Rystem.OpenAi 3.3.12

Unofficial Fluent C#/.NET SDK for accessing the OpenAI API (Easy swap among OpenAi and Azure OpenAi)

Last update with Cost and Tokens calculation

A simple C# .NET wrapper library to use with OpenAI's API.

Help the project

Contribute: https://www.buymeacoffee.com/keyserdsoze

Contribute: https://patreon.com/Rystem

Stars

Requirements

This library targets .NET standard 2.1 and above.

Adv

Watch out my Rystem framework to be able to do .Net webapp faster (easy integration with repository pattern or CQRS for your Azure services).

What is Rystem?

Setup

Install package Rystem.OpenAi from Nuget. Here's how via command line:

Install-Package Rystem.OpenAi

Documentation

Table of Contents

• Unofficial Fluent C#/.NET SDK for accessing the OpenAI API (Easy swap among OpenAi and Azure OpenAi)
  • Last update with Cost and Tokens calculation
  • Help the project
    • Contribute: https://www.buymeacoffee.com/keyserdsoze
    • Contribute: https://patreon.com/Rystem
  • Stars
  • Requirements
    • Adv
    • What is Rystem?
  • Setup
  • Documentation
    • Table of Contents
  • Startup Setup
  • Dependency Injection
    • Add to service collection the OpenAi service in your DI
  • Dependency Injection With Azure
    • Add to service collection the OpenAi service in your DI with Azure integration
    • Add to service collection the OpenAi service in your DI with Azure integration and app registration
    • Add to service collection the OpenAi service in your DI with Azure integration and system assigned managed identity
    • Add to service collection the OpenAi service in your DI with Azure integration and user assigned managed identity
  • Use different version
  • Dependency Injection With Factory
  • Without Dependency Injection
  • Models
    • List Models
    • Retrieve Models
    • Delete fine-tune model
  • Completions
    • Streaming
  • Chat
    • Chat Streaming
    • Chat functions
      • Simple function configuration
      • Function with framework
      • Null behavior from Function framework
  • Edits
  • Images
    • Create Image
    • Create Image Edit
    • Create Image Variation
  • Embeddings
    • Create Embedding
    • Distance for embedding
    • Which distance function should I use?
  • Audio
    • Create Transcription
    • Create Translation
  • File
    • List files
    • Upload file
    • Delete file
    • Retrieve file
    • Retrieve file content
  • Fine-Tunes
    • Create fine-tune
    • List fine-tunes
    • Retrieve fine-tune
    • Cancel fine-tune
    • List fine-tune events
    • List fine-tune events as stream
    • Delete fine-tune model
  • Moderations
    • Create moderation
  • Utilities
    • Cosine Similarity
    • Tokens
    • Cost
    • Setup price
  • Management
    • Billing
    • Deployments

Startup Setup

📖 Back to summary
You may install with Dependency Injection one or more than on integrations at the same time. Furthermore you don't need to use the Dependency Injection pattern and use a custom Setup.

Dependency Injection

📖 Back to summary

Add to service collection the OpenAi service in your DI

var apiKey = configuration["Azure:ApiKey"];
services.AddOpenAi(settings =>
{
    settings.ApiKey = apiKey;
});

Dependency Injection With Azure

Add to service collection the OpenAi service in your DI with Azure integration

When you want to use the integration with Azure, you need to specify all the models you're going to use. In the example you may find the model name for DavinciText3. You still may add a custom model, with MapDeploymentCustomModel.

builder.Services.AddOpenAi(settings =>
{
    settings.ApiKey = apiKey;
    settings.Azure.ResourceName = "AzureResourceName (Name of your deployed service on Azure)";
    settings.Azure
        .MapDeploymentTextModel("Test (The name from column 'Model deployment name' in Model deployments blade in your Azure service)", TextModelType.DavinciText3);
});

Add to service collection the OpenAi service in your DI with Azure integration and app registration

See how to create an app registration here.

var resourceName = builder.Configuration["Azure:ResourceName"];
var clientId = builder.Configuration["AzureAd:ClientId"];
var clientSecret = builder.Configuration["AzureAd:ClientSecret"];
var tenantId = builder.Configuration["AzureAd:TenantId"];
builder.Services.AddOpenAi(settings =>
{
    settings.Azure.ResourceName = resourceName;
    settings.Azure.AppRegistration.ClientId = clientId;
    settings.Azure.AppRegistration.ClientSecret = clientSecret;
    settings.Azure.AppRegistration.TenantId = tenantId;
    settings.Azure
        .MapDeploymentTextModel("Test", TextModelType.CurieText)
        .MapDeploymentTextModel("text-davinci-002", TextModelType.DavinciText2)
        .MapDeploymentEmbeddingModel("Test", EmbeddingModelType.AdaTextEmbedding);
});

Add to service collection the OpenAi service in your DI with Azure integration and system assigned managed identity

See how to create a managed identity here.
System Assigned Managed Identity

var resourceName = builder.Configuration["Azure:ResourceName"];
builder.Services.AddOpenAi(settings =>
{
    settings.Azure.ResourceName = resourceName;
    settings.Azure.ManagedIdentity.UseDefault = true;
    settings.Azure
        .MapDeploymentTextModel("Test", TextModelType.CurieText)
        .MapDeploymentTextModel("text-davinci-002", TextModelType.DavinciText2)
        .MapDeploymentEmbeddingModel("Test", EmbeddingModelType.AdaTextEmbedding);
});

Add to service collection the OpenAi service in your DI with Azure integration and user assigned managed identity

See how to create a managed identity here.
User Assigned Managed Identity

var resourceName = builder.Configuration["Azure:ResourceName"];
var managedIdentityId = builder.Configuration["ManagedIdentity:ClientId"];
builder.Services.AddOpenAi(settings =>
{
    settings.Azure.ResourceName = resourceName;
    settings.Azure.ManagedIdentity.Id = managedIdentityId;
    settings.Azure
        .MapDeploymentTextModel("Test", TextModelType.CurieText)
        .MapDeploymentTextModel("text-davinci-002", TextModelType.DavinciText2)
        .MapDeploymentEmbeddingModel("Test", EmbeddingModelType.AdaTextEmbedding);
});

Use different version

📖 Back to summary
You may install different version for each endpoint.

 services.AddOpenAi(settings =>
        {
            settings.ApiKey = azureApiKey;
            settings
                .UseVersionForChat("2023-03-15-preview");
        });

In this example We are adding a different version only for chat, and all the other endpoints will use the same (in this case the default version).

Dependency Injection With Factory

📖 Back to summary
You may install more than one OpenAi integration, using name parameter in configuration. In the next example we have two different configurations, one with OpenAi and a default name and with Azure OpenAi and name "Azure"

var apiKey = context.Configuration["OpenAi:ApiKey"];
services
    .AddOpenAi(settings =>
    {
        settings.ApiKey = apiKey;
    });
var azureApiKey = context.Configuration["Azure:ApiKey"];
var resourceName = context.Configuration["Azure:ResourceName"];
var clientId = context.Configuration["AzureAd:ClientId"];
var clientSecret = context.Configuration["AzureAd:ClientSecret"];
var tenantId = context.Configuration["AzureAd:TenantId"];
services.AddOpenAi(settings =>
{
    settings.ApiKey = azureApiKey;
    settings
        .UseVersionForChat("2023-03-15-preview");
    settings.Azure.ResourceName = resourceName;
    settings.Azure.AppRegistration.ClientId = clientId;
    settings.Azure.AppRegistration.ClientSecret = clientSecret;
    settings.Azure.AppRegistration.TenantId = tenantId;
    settings.Azure
        .MapDeploymentTextModel("text-curie-001", TextModelType.CurieText)
        .MapDeploymentTextModel("text-davinci-003", TextModelType.DavinciText3)
        .MapDeploymentEmbeddingModel("OpenAiDemoModel", EmbeddingModelType.AdaTextEmbedding)
        .MapDeploymentChatModel("gpt35turbo", ChatModelType.Gpt35Turbo0301);
}, "Azure");

I can retrieve the integration with IOpenAiFactory interface and the name of the integration.

private readonly IOpenAiFactory _openAiFactory;

public CompletionEndpointTests(IOpenAiFactory openAiFactory)
{
    _openAiFactory = openAiFactory;
}

public async ValueTask DoSomethingWithDefaultIntegrationAsync()
{
    var openAiApi = _openAiFactory.Create();
    openAiApi.Completion.........
}

public async ValueTask DoSomethingWithAzureIntegrationAsync()
{
    var openAiApi = _openAiFactory.Create("Azure");
    openAiApi.Completion.........
}

or get the more specific service

public async ValueTask DoSomethingWithAzureIntegrationAsync()
{
    var openAiEmbeddingApi = _openAiFactory.CreateEmbedding(name);
    openAiEmbeddingApi.Request(....);
}

Without Dependency Injection

📖 Back to summary
You may configure in a static constructor or during startup your integration without the dependency injection pattern.

  OpenAiService.Instance.AddOpenAi(settings =>
    {
        settings.ApiKey = apiKey;
    }, "NoDI");

and you can use it with the same static class OpenAiService and the static Create method

var openAiApi = OpenAiService.Factory.Create(name);
openAiApi.Embedding......

or get the more specific service

var openAiEmbeddingApi = OpenAiService.Factory.CreateEmbedding(name);
openAiEmbeddingApi.Request(....);

Models

📖 Back to summary
List and describe the various models available in the API. You can refer to the Models documentation to understand what models are available and the differences between them.
You may find more details here, and here samples from unit test.

List Models

Lists the currently available models, and provides basic information about each one such as the owner and availability.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Model.ListAsync();

Retrieve Models

Retrieves a model instance, providing basic information about the model such as the owner and permissioning.

var openAiApi = _openAiFactory.Create(name);
var result = await openAiApi.Model.RetrieveAsync(TextModelType.DavinciText3.ToModelId());

Delete fine-tune model

Delete a fine-tuned model. You must have the Owner role in your organization.

var openAiApi = _openAiFactory.Create(name);
var deleteResult = await openAiApi.Model
    .DeleteAsync(fineTuneModelId);

Completions

📖 Back to summary
Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.
You may find more details here, and here samples from unit test

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Completion
    .Request("One Two Three Four Five Six Seven Eight Nine One Two Three Four Five Six Seven Eight")
    .WithModel(TextModelType.CurieText)
    .WithTemperature(0.1)
    .SetMaxTokens(5)
    .ExecuteAsync();

Streaming

var openAiApi = _openAiFactory.Create(name);
var results = new List<CompletionResult>();
        await foreach (var x in openAiApi.Completion
           .Request("Today is Monday, tomorrow is", "10 11 12 13 14")
           .WithTemperature(0)
           .SetMaxTokens(3)
           .ExecuteAsStreamAsync())
        {
            results.Add(x);
        }

Chat

📖 Back to summary
Given a chat conversation, the model will return a chat completion response.
You may find more details here, and here samples from unit test.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Chat
        .Request(new ChatMessage { Role = ChatRole.User, Content = "Hello!! How are you?" })
        .WithModel(ChatModelType.Gpt4)
        .WithTemperature(1)
        .ExecuteAsync();

Chat Streaming

var openAiApi = _openAiFactory.Create(name);
var results = new List<ChatResult>();
    await foreach (var x in openAiApi.Chat
        .Request(new ChatMessage { Role = ChatRole.User, Content = "Hello!! How are you?" })
        .WithModel(ChatModelType.Gpt35Turbo)
        .WithTemperature(1)
        .ExecuteAsStreamAsync())
        {
            results.Add(x);
        }

Chat functions

You may find the update here

Simple function configuration

Here an example based on the link, you may find it in unit test.

var request = openAiApi.Chat
            .RequestWithUserMessage("What is the weather like in Boston?")
            .WithModel(ChatModelType.Gpt35Turbo)
            .WithFunction(new JsonFunction
            {
                Name = functionName,
                Description = "Get the current weather in a given location",
                Parameters = new JsonFunctionNonPrimitiveProperty()
                    .AddPrimitive("location", new JsonFunctionProperty
                    {
                        Type = "string",
                        Description = "The city and state, e.g. San Francisco, CA"
                    })
                    .AddEnum("unit", new JsonFunctionEnumProperty
                    {
                        Type = "string",
                        Enums = new List<string> { "celsius", "fahrenheit" }
                    })
                    .AddRequired("location")
            });
var response = await request
    .ExecuteAndCalculateCostAsync();
var function = response.Result.Choices[0].Message.Function;
var weatherRequest = JsonSerializer.Deserialize<WeatherRequest>(function.Arguments);
request
    .AddFunctionMessage(functionName, "{\"temperature\": \"22\", \"unit\": \"celsius\", \"description\": \"Sunny\"}");
response = await request
    .ExecuteAndCalculateCostAsync();
var content = response.Result.Choices[0].Message.Content;

In this case you receive as finish reason instead of "stop" the word "functionExecuted".

Assert.Equal("functionExecuted", response.Result.Choices[0].FinishReason);

Function with framework

You can create your function using the interface IOpenAiChatFunction

internal sealed class WeatherFunction : IOpenAiChatFunction
{
    private static readonly JsonSerializerOptions s_options = new()
    {
        DefaultIgnoreCondition = JsonIgnoreCondition.WhenWritingNull,
        WriteIndented = true,
    };
    static WeatherFunction()
    {
        var converter = new JsonStringEnumConverter();
        s_options.Converters.Add(converter);
    }
    public const string NameLabel = "get_current_weather";
    public string Name => NameLabel;
    private const string DescriptionLabel = "Get the current weather in a given location";
    public string Description => DescriptionLabel;
    public Type Input => typeof(WeatherRequest);
    public async Task<object> WrapAsync(string message)
    {
        var request = System.Text.Json.JsonSerializer.Deserialize<WeatherRequest>(message, s_options);
        if (request == null)
            await Task.Delay(0);
        return new WeatherResponseModel
        {
            Description = "Sunny",
            Temperature = 22,
            Unit = "Celsius"
        };
    }
}

⚠️ Pay attention when you use "enum", in .Net you have to use the JsonStringEnumConverter like in the example.

You have to setup it in dependency injection

services
    .AddOpenAiChatFunction<WeatherFunction>();

You have to create the Request model for your json:

internal sealed class WeatherRequest
{
    [JsonPropertyName("location")]
    [JsonRequired]
    [JsonPropertyDescription("The city and state, e.g. San Francisco, CA")]
    public string Location { get; set; }
    [JsonPropertyName("unit")]
    [JsonPropertyDescription("Unit Measure of temperature. e.g. Celsius or Fahrenheit")]
    public string Unit { get; set; }
}

You can use some JsonProperty attribute like:

• JsonPropertyName: name of the property
• JsonPropertyDescription: description of what the property is.
• JsonRequired: to set as Required for OpenAi
• JsonPropertyAllowedValues: to have only a range of possible values for the property.
• JsonPropertyRange: to have a range of values
• JsonPropertyMaximum: to have a maximum value for the property
• JsonPropertyMinimum: to have a minimum value for the property
• JsonPropertyMultipleOf: to have only a multiple of a value for the property

After the configuration you can use this function framework in this way:

var openAiApi = _openAiFactory.Create(name);
var response = await openAiApi.Chat
    .RequestWithUserMessage("What is the weather like in Boston?")
    .WithModel(ChatModelType.Gpt35Turbo_Snapshot)
    .WithFunction(WeatherFunction.NameLabel)
    .ExecuteAndCalculateCostAsync(true);

var content = response.Result.Choices[0].Message.Content;

With true in method ExecuteAsync or ExecuteAndCalculateCostAsync you ask the api to call automatically your function when a function is requested by OpenAi. You, also, can add all the functions you injected in one go.

services
    .AddOpenAiChatFunction<WeatherFunction>()
    .AddOpenAiChatFunction<AirplaneFunction>()
    .AddOpenAiChatFunction<GroceryFunction>()
    .AddOpenAiChatFunction<MapFunction>();

and use WithAllFunctions method

await foreach (var x in openAiApi.Chat
    .RequestWithUserMessage("What is the weather like in Boston?")
    .WithModel(ChatModelType.Gpt35Turbo_Snapshot)
    .WithAllFunctions()
    .ExecuteAsStreamAndCalculateCostAsync(true))

With true, automatically the framework understands the request from OpenAi and will use the right function to submit a new request.

Task<object> WrapAsync(string message);

from IOpenAiChatFunction

In this case you receive as finish reason instead of "stop" the word "functionAutoExecuted".

Assert.Equal("functionAutoExecuted", response.Result.Choices[0].FinishReason);

Null behavior from Function framework

If you return from your WrapAsync null, the framework doesn't make another call to open ai and return immediately as finish reason "null".

For example if I create a "unuseful" function that returns always null.

internal sealed class NullFunction : IOpenAiChatFunction
{
    public const string NameLabel = "get_current_cart";
    public string Name => NameLabel;
    private const string DescriptionLabel = "Get the current cart of your user";
    public string Description => DescriptionLabel;
    public Type Input => typeof(NullRequestModel);
    public Task<object> WrapAsync(string message)
    {
        _ = System.Text.Json.JsonSerializer.Deserialize<NullRequestModel>(message);
        return Task.FromResult(default(object));
    }
}

I can test the behavior, and I expect the finish reason as "null".

var openAiApi = _openAiFactory.Create(name);
Assert.NotNull(openAiApi.Chat);
var response = await openAiApi.Chat
    .RequestWithUserMessage("My username is Keyser D. Soze and I want to know what I have in my cart.")
    .WithModel(ChatModelType.Gpt35Turbo_Snapshot)
    .WithAllFunctions()
    .ExecuteAsync(true);

var function = response.Choices[0].Message.Function;
Assert.NotNull(function);
Assert.Contains("Keyser D. Soze", function.Arguments);
Assert.Equal("null", response.Choices[0].FinishReason);

Edits

📖 Back to summary
Given a prompt and an instruction, the model will return an edited version of the prompt. You may find more details here, and here samples from unit test.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Edit
        .Request("Fix the spelling mistakes")
        .WithModel(EditModelType.TextDavinciEdit)
        .SetInput("What day of the wek is it?")
        .WithTemperature(0.5)
        .ExecuteAsync();

Images

📖 Back to summary
Given a prompt and/or an input image, the model will generate a new image.
You may find more details here, and here samples from unit test.

Create Image

Creates an image given a prompt.

var openAiApi = _openAiFactory.Create(name);
var response = await openAiApi.Image
    .Generate("A cute baby sea otter")
    .WithSize(ImageSize.Small)
    .ExecuteAsync();

Download directly and save as stream

var openAiApi = _openAiFactory.Create(name);
var streams = new List<Stream>();
await foreach (var image in openAiApi.Image
    .Generate("A cute baby sea otter")
    .WithSize(ImageSize.Small)
    .DownloadAsync())
{
    streams.Add(image);
}

Create Image Edit

Creates an edited or extended image given an original image and a prompt.

var openAiApi = _openAiFactory.Create(name);
var response = await openAiApi.Image
    .Generate("A cute baby sea otter wearing a beret")
    .EditAndTrasformInPng(editableFile, "otter.png")
    .WithSize(ImageSize.Small)
    .WithNumberOfResults(2)
    .ExecuteAsync();

Create Image Variation

Creates a variation of a given image.

var openAiApi = _openAiFactory.Create(name);
var response = await openAiApi.Image
    .VariateAndTransformInPng(editableFile, "otter.png")
    .WithSize(ImageSize.Small)
    .WithNumberOfResults(1)
    .ExecuteAsync();

Embeddings

📖 Back to summary
Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
You may find more details here, and here samples from unit test.

Create Embedding

Creates an embedding vector representing the input text.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Embedding
    .Request("A test text for embedding")
    .ExecuteAsync();

Create Embedding with custom dimensions

Creates an embedding with custom dimensions vector representing the input text. Only supported in text-embedding-3 and later models.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Embedding
    .Request("A test text for embedding")
    .WithDimensions(1536)
    .ExecuteAsync();

Distance for embedding

For searching over many vectors quickly, we recommend using a vector database. You can find examples of working with vector databases and the OpenAI API in our Cookbook on GitHub. Vector database options include:

• Pinecone, a fully managed vector database
• Weaviate, an open-source vector search engine
• Redis as a vector database
• Qdrant, a vector search engine
• Milvus, a vector database built for scalable similarity search
• Chroma, an open-source embeddings store

Which distance function should I use?

We recommend cosine similarity. The choice of distance function typically doesn’t matter much.

OpenAI embeddings are normalized to length 1, which means that:

Cosine similarity can be computed slightly faster using just a dot product Cosine similarity and Euclidean distance will result in the identical rankings

You may use the utility service in this repository to calculate in C# the distance with Cosine similarity

Audio

📖 Back to summary
You may find more details here, and here samples from unit test.

Create Transcription

Transcribes audio into the input language.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Audio
    .Request(editableFile, "default.mp3")
    .TranscriptAsync();

Also available as a more verbose result exposing timestamps, confidence and statistics:

var openAiApi = _openAiFactory.Create(name);
var verboseResults = await openAiApi.Audio
    .Request(editableFile, "default.mp3")
    .VerboseTranscriptAsync();

Create Translation

Translates audio into English.

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Audio
    .Request(editableFile, "default.mp3")
    .TranslateAsync();

Also available as a more verbose result exposing timestamps, confidence and statistics:

var openAiApi = _openAiFactory.Create(name);
var verboseResults = await openAiApi.Audio
    .Request(editableFile, "default.mp3")
    .VerboseTranslateAsync();

File

📖 Back to summary
Files are used to upload documents that can be used with features like Fine-tuning.
You may find more details here, and here samples from unit test.

List files

Returns a list of files that belong to the user's organization.

  var openAiApi = _openAiFactory.Create(name);
  var results = await openAiApi.File
            .AllAsync();

Upload file

Upload a file that contains document(s) to be used across various endpoints/features. Currently, the size of all the files uploaded by one organization can be up to 1 GB. Please contact us if you need to increase the storage limit.

var openAiApi = _openAiFactory.Create(name);
var uploadResult = await openAiApi.File
        .UploadFileAsync(editableFile, name);

Delete file

Delete a file.

var openAiApi = _openAiFactory.Create(name);
var deleteResult = await openAiApi.File
        .DeleteAsync(uploadResult.Id);

Retrieve file

Returns information about a specific file.

var openAiApi = _openAiFactory.Create(name);
var retrieve = await openAiApi.File
        .RetrieveAsync(uploadResult.Id);

Retrieve file content

Returns the contents of the specified file

var openAiApi = _openAiFactory.Create(name);
var contentRetrieve = await openAiApi.File
        .RetrieveFileContentAsStringAsync(uploadResult.Id);

Fine-Tunes

📖 Back to summary
Manage fine-tuning jobs to tailor a model to your specific training data.
You may find more details here, and here samples from unit test.

Create fine-tune

Creates a job that fine-tunes a specified model from a given dataset. Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

var openAiApi = _openAiFactory.Create(name);
var createResult = await openAiApi.FineTune
                            .Create(fileId)
                            .ExecuteAsync();

List fine-tunes

List your organization's fine-tuning jobs

var openAiApi = _openAiFactory.Create(name);
var allFineTunes = await openAiApi.FineTune
                    .ListAsync();

Retrieve fine-tune

Gets info about the fine-tune job.

var openAiApi = _openAiFactory.Create(name);
var retrieveFineTune = await openAiApi.FineTune
                        .RetrieveAsync(fineTuneId);

Cancel fine-tune

Immediately cancel a fine-tune job.

var openAiApi = _openAiFactory.Create(name);
var cancelResult = await openAiApi.FineTune
                        .CancelAsync(fineTuneId);

List fine-tune events

Get fine-grained status updates for a fine-tune job.

var openAiApi = _openAiFactory.Create(name);
var events = await openAiApi.FineTune
                    .ListEventsAsync(fineTuneId);

List fine-tune events as stream

Get fine-grained status updates for a fine-tune job.

var openAiApi = _openAiFactory.Create(name);
var events = await openAiApi.FineTune
                    .ListEventsAsStreamAsync(fineTuneId);

Delete fine-tune model

Delete a fine-tuned model. You must have the Owner role in your organization.

var openAiApi = _openAiFactory.Create(name);
var deleteResult = await openAiApi.FineTune
    .DeleteAsync(fineTuneModelId);

Moderations

📖 Back to summary
Given a input text, outputs if the model classifies it as violating OpenAI's content policy.
You may find more details here, and here samples from unit test.

Create moderation

Classifies if text violates OpenAI's Content Policy

var openAiApi = _openAiFactory.Create(name);
var results = await openAiApi.Moderation
        .Create("I want to kill them.")
        .WithModel(ModerationModelType.TextModerationStable)
        .ExecuteAsync();

Utilities

📖 Back to summary
Utilities for OpenAi, you can inject the interface IOpenAiUtility everywhere you need it. In IOpenAiUtility you can find:

Cosine Similarity

📖 Back to embeddings
In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval [−1,1]. For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in [0,1]. Here an example from Unit test.

IOpenAiUtility _openAiUtility;
var resultOfCosineSimilarity = _openAiUtility.CosineSimilarity(results.Data.First().Embedding, results.Data.First().Embedding);
Assert.True(resultOfCosineSimilarity >= 1);

Without DI, you need to setup an OpenAiService without Dependency Injection and after that you can use

IOpenAiUtility openAiUtility = OpenAiService.Factory.Utility();

Tokens

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You can think of tokens as pieces of words, where 1,000 tokens is about 750 words. You can calculate your request tokens with the Tokenizer service in Utility.

IOpenAiUtility _openAiUtility
var encoded = _openAiUtility.Tokenizer
    .WithChatModel(ChatModelType.Gpt4)
    .Encode(value);
Assert.Equal(numberOfTokens, encoded.NumberOfTokens);
var decoded = _openAiUtility.Tokenizer.Decode(encoded.EncodedTokens);
Assert.Equal(value, decoded);

Cost

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You can think of tokens as pieces of words, where 1,000 tokens is about 750 words.

IOpenAiCost _openAiCost;
var integrationName = "Azure";
var manualCostCalculator = _openAiCost.Configure(x =>
{
    x
    .WithFamily(ModelFamilyType.Gpt3_5)
    .WithType(OpenAiType.Chat);
}, integrationName);
var manualCalculatedPrice = manualCostCalculator.Invoke(new OpenAiUsage
{
    PromptTokens = numberOfTokens * times,
});

You may get price for your request directly from any endpoint

 var chat = openAiApi.Chat
        .Request(new ChatMessage { Role = ChatRole.User, Content = content })
        .WithModel(chatModel)
        .WithTemperature(1);
 var costForRequest = chat.CalculateCost();

You can get the cost for current request

var chat = openAiApi.Chat
        .Request(new ChatMessage { Role = ChatRole.User, Content = content })
        .WithModel(chatModel)
        .WithTemperature(1);
 var responseForChatWithCost = await chat.ExecuteAndCalculateCostAsync();
 var costForRequestAndResponse = responseForChatWithCost.CalculateCost();

Setup price

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During setup of your OpenAi service you may add your custom price table with settings.Price property.

services.AddOpenAi(settings =>
{
    settings.ApiKey = azureApiKey;
    settings
        .UseVersionForChat("2023-03-15-preview");
    settings.Azure.ResourceName = resourceName;
    settings.Azure.AppRegistration.ClientId = clientId;
    settings.Azure.AppRegistration.ClientSecret = clientSecret;
    settings.Azure.AppRegistration.TenantId = tenantId;
    settings.Azure
        .MapDeploymentTextModel("text-curie-001", TextModelType.CurieText)
        .MapDeploymentTextModel("text-davinci-003", TextModelType.DavinciText3)
        .MapDeploymentEmbeddingModel("OpenAiDemoModel", EmbeddingModelType.AdaTextEmbedding)
        .MapDeploymentChatModel("gpt35turbo", ChatModelType.Gpt35Turbo0301);
    settings.Price
        .SetFineTuneForAda(0.2M, 0.2M)
        .SetAudioForTranslation(0.2M);
}, "Azure");

Management

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In your openai dashboard you may get the billing usage, or users, or taxes, or similar. Here you have an api to retrieve this kind of data.

Billing

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You may use the management endpoint to retrieve data for your usage. Here an example on how to get the usage for the month of april.

var management = _openAiFactory.CreateManagement(integrationName);
var usages = await management
    .Billing
    .From(new DateTime(2023, 4, 1))
    .To(new DateTime(2023, 4, 30))
    .GetUsageAsync();
Assert.NotEmpty(usages.DailyCosts);

Deployments

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Only for Azure you have to deploy a model to use model in yout application. You can configure Deployment during startup of your application.

services.AddOpenAi(settings =>
{
    settings.ApiKey = azureApiKey;
    settings
        .UseVersionForChat("2023-03-15-preview");
    settings.Azure.ResourceName = resourceName;
    settings.Azure.AppRegistration.ClientId = clientId;
    settings.Azure.AppRegistration.ClientSecret = clientSecret;
    settings.Azure.AppRegistration.TenantId = tenantId;
    settings.Azure
        .MapDeploymentTextModel("text-curie-001", TextModelType.CurieText)
        .MapDeploymentTextModel("text-davinci-003", TextModelType.DavinciText3)
        .MapDeploymentEmbeddingModel("OpenAiDemoModel", EmbeddingModelType.AdaTextEmbedding)
        .MapDeploymentChatModel("gpt35turbo", ChatModelType.Gpt35Turbo0301)
        .MapDeploymentCustomModel("ada001", "text-ada-001");
    settings.Price
        .SetFineTuneForAda(0.0004M, 0.0016M)
        .SetAudioForTranslation(0.006M);
}, "Azure");

During startup you can configure other deployments on your application or on Azure.

var app = builder.Build();
await app.Services.MapDeploymentsAutomaticallyAsync(true);

or a specific integration or list of integration that you setup previously.

await app.Services.MapDeploymentsAutomaticallyAsync(true, "Azure", "Azure2");

You can do this step with No dependency injection integration too.

MapDeploymentsAutomaticallyAsync is a extensions method for IServiceProvider, with true you can automatically install on Azure the deployments you setup on application. In the other parameter you can choose which integration runs this automatic update. In the example it's running for the default integration. With the Management endpoint you can programatically configure or manage deployments on Azure.

You can create a new deployment

var createResponse = await openAiApi.Management.Deployment
    .Create(deploymentId)
    .WithCapacity(2)
    .WithDeploymentTextModel("ada", TextModelType.AdaText)
    .WithScaling(Management.DeploymentScaleType.Standard)
    .ExecuteAsync();

Get a deployment by Id

var deploymentResult = await openAiApi.Management.Deployment.RetrieveAsync(createResponse.Id);

List of all deployments by status

var listResponse = await openAiApi.Management.Deployment.ListAsync();

Update a deployment

var updateResponse = await openAiApi.Management.Deployment
    .Update(createResponse.Id)
    .WithCapacity(1)
    .WithDeploymentTextModel("ada", TextModelType.AdaText)
    .WithScaling(Management.DeploymentScaleType.Standard)
    .ExecuteAsync();

Delete a deployment by Id

var deleteResponse = await openAiApi.Management.Deployment
    .DeleteAsync(createResponse.Id);