Mete Atamel

A user recently reported an intermittent error with C# and Gemini 1.5 model on Vertex AI’s streaming API. In this blog post, I want to outline what the error is, what causes it, and how to avoid it with the hopes of saving some frustration for someone out there.

Error

The user reported using Google.Cloud.AIPlatform.V1 library with version 2.27.0 to use Gemini 1.5 via Vertex AI’s streaming API and running into an intermittent System.IO.IOException.

This week, I’m speaking at 3 meetups on Gemini Code Assist. My talk has a little introduction to GenAI and Gemini, followed by a series of hands-on demos that showcase different features of Gemini Code Assist.

In the demos, I setup Gemini Code Assist in Cloud Code IDE plugin in Visual Studio Code. Then, I show how to design and create an application, explain, run, generate, test, transform code, and finish with understanding logs with the help of Gemini.

Introduction

In my recent blog post, I’ve been exploring Vertex AI’s Gemini REST API and mainly talked about the streamGenerateContent method which is a streaming API.

Recently, a new method appeared in Vertex AI docs: generateContent which is the non-streaming (unary) version of the API.

In this short blog post, I take a closer look at the new non-streaming generateContent API and explain why it makes sense to use as a simpler API when the latency is not super critical.

Everyone is excited about generative AI (gen AI) nowadays and rightfully so. You might be generating text with PaLM 2 or Gemini Pro, generating images with ImageGen 2, translating code from language to another with Codey, or describing images and videos with Gemini Pro Vision.

No matter how you’re using gen AI, at the end of the day, you’re calling an endpoint either with an SDK or a library or via a REST API. Workflows, my go-to service to orchestrate and automate other services, is more relevant than ever when it comes to gen AI.

Introduction

In my previous Using Vertex AI Gemini REST API post, I showed how to use the Gemini REST API from languages without SDK support yet such as C# and Rust.

There’s actually another way to use Gemini from languages without SDK support: GAPIC libraries. In this post, I show you how to use Vertex AI Gemini from GAPIC libraries, using C# as an example.

What is GAPIC?

At this point, you might be wondering: What’s GAPIC? GAPIC stands for Google API CodeGen. In Google Cloud, all services have auto-generated libraries from Google’s service proto files. Since these libraries are auto-generated, they’re not the easiest and most intuitive way of calling a service. Because of that, some services also have hand-written SDKs/libraries on top of GAPIC libraries.

Introduction

Back in December, Google announced Gemini, its most capable and general model so far available from Google AI Studio and Google Cloud Vertex AI.

The Try the Vertex AI Gemini API documentation page shows instructions on how to use the Gemini API from Python, Node.js, Java, and Go.

That’s great but what about other languages?

Even though there are no official SDKs/libraries for other languages yet, you can use the Gemini REST API to access the same functionality with a little bit more work on your part.

I’ve been using my blog for work-related topics since I can remember. I don’t know why but recently, I had an itch to write about non-work related topics for no particular reason. I’m not sure if anyone would care or find my non-tech writing useful but I’ll definitely find it useful for me as a mark of my consciousness at a given time, so here we go.

In the Create and deploy a new web app to Cloud Run with Duet AI post, I created a simple web application and deployed to Cloud Run using Duet AI’s help. Duet AI has been great to get a new and simple app up and running. But does it help for existing apps? Let’s figure it out.

In this blog post, I take an existing web app, explore it, test it, add a unit test, add new functionality, and add more unit tests all with the help of Duet AI. Again, I captured some lessons learned along the way to get the most out of Duet AI.

I’ve been playing with Duet AI, Google’s AI-powered collaborator, recently to see how useful it can be for my development workflow. I’m pleasantly surprised how helpful Duet AI can be when provided with specific questions with the right context.

In this blog post, I document my journey of creating and deploying a new web application to Cloud Run with Duet AI’s help. I also capture some lessons learned along the way to get the most out of Duet AI.

As you orchestrate more services with Workflows, the workflow gets more complicated with more steps, jumps, iterations, parallel branches. When the workflow execution inevitably fails at some point, you need to debug and figure out which step failed and why. So far, you only had an execution summary with inputs/outputs and logs to rely on in your execution debugging. While this was good enough for basic workflows, it didn’t provide step level debugging information.