Mscc.GenerativeAI 0.4.5

Gemini AI Client for .NET

Access the Gemini API in Google AI Studio and Google Cloud Vertex AI.

Getting Started

Add the package using the dotnet command line tool in your .NET project folder.

> dotnet add package Mscc.GenerativeAI

Alternatively, if you are working with Visual Studio use the NuGet package manager to add a reference to Mscc.GenerativeAI.

PM> Install-Package Mscc.GenerativeAI

You can then add this code to your sources whenever you need to access any Gemini API provided by Google. This package works for Google AI (Google AI Studio) and Google Cloud Vertex AI.

Authentication use cases

The package supports the following use cases to authenticate.

• Google AI: Authentication with an API key
• Google AI: Authentication with OAuth
• Vertex AI: Authentication with Application Default Credentials (ADC)

This applies mainly to the instantiation procedure.

Examples

Use of Gemini API in either Google AI or Vertex AI is almost identical. The major difference is the way to instantiate the model handling your prompt.

Choose an API and authentication mode

Google AI with an API key

using Mscc.GenerativeAI;
// Google AI with an API key
var model = new GenerativeModel(apiKey: "your API key", model: Model.GeminiPro);

Google AI with OAuth. Use gcloud auth application-default print-access-token to get the access token.

using Mscc.GenerativeAI;
// Google AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var model = new GenerativeModel(model: Model.GeminiPro);
model.AccessToken = accessToken;

Vertex AI with OAuth. Use gcloud auth application-default print-access-token to get the access token.

using Mscc.GenerativeAI;
// Vertex AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var vertex = new VertexAI(projectId: projectId, region: region);
var model = vertex.GenerativeModel(model: Model.GeminiPro);
model.AccessToken = accessToken;

The ConfigurationFixture type in the test project implements multiple options to retrieve sensitive information, ie. API key or access token.

Using Google AI Gemini API

Working with Google AI in your application requires an API key. Get an API key from Google AI Studio.

using Mscc.GenerativeAI;

var apiKey = "your_api_key";

var prompt = "Write a story about a magic backpack.";
var model = new GenerativeModel(apiKey: apiKey, model: Model.GeminiPro);

var response = model.GenerateContent(prompt).Result;
Console.WriteLine(response.Text);

Using Vertex AI Gemini API

Use of Vertex AI requires an account on Google Cloud, a project with billing and Vertex AI API enabled.

using Mscc.GenerativeAI;

var projectId = "your_google_project_id";	// the ID of a project, not its name.
var region = "us-central1";					// see documentation for available regions.
var accessToken = "your_access_token";      // use `gcloud auth application-default print-access-token` to get it.

var prompt = "Write a story about a magic backpack.";
var vertex = new VertexAI(projectId: projectId, region: region);
var model = vertex.GenerativeModel(model: Model.GeminiPro);
model.AccessToken = accessToken;

var response = model.GenerateContent(prompt).Result;
Console.WriteLine(response.Text);

Using the tests

The repository contains a number of test cases for Google AI and Vertex AI. You will find them in the tests folder. They are part of the [GenerativeAI solution]. To run the tests, either enter the relevant information into the appsettings.json, create a new appsettings.user.json file with the same JSON structure in the tests folder, or define the following environment variables

• GOOGLE_API_KEY
• GOOGLE_PROJECT_ID
• GOOGLE_REGION
• GOOGLE_ACCESS_TOKEN (optional: if absent, gcloud auth application-default print-access-token is executed)

The test cases should provide more insights and use cases on how to use the Mscc.GenerativeAI package in your .NET projects.

Feedback

You can create issues at the https://github.com/mscraftsman/generative-ai repository.