generative-ai

Gemini AI SDK for .NET and ASP.NET Core enables developers to use Google's state-of-the-art generative AI models to build AI-powered features and applications.

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The 'Generative AI' repository provides a C# library for interacting with Google's Generative AI models, specifically the Gemini models. It allows users to access and integrate the Gemini API into .NET applications, supporting functionalities such as listing available models, generating content, creating tuned models, working with large files, starting chat sessions, and more. The repository also includes helper classes and enums for Gemini API aspects. Authentication methods include API key, OAuth, and various authentication modes for Google AI and Vertex AI. The package offers features for both Google AI Studio and Google Cloud Vertex AI, with detailed instructions on installation, usage, and troubleshooting.

README:

Gemini AI Client for .NET and ASP.NET Core

Access and integrate the Gemini API into your .NET applications. The packages support both Google AI Studio and Google Cloud Vertex AI.

Name	Package	Status
Client for .NET	Mscc.GenerativeAI
Client for ASP.NET (Core)	Mscc.GenerativeAI.Web
Client for .NET using Google API Client Library	Mscc.GenerativeAI.Google
Client for Microsoft.Extensions.AI and Semantic Kernel	Mscc.GenerativeAI.Microsoft

Read more about Mscc.GenerativeAI.Web and how to add it to your ASP.NET (Core) web applications. Read more about Mscc.GenerativeAI.Google. Read more about Mscc.GenerativeAI.Microsoft and how to use it with Semantic Kernel.

Install the package 🖥️

Install the package Mscc.GenerativeAI from NuGet. You can install the package from the command line using either the command line or the NuGet Package Manager Console. Or you add it directly to your .NET project.

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

> dotnet add package Mscc.GenerativeAI

Working with Visual Studio use the NuGet Package Manager to install the package Mscc.GenerativeAI.

PM> Install-Package Mscc.GenerativeAI

Alternatively, add the following line to your .csproj file.

  <ItemGroup>
    <PackageReference Include="Mscc.GenerativeAI" Version="2.1.1" />
  </ItemGroup>

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.

Features (as per Gemini analysis) ✦

The provided code defines a C# library for interacting with Google's Generative AI models, specifically the Gemini models. It provides functionalities to:

List available models: This allows users to see which models are available for use.
Get information about a specific model: This provides details about a specific model, such as its capabilities and limitations.
Generate content: This allows users to send prompts to a model and receive generated text in response.
Generate content stream: This allows users to receive a stream of generated text from a model, which can be useful for real-time applications.
Generate a grounded answer: This allows users to ask questions and receive answers that are grounded in provided context.
Generate embeddings: This allows users to convert text into numerical representations that can be used for tasks like similarity search.
Count tokens: This allows users to estimate the cost of using a model by counting the number of tokens in a prompt or response.
Start a chat session: This allows users to have a back-and-forth conversation with a model.
Create tuned models: This allows users to provide samples for tuning an existing model. Currently, only the text-bison-001 and gemini-1.0-pro-001 models are supported for tuning
File API: This allows users to upload large files and use them with Gemini 1.5.

The package also defines various helper classes and enums to represent different aspects of the Gemini API, such as model names, request parameters, and response data.

Authentication use cases 👥

The package supports the following use cases to authenticate.

API	Authentication	Remarks
Google AI	Authentication with an API key
Google AI	Authentication with OAuth	required for tuned models
Vertex AI	Authentication with Application Default Credentials (ADC)
Vertex AI	Authentication with Credentials by Metadata Server	requires access to a metadata server
Vertex AI	Authentication with OAuth	using Mscc.GenerativeAI.Google
Vertex AI	Authentication with Service Account	using Mscc.GenerativeAI.Google

This applies mainly to the instantiation procedure.

Getting Started 🚀

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.

Using Environment variables

In the cloud most settings are configured via environment variables (EnvVars). The ease of configuration, their widespread support and the simplicity of environment variables makes them a very interesting option.

Variable Name	Description
GOOGLE_AI_MODEL	The name of the model to use (default is Model.Gemini15Pro)
GOOGLE_API_KEY	The API key generated in Google AI Studio
GOOGLE_PROJECT_ID	Project ID in Google Cloud to access the APIs
GOOGLE_REGION	Region in Google Cloud (default is us-central1)
GOOGLE_ACCESS_TOKEN	The access token required to use models running in Vertex AI
GOOGLE_APPLICATION_CREDENTIALS	Path to the application credentials file.
GOOGLE_WEB_CREDENTIALS	Path to a Web credentials file.

Using any environment variable provides simplified access to a model.

using Mscc.GenerativeAI;

var model = new GenerativeModel();

Choose an API and authentication mode

Google AI with an API key

using Mscc.GenerativeAI;
// Google AI with an API key
var googleAI = new GoogleAI(apiKey: "your API key");
var model = googleAI.GenerativeModel(model: Model.Gemini15Pro);

// Original approach, still valid.
// 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.Gemini15Pro);
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.Gemini15Pro);
model.AccessToken = accessToken;

The ConfigurationFixture type in the test project implements multiple options to retrieve sensitive information, i.e. 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 = await model.GenerateContent(prompt);
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.Gemini15Pro);
model.AccessToken = accessToken;

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

More examples 🪄

Supported models are accessible via the Model class. Since release 0.9.0 there is support for the previous PaLM 2 models and their functionalities.

Use system instruction

The model can be injected with a system instruction that applies to all further requests. Following is an example how to instruct the model to respond like a pirate.

var apiKey = "your_api_key";
var systemInstruction = new Content("You are a friendly pirate. Speak like one.");
var prompt = "Good morning! How are you?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15ProLatest, systemInstruction: systemInstruction);
var request = new GenerateContentRequest(prompt);

var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

The response might look similar to this:

Ahoy there, matey! I be doin' finer than a freshly swabbed poop deck on this fine mornin', how about yerself?  
Shimmer me timbers, it's good to see a friendly face!  
What brings ye to these here waters?

Generate structured output

Gemini generates unstructured text by default, but some applications require structured text. For these use cases, you can constrain Gemini to respond with JSON, a structured data format suitable for automated processing.

You can control the structure of the JSON response by suppling a schema. There are two ways to supply a schema to the model:

As text in the prompt
As a structured schema supplied through model configuration

class Recipe {
    public string RecipeName { get; set; }
}

// generate structure JSON output
var apiKey = "your_api_key";
var prompt = "List a few popular cookie recipes.";
var googleAi = new GoogleAI(apiKey);
var model = googleAi.GenerativeModel(model: Model.Gemini15ProLatest);
var generationConfig = new GenerationConfig()
{
    ResponseMimeType = "application/json",
    ResponseSchema = new List<Recipe>()
};

var response = await model.GenerateContent(prompt, 
    generationConfig: generationConfig);
Console.WriteLine(response?.Text);
}

The output might look like this:

[{"recipeName": "Chocolate Chip Cookies"}, {"recipeName": "Peanut Butter Cookies"}, {"recipeName": "Snickerdoodles"}, {"recipeName": "Oatmeal Raisin Cookies"}, {"recipeName": "Sugar Cookies"}]

Use Google Search

To activate Google Search as a tool, set the boolean property UseGoogleSearch to true, like the following example.

var apiKey = "your_api_key";
var prompt = "When is the next total solar eclipse in Mauritius?";
var genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini20FlashExperimental);
model.UseGoogleSearch = true;

var response = await model.GenerateContent(prompt);
Console.WriteLine(string.Join(Environment.NewLine,
    response.Candidates![0].Content!.Parts
        .Select(x => x.Text)
        .ToArray()));

More details are described in the API documentation on Search as a tool.

Grounding with Google Search

The simplest version is to toggle the boolean property UseGrounding, like so.

var apiKey = "your_api_key";
var prompt = "What is the current Google stock price?";
var genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro002);
model.UseGrounding = true;

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

In case that you would like to have more control over the Google Search retrieval parameters, use the following approach.

var apiKey = "your_api_key";
var prompt = "Who won Wimbledon this year?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro002,
    tools: [new Tool { GoogleSearchRetrieval = 
        new(DynamicRetrievalConfigMode.ModeUnspecified, 0.06f) }]);

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

In either case, the returned Candidates item type has an additional property GroundingMetadata which provides the details of the Google Search based grounding

Text-and-image input

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Parse the time and city from the airport board shown in this image into a list, in Markdown";
var model = new GenerativeModel(apiKey: apiKey, model: Model.GeminiVisionPro);
var request = new GenerateContentRequest(prompt);
await request.AddMedia("https://raw.githubusercontent.com/mscraftsman/generative-ai/refs/heads/main/tests/Mscc.GenerativeAI/payload/timetable.png");

var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

The part of InlineData is supported by both Google AI and Vertex AI. Whereas the part FileData is restricted to Vertex AI only.

Chat conversations

Gemini enables you to have freeform conversations across multiple turns. You can interact with Gemini Pro using a single-turn prompt and response or chat with it in a multi-turn, continuous conversation, even for code understanding and generation.

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var model = new GenerativeModel(apiKey: apiKey);    // using default model: gemini-1.5-pro
var chat = model.StartChat();   // optionally pass a previous history in the constructor.

// Instead of discarding you could also use the response and access `response.Text`.
_ = await chat.SendMessage("Hello, fancy brainstorming about IT?");
_ = await chat.SendMessage("In one sentence, explain how a computer works to a young child.");
_ = await chat.SendMessage("Okay, how about a more detailed explanation to a high schooler?");
_ = await chat.SendMessage("Lastly, give a thorough definition for a CS graduate.");

// A chat session keeps every response in its history.
chat.History.ForEach(c => Console.WriteLine($"{c.Role}: {c.Text}"));

// Last request/response pair can be removed from the history.
var latest = chat.Rewind();
Console.WriteLine($"{latest.Sent} - {latest.Received}");

Use Gemini 1.5 with large files

With Gemini 1.5 you can create multimodal prompts supporting large files.

The following example uploads one or more files via File API and the created File URIs are used in the GenerateContent call to generate text.

using Mscc.GenerativeAI;

var apiKey = "your_api_key";
var prompt = "Make a short story from the media resources. The media resources are:";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini15Pro);

// Upload your large image(s).
// Instead of discarding you could also use the response and access `response.Text`.
var filePath = Path.Combine(Environment.CurrentDirectory, "verylarge.png");
var displayName = "My very large image";
_ = await model.UploadMedia(filePath, displayName);

// Create the prompt with references to File API resources.
var request = new GenerateContentRequest(prompt);
var files = await model.ListFiles();
foreach (var file in files.Where(x => x.MimeType.StartsWith("image/")))
{
    Console.WriteLine($"File: {file.Name}");
    request.AddMedia(file);
}
var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);

Create a tuned model

The Gemini API lets you tune models on your own data. Since it's your data and your tuned models this needs stricter access controls than API-Keys can provide.

Before you can create a tuned model, you'll need to set up OAuth for your project.

using Mscc.GenerativeAI;

var projectId = "your_google_project_id"; // the ID of a project, not its name.
var accessToken = "your_access_token";      // use `gcloud auth application-default print-access-token` to get it.
var model = new GenerativeModel(apiKey: null, model: Model.Gemini10Pro001)
{
    AccessToken = accessToken, ProjectId = projectId
};
var parameters = new HyperParameters() { BatchSize = 2, LearningRate = 0.001f, EpochCount = 3 };
var dataset = new List<TuningExample>
{    
    new() { TextInput = "1", Output = "2" },
    new() { TextInput = "3", Output = "4" },
    new() { TextInput = "-3", Output = "-2" },
    new() { TextInput = "twenty two", Output = "twenty three" },
    new() { TextInput = "two hundred", Output = "two hundred one" },
    new() { TextInput = "ninety nine", Output = "one hundred" },
    new() { TextInput = "8", Output = "9" },
    new() { TextInput = "-98", Output = "-97" },
    new() { TextInput = "1,000", Output = "1,001" },
    new() { TextInput = "thirteen", Output = "fourteen" },
    new() { TextInput = "seven", Output = "eight" },
};
var request = new CreateTunedModelRequest(Model.Gemini10Pro001, 
    "Simply autogenerated Test model",
    dataset,
    parameters);

var response = await model.CreateTunedModel(request);
Console.WriteLine($"Name: {response.Name}");
Console.WriteLine($"Model: {response.Metadata.TunedModel} (Steps: {response.Metadata.TotalSteps})");

(This is still work in progress but operational. Future release will provide types to simplify the create request.)

Tuned models appear in your Google AI Studio library.

More samples

The folders samples and tests contain more examples.

Sample console application
ASP.NET Core Minimal web application
ASP.NET Core MVP web application (work in progress!)

Troubleshooting ⚡

Sometimes you might have authentication warnings HTTP 403 (Forbidden). Especially while working with OAuth-based authentication. You can fix it by re-authenticating through ADC.

gcloud config set project "$PROJECT_ID"

gcloud auth application-default set-quota-project "$PROJECT_ID"
gcloud auth application-default login

Make sure that the required API have been enabled.

# ENABLE APIs
gcloud services enable aiplatform.googleapis.com

In case of long-running streaming requests it can happen that you get a HttpIOException: The response ended prematurely while waiting for the next frame from the server. (ResponseEnded). The root cause is the .NET runtime and the solution is to upgrade to the latest version of the .NET runtime. In case that you cannot upgrade you might disable dynamic window sizing as a workaround: Either using the environment variable DOTNET_SYSTEM_NET_HTTP_SOCKETSHTTPHANDLER_HTTP2FLOWCONTROL_DISABLEDYNAMICWINDOWSIZING

DOTNET_SYSTEM_NET_HTTP_SOCKETSHTTPHANDLER_HTTP2FLOWCONTROL_DISABLEDYNAMICWINDOWSIZING=true

or setting an AppContext switch:

AppContext.SetSwitch("System.Net.SocketsHttpHandler.Http2FlowControl.DisableDynamicWindowSizing", true);

Several issues regarding this problem have been reported on GitHub:

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.

Try it out 🤩

The following link opens an instance of the code repository in Google Project IDX.

This lets you work instantly with the code base without having to install anything.

Feedback ✨

For support and feedback kindly create issues at the https://github.com/mscraftsman/generative-ai repository.

License 📜

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.

Citation 📚

If you use Mscc.GenerativeAI in your research project, kindly cite as follows

@misc{Mscc.GenerativeAI,
  author = {Kirstätter, J and MSCraftsman},
  title = {Mscc.GenerativeAI - Gemini AI Client for .NET and ASP.NET Core},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  note = {https://github.com/mscraftsman/generative-ai}
}

Created by Jochen Kirstätter.

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Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 595

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.2k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k