# Gemini API Client for .NET and ASP.NET Core
[](https://github.com/mscraftsman/generative-ai/blob/main/LICENSE)
[](https://github.com/mscraftsman/generative-ai/actions/workflows/dotnetcore.yml)
[](https://github.com/mscraftsman/generative-ai/stargazers)
Access and integrate the Gemini API into your .NET applications. This SDK allows you to connect to the Gemini API through either [Google AI Studio](https://aistudio.google.com/prompts/new_chat?model=gemini-2.0-flash-exp) or [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/gemini-v2).
The SDK is fully compatible with all Gemini API models and features, including recent additions like improved tool usage (code execution, function calling and integrated Google search grounding), and media generation (Imagen).
| Name | Package | Status |
|--------------------------------------------------------|-----------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Client for .NET | [Mscc.GenerativeAI](https://www.nuget.org/packages/Mscc.GenerativeAI/) | [](https://www.nuget.org/packages/Mscc.GenerativeAI/)[](https://www.nuget.org/packages/Mscc.GenerativeAI/) |
| Client for ASP.NET (Core) | [Mscc.GenerativeAI.Web](https://www.nuget.org/packages/Mscc.GenerativeAI.Web/) | [](https://www.nuget.org/packages/Mscc.GenerativeAI.Web/)[](https://www.nuget.org/packages/Mscc.GenerativeAI.Web/) |
| Client for .NET using Google API Client Library | [Mscc.GenerativeAI.Google](https://www.nuget.org/packages/Mscc.GenerativeAI.Google/) | [](https://www.nuget.org/packages/Mscc.GenerativeAI.Google/)[](https://www.nuget.org/packages/Mscc.GenerativeAI.Google/) |
| Client for Microsoft.Extensions.AI and Semantic Kernel | [Mscc.GenerativeAI.Microsoft](https://www.nuget.org/packages/Mscc.GenerativeAI.Microsoft/) | [](https://www.nuget.org/packages/Mscc.GenerativeAI.Microsoft/)[](https://www.nuget.org/packages/Mscc.GenerativeAI.Microsoft/) |
Read more about [Mscc.GenerativeAI.Web](./src/Mscc.GenerativeAI.Web) and how to add it to your ASP.NET (Core) web applications.
Read more about [Mscc.GenerativeAI.Google](./src/Mscc.GenerativeAI.Google).
Read more about [Mscc.GenerativeAI.Microsoft](./src/Mscc.GenerativeAI.Microsoft) and how to use it with Semantic Kernel.
## Install the package 🖥️
Install the package [Mscc.GenerativeAI](https://www.nuget.org/packages/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.
```text
> dotnet add package Mscc.GenerativeAI
```
Working with Visual Studio use the NuGet Package Manager to install the package Mscc.GenerativeAI.
```text
PM> Install-Package Mscc.GenerativeAI
```
Alternatively, add the following line to your `.csproj` file.
```text
```
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-2.0-flash-001` models are supported for tuning
- **File API**: This allows users to upload large files and use them with Gemini 2.0 and later.
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](https://ai.google.dev/tutorials/setup) | |
| Google AI | [Authentication with OAuth](https://ai.google.dev/docs/oauth_quickstart) | required for tuned models |
| Vertex AI | [Authentication with Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/provide-credentials-adc#local-dev) | |
| Vertex AI | [Authentication with Credentials by Metadata Server](https://cloud.google.com/docs/authentication/rest#metadata-server) | requires access to a metadata server |
| Vertex AI | [Authentication with OAuth]() | using [Mscc.GenerativeAI.Google](./src/Mscc.GenerativeAI.Google) |
| Vertex AI | [Authentication with Service Account](https://cloud.google.com/docs/authentication/provide-credentials-adc#service-account) | using [Mscc.GenerativeAI.Google](./src/Mscc.GenerativeAI.Google) |
| Vertex AI | [Express Mode with an API key](https://cloud.google.com/vertex-ai/generative-ai/docs/start/express-mode/overview) | |
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.Gemini25Pro*) |
| GOOGLE_API_KEY | The API key generated in Google AI Studio |
| GEMINI_API_KEY | -"- |
| GOOGLE_PROJECT_ID | Project ID in Google Cloud to access the APIs |
| GOOGLE_CLOUD_PROJECT | -"- |
| GOOGLE_REGION | Region in Google Cloud (default is *us-central1*) |
| GOOGLE_CLOUD_LOCATION | -"- |
| 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.
```csharp
using Mscc.GenerativeAI;
var model = new GenerativeModel();
```
### Choose an API and authentication mode
Google AI with an API key
```csharp
using Mscc.GenerativeAI;
// Google AI with an API key
var googleAI = new GoogleAI(apiKey: "your API key");
var model = googleAI.GenerativeModel(model: Model.Gemini25Flash);
```
Google AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
```csharp
using Mscc.GenerativeAI;
// Google AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
var accessToken = "your access token";
var googleAI = new GoogleAI(accessToken: accessToken);
var model = googleAI.GenerativeModel(model: Model.Gemini25Flash);
```
Vertex AI with OAuth. Use `gcloud auth application-default print-access-token` to get the access token.
```csharp
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, accessToken: accessToken);
var model = vertex.GenerativeModel(model: Model.Gemini25Flash);
```
Vertex AI in express mode using an API key.
```csharp
using Mscc.GenerativeAI;
// Vertex AI in express mode with an API key.
var vertex = new VertexAI(apiKey: "your API key");
var model = vertex.GenerativeModel(model: Model.Gemini25Flash);
```
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](https://aistudio.google.com/app/apikey).
```csharp
using Mscc.GenerativeAI;
var apiKey = "your_api_key";
var prompt = "Write a story about a magic backpack.";
var googleAI = new GoogleAI(apiKey: apiKey);
var model = googleAI.GenerativeModel(model: Model.Gemini25Flash);
var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);
```
### Using Vertex AI Gemini API
Use of [Vertex AI](https://console.cloud.google.com/vertex-ai) requires an account on [Google Cloud](https://console.cloud.google.com/), a project with billing and Vertex AI API enabled.
```csharp
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, accessToken: accessToken);
var model = vertex.GenerativeModel(model: Model.Gemini25Flash);
var response = await model.GenerateContent(prompt);
Console.WriteLine(response.Text);
```
### Using Vertex AI in express mode
[Vertex AI in express mode](https://cloud.google.com/vertex-ai/generative-ai/docs/start/express-mode/overview) is the fastest way to start building generative AI applications on Google Cloud. Signing up in express mode is quick and easy, and it doesn't require entering any billing information. After you sign up, you can access and use Google Cloud APIs in just a few steps.
```csharp
using Mscc.GenerativeAI;
var prompt = "Explain bubble sort to me.";
var vertex = new VertexAI(apiKey: "your API key");
var model = vertex.GenerativeModel(model: Model.Gemini25Flash);
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.
```csharp
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.Gemini25Flash,
systemInstruction: systemInstruction);
var request = new GenerateContentRequest(prompt);
var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);
```
The response might look similar to this:
```text
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 supplying 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
```csharp
class Recipe {
public string RecipeName { get; set; }
public List Ingredients { get; set; }
}
// generate structure JSON output
var apiKey = "your_api_key";
var prompt = "List a few popular cookie recipes, and include the amounts of ingredients.";
var googleAi = new GoogleAI(apiKey);
var model = googleAi.GenerativeModel(model: Model.Gemini25Flash);
var generationConfig = new GenerationConfig()
{
ResponseMimeType = "application/json",
ResponseSchema = Schema.FromType>()
};
var response = await model.GenerateContent(prompt,
generationConfig: generationConfig);
Console.WriteLine(response?.Text);
```
The output might look like this:
```json
[{"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.
```csharp
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.Gemini25Flash);
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](https://ai.google.dev/gemini-api/docs/models/gemini-v2#search-tool).
### Grounding with Google Search (deprecated)
The simplest version is to toggle the boolean property `UseGrounding`, like so.
```csharp
var apiKey = "your_api_key";
var prompt = "What is the current Google stock price?";
var genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini25Flash);
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.
```csharp
var apiKey = "your_api_key";
var prompt = "Who won Wimbledon this year?";
IGenerativeAI genAi = new GoogleAI(apiKey);
var model = genAi.GenerativeModel(Model.Gemini25Flash,
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
### Thinking and budget
```csharp
using Mscc.GenerativeAI;
var apiKey = "your_api_key";
var prompt = "Give me a tutorial to create a landing page";
var googleAI = new GoogleAI(apiKey: apiKey);
var model = googleAI.GenerativeModel(model: Model.Gemini25Flash);
var generationConfig = new GenerationConfig()
{
ThinkingConfig = new ThinkingConfig()
{
IncludeThoughts = true,
ThinkingBudget = 8192
}
};
var response = await model.GenerateContent(prompt,
generationConfig: generationConfig);
Console.WriteLine(response?.Text);
```
### Text-and-image input
```csharp
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 googleAI = new GoogleAI(apiKey: apiKey);
var model = googleAI.GenerativeModel(model: Model.Gemini25Flash);
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.
```csharp
using Mscc.GenerativeAI;
var apiKey = "your_api_key";
var googleAI = new GoogleAI(apiKey);
var model = googleAI.GenerativeModel(); // using default model: gemini-2.5-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.
```csharp
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.Gemini25Flash);
// 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($"File: {file.Name}");
request.AddMedia(file);
}
var response = await model.GenerateContent(request);
Console.WriteLine(response.Text);
```
Read more about [Gemini 1.5: Our next-generation model, now available for Private Preview in Google AI Studio](https://developers.googleblog.com/2024/02/gemini-15-available-for-private-preview-in-google-ai-studio.html).
### Create a tuned model
The Gemini API lets you tune models on your own data. Since it's your data and your tuned models this needs stricter access controls than API-Keys can provide.
Before you can create a tuned model, you'll need to [set up OAuth for your project](https://ai.google.dev/palm_docs/oauth_quickstart).
```csharp
using Mscc.GenerativeAI;
var projectId = "your_google_project_id"; // the ID of a project, not its name.
var accessToken = "your_access_token"; // use `gcloud auth application-default print-access-token` to get it.
var googleAI = new GoogleAI(accessToken: accessToken);
var model = googleAI.GenerativeModel(model: Model.Gemini25Flash);
model.ProjectId = projectId;
var parameters = new HyperParameters() { BatchSize = 2, LearningRate = 0.001f, EpochCount = 3 };
var dataset = new List
{
new() { TextInput = "1", Output = "2" },
new() { TextInput = "3", Output = "4" },
new() { TextInput = "-3", Output = "-2" },
new() { TextInput = "twenty two", Output = "twenty three" },
new() { TextInput = "two hundred", Output = "two hundred one" },
new() { TextInput = "ninety nine", Output = "one hundred" },
new() { TextInput = "8", Output = "9" },
new() { TextInput = "-98", Output = "-97" },
new() { TextInput = "1,000", Output = "1,001" },
new() { TextInput = "thirteen", Output = "fourteen" },
new() { TextInput = "seven", Output = "eight" },
};
var request = new CreateTunedModelRequest(Model.Gemini25Flash,
"Simply autogenerated Test model",
dataset,
parameters);
var response = await model.CreateTunedModel(request);
Console.WriteLine($"Name: {response.Name}");
Console.WriteLine($"Model: {response.Metadata.TunedModel} (Steps: {response.Metadata.TotalSteps})");
```
(This is still work in progress but operational. Future release will provide types to simplify the create request.)
Tuned models appear in your Google AI Studio library.
[](https://aistudio.google.com/app/library)
Read more about [Tune Gemini Pro in Google AI Studio or with the Gemini API](https://developers.googleblog.com/2024/03/tune-gemini-pro-in-google-ai-studio-or-gemini-api.html).
### More samples
The folders [samples](./samples) and [tests](./tests) contain more examples.
- [Sample console application](./samples/Console.Minimal.Prompt)
- [ASP.NET Core Minimal web application](./samples/Web.Minimal.Api)
- [ASP.NET Core MVP web application](./samples/Web.Mvc) (work in progress!)
## Troubleshooting ⚡
Sometimes you might have authentication warnings **HTTP 403** (Forbidden). Especially while working with OAuth-based authentication. You can fix it by re-authenticating through ADC.
```bash
gcloud config set project "$PROJECT_ID"
gcloud auth application-default login
gcloud auth application-default set-quota-project "$PROJECT_ID"
```
Make sure that the required API have been enabled.
```bash
# 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`](https://docs.microsoft.com/en-us/dotnet/core/tools/dotnet-environment-variables)
```bash
DOTNET_SYSTEM_NET_HTTP_SOCKETSHTTPHANDLER_HTTP2FLOWCONTROL_DISABLEDYNAMICWINDOWSIZING=true
```
or setting an `AppContext` switch:
```csharp
AppContext.SetSwitch("System.Net.SocketsHttpHandler.Http2FlowControl.DisableDynamicWindowSizing", true);
```
Several issues regarding this problem have been reported on GitHub:
- https://github.com/dotnet/runtime/pull/97881
- https://github.com/grpc/grpc-dotnet/issues/2361
- https://github.com/grpc/grpc-dotnet/issues/2358
## Using the tests 🧩
The repository contains a number of test cases for Google AI and Vertex AI. You will find them in the [tests](./tests) folder. They are part of the [GenerativeAI solution].
To run the tests, either enter the relevant information into the [appsettings.json](./tests/Mscc.GenerativeAI/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](https://github.com/mscraftsman/generative-ai) package in your .NET projects.
## Timeout Configuration ⏱
You have two ways to configure timeouts for API requests:
### Instance Default Timeout
You can set a default timeout for all operations on a `GenerativeModel` instance by setting its `Timeout` property (inherited from `BaseModel`). This should ideally be done after creating the model instance and before its first use:
```csharp
var model = new GenerativeModel(...);
model.Timeout = TimeSpan.FromSeconds(60);
```
### Per-Request Timeout
For finer-grained control, you can specify a timeout for individual API calls using the `RequestOptions` parameter available on methods like `GenerateContent`, `CountTokens`, etc. This timeout works via cancellation:
```csharp
var options = new RequestOptions { Timeout = TimeSpan.FromMilliseconds(500) };
await model.GenerateContent(prompt, requestOptions: options);
```
## Resource Management (IDisposable) 🗑
`GenerativeModel` (via `BaseModel`) now implements `IDisposable` to manage the lifecycle of the underlying resources. It is recommended to dispose of `GenerativeModel` instances when they are no longer needed. This can be done using a `using` statement or by manually calling `Dispose()`.
Example:
```csharp
using (var model = new GenerativeModel(...))
{
// use model
}
// model is automatically disposed here
```
Or manually:
```csharp
var model = new GenerativeModel(...);
try
{
// use model
}
finally
{
model.Dispose();
}
```
## 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 and Contributions 🤝
For support and feedback kindly create issues at the repository.
We encourage the community to contribute to this project! If there are additional features, interfaces, or improvements you would like to see, feel free to submit a pull request. Contributions of any kind are highly appreciated.
### How to Contribute 📝
- Clone or fork the repository.
- Create a new branch for your feature or bugfix.
- Commit your changes and push the branch to GitHub.
- Submit a pull request, and we will review it as soon as possible.
## Star History 🌟
## License 📜
This project is licensed under the [Apache-2.0 License](LICENSE) - see the [LICENSE](LICENSE) file for details.
## Citation 📚
If you use [Mscc.GenerativeAI](https://github.com/mscraftsman/generative-ai) in your research project, kindly cite as follows
```text
@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}
}
```
## Sponsorship 💲
If you found these libraries helpful and would like to promote continued development, please consider [sponsoring the maintainers](https://github.com/sponsors/jochenkirstaetter).
---
Created by [Jochen Kirstätter](https://jochen.kirstaetter.name/).
