Mete Atamel

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.

In my previous post, I talked about multi-language libraries and samples for GenAI. In this post, I want to zoom into some C# specific information for GenAI in Vertex AI.

C# GenAI samples for Vertex AI

If you want to skip this blog post and just jump into code, there’s a collection of C# GenAI samples for Vertex AI.

Workflows is a versatile service in orchestrating and automating a wide range of use cases: microservices, business processes, Data and ML pipelines, IT operations, and more. It can also be used to automate deployment of containerized applications on Kubernetes Engine (GKE) and this got even easier with the newly released (in preview) Kubernetes API Connector.

The new Kubernetes API connector enables access to GKE services from Workflows and this in turn enables Kubernetes based resource management or orchestration, scheduled Kubernetes jobs, and more.

You might think that you need to know Python to be able to use GenAI with VertexAI. While Python is the dominant language in GenAI (and Vertex AI is no exception in that regard), you can actually use GenAI in Vertex AI from other languages such as Java, C#, Node.js, Go, and more.

Let’s take a look at the details.

Vertex AI SDK for Python

The official SDK for Vertex AI is Vertex AI SDK for Python and as expected, it’s in Python. You can initialize Vertex AI SDK with some parameters and utilize GenAI models with a few lines of code: