when the corpora are tried to be used with Google Apps Script, the script is complicated cumbersome. To address this challenge, I have created a library for managing the corpora using Google Apps Script. Ref With this library, managing corpora becomes effortless, requiring only straightforward scripts.
Gists
Description
In the current stage, v1beta of Gemini API can use the corpora. Ref When the corpora are used, the values can be searched with the semantic search. In the current stage, 5 corpora can be created in a single project. And, each corpus can have 10,000 documents and 1,000,000 chunks. In this report, I would like to introduce a method for achieving the semantic search using the corpora with Google Apps Script.
Gists
Description
I have published “Flexible Labeling for Gmail using Gemini Pro API with Google Apps Script” on December 19, 2023. Today, I published “Categorization using Gemini Pro API with Google Apps Script”.
In this report, as part 2, I would like to introduce 2 sample scripts for flexible labeling for Gmail using the semantic search and the function calling of Gemini Pro API with Google Apps Script.
Usage
In order to test this script, please do the following flow.
Gists
Abstract
This report explores using the Gemini Pro API with Google Apps Script to achieve flexible data categorization.
Introduction
The recent release of the LLM model Gemini as an API on Vertex AI and Google AI Studio opens a world of possibilities. Ref and Ref I believe Gemini API significantly expands the potential of Google Apps Script and paves the way for diverse applications. In this report, I present the flexible categorization of data using Gemini Pro API with Google Apps Script.
Gists
Abstract
Gemini API unlocks semantic search for Google Apps Script, boosting its power beyond automation. This report explores the result of attempting the semantic search using Gemini Pro API with Google Apps Script.
Introduction
The recent release of the LLM model Gemini as an API on Vertex AI and Google AI Studio opens a world of possibilities. Ref and Ref I believe Gemini API significantly expands the potential of Google Apps Script and paves the way for diverse applications. In this report, I present a result for attempting the semantic search using Gemini Pro API with Google Apps Script.
Gists
Description
When the generated text can be automatically inserted into the cursor position of Google Document, Google Spreadsheet, and Google Slide, it will be useful for users. This report introduces sample scripts for achieving this.
Sample scripts
Here, I would like to introduce 3 sample scripts for a Google Document, a Google Spreadsheet, and a Google Slide.
Create an API key
These sample scripts request Gemini Pro API using an API key. So, please create your API key.
Gists
Abstract
It is considered that when the current error message of Google Apps Script is expanded, it will be useful for a lot of users. This report introduces a sample script for expanding the error message of Google Apps Script using Gemini Pro API with Google Apps Script.
Introduction
The recent release of the LLM model Gemini as an API on Vertex AI and Google AI Studio opens a world of possibilities. Ref and Ref
Gists
Abstract
The release of Gemini API is expected to expand the future of Google Apps Script. This report introduces a sample script for flexible email labeling in Gmail using Gemini API with Google Apps Script.
Introduction
The recent release of the LLM model Gemini as an API on Vertex AI and Google AI Studio opens a world of possibilities. Ref and Ref I believe Gemini API significantly expands the potential of Google Apps Script and paves the way for diverse applications. In this report, I present a sample script for flexible email labeling in Gmail using Gemini Pro API with Google Apps Script.
Gists
Abstract
Gemini LLM, now a Vertex AI/Studio API, unlocks easy document summarization and image analysis via Google Apps Script. This report details an example script for automatically creating the description of the files on Google Drive and highlights seamless integration options with API keys.
Introduction
Recently, the LLM model Gemini has been released and is now available as an API on Vertex AI and Google AI Studio. Ref and Ref This report presents a simple Google Apps Script example for automatically creating descriptions of files on Google Drive using the Gemini Pro API. It is considered that when the description of files on Google Drive can be easily created, it will help users manage a lot of files.