Creating a Generic Generative Template Language in Google Gemini

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I’ve been a fan of ‘random text’ for some time. "Random text" is a bit vague, but to me the idea of using code to generate random stories, or even snippets, is fascinating. Back in April, I blogged about how I created short dragon-based stories. It took a generic string: A #adjective# dragon lives #place#. She #verb# her hoard, which consists of a #number# of #thing#, #number# of #thing#, and #number# of #thing#. She feels #feeling#. And created a story by replacing the pound-wrapped tokens with real words. I used a couple of different tools to build this, but the core one was a cool little Node library named random-word-slugs. It’s a powerful random word library... more →
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Creating a Content Assistant with Gemini and GenAI

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One of the use cases for generative AI that I’ve discussed before is the idea of using the tool to aid in the writing process. I’m not talking about creating content so much as creating suggestions and providing feedback about the content you yourself have created. This past weekend I worked on a "general purpose" tool with this in mind and thought I’d share it to get your feedback. ("You" being the smart readers of this blog who keep me honest when I show something stupid. 😉 The Application # The application itself is rather straightforward. It asks two questions – first, what are you trying to accomplish with your writing and what are the desired... more →
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Using JSON Schema with Google Gemini

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Back about a month ago, I wrote up a post on how to generate JSON results using Google Gemini, "JSON Results with Google Gemini Generative AI API Calls". While you should read that post first, the process basically boiled down to: Setting the response type of the result to JSON. Without this, Gemini will return JSON but encoded in Markdown. Using a System Instruction to give directions on the "shape" of the JSON, i.e., use this key and that key. While these techniques work well, recently yet another feature was added that makes this even better, JSON schema support. JSON Schema is an abstract way to define the shape of JSON and can be really useful in validation. The website... more →
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Tracking Gemini Models with Pipedream

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APIs, tools, consumer features, and heck, pretty much every aspect, of generative AI is changing at an incredibly rapid pace. I mostly focus on just Google Gemini and even that is pretty difficult to keep up with. Recently, Linda Lawton shared that she actually uses an automation script to keep track of the models currently available in Gemini. I thought that was a great idea and decided to see if I could build something similar using Pipedream. Here’s what I came up with. Getting Available Models via API # Normally, if I wanted to know what models I had available, I’d go to AI Studio and just look, or check the docs. While that’s fine usually, there’s actually an API... more →
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Using the Gemini File API for Prompts with Media

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Using media in your prompts (what’s called ‘multimodal’) with the Gemini API is fairly simple in small cases. You can encode your input with base64 and pass it along with your prompt. While this works well, it’s got limitations that may be quickly hit – most specifically a file size limit of 20 megs. A few months ago, I shared a demo of using your device’s camera to detect cat breeds. With today’s cameras taking incredibly detailed pictures, I hit that limit right away and had to write some code to resize the image to a smaller size. Luckily, the Gemini API has a better way of handling that, the File API. The File API # This API provides a separate method... more →
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Testing Google’s New Gemini Flash Model

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I’m currently at Google I/O waiting for the next session to start and decided to take a quick look at the latest Gemini model to be released, Flash 1.5. As the name implies, this is a ‘speedier’ model built to return responses quicker than other models, with the tradeoff that the results may not be as good. Like most things in life, there’s going to be tradeoffs. Gemini’s Pro 1.5 model will definitely be slower but will return better results. When and how you choose is… well that’s a good question, right? I decided to build a tool so I could play with this myself. The idea is to let me enter a prompt and have it run both Flash and Pro models and see both... more →
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Building a Chat Integration with Google Gemini

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It’s been on my queue to investigate how to use Generative AI in a ‘chat’ interface versus "one prompt and answer" mode for some time and today I finally got a chance to check it out. I’ll share my thoughts below, but once again I want to thank Allen Firstenberg for his help while I worked through some issues. As always, take what I’m sharing as the opinion of a developer still very new to this space. Any mistakes are my fault! What is GenAI chat? # Specifically, what is chat when it comes to generative AI? Nothing. Seriously. All ‘chat’ is taking your initial prompt, getting the result, then taking your next prompt and appending it. So for... more →
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JSON Results with Google Gemini Generative AI API Calls

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Forgive the somewhat alliterative title there, but today’s post covers something that’s been on my mind since I started playing with Google Gemini, specifically, how to get the results of your API calls in JSON. To be clear, the REST API returns a result in JSON, but I’m talking about the content of the result itself. Before I continue, a quick shot out to Allen Firstenberg who has been helping me off and on with Google Gemini stuff. Anything I get wrong though is entirely my fault. 😜 Ok, so before I go on, let’s look at a typical result. Take a prompt like so: "What is the nature of light". Pass this to Gemini via the API, and the result you get, once you... more →
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