ServiceStack.AI

ServiceStack.AI

Speech-to-Text and ChatGPT providers to enable natural language features in .NET

ServiceStack.AI Providers​

As the AI landscape is actively changing we want our Apps to be able to easily switch to different Speech-to-Text or ChatGPT and TypeChat providers so we're able to easily evaluate and use the best provider for our use-case.

To support this we're maintaining FREE implementation-agnostic abstractions for different AI and GPT Providers to enable AI features in .NET Apps under the new ServiceStack.AI namespace in the dep-free ServiceStack.Interfaces package.

The implementations for these abstractions are maintained across NuGet packages in accordance with their dependencies:

  • ServiceStack.Aws - AI & GPT Providers for Amazon Web Services
  • ServiceStack.Azure - AI & GPT Providers for Microsoft Azure
  • ServiceStack.GoogleCloud - AI & GPT Providers for Google Cloud
  • ServiceStack.AI - AI & GPT Providers for OpenAI APIs and local Whisper and Node TypeChat installs

These abstractions and their implementations enable .NET projects to add AI-powered natural language features to their Apps whilst decoupling their Speech-to-text and ChatGPT requirements from any single implementation where they can be easily substituted as needed.

.NET TypeChat Examples​

An easy way to evaluate the new ServiceStack.AI providers is by exploring the TypeChat .NET Examples Project which contains .NET implementations for each TypeChat Example that are already configured to work with all available providers which has a Live Demo hosted at:

https://typechat.netcore.io

ISpeechToText​

The ISpeechToText interface abstracts Speech-to-Text providers behind a simple API:

public interface ISpeechToText
{
    // Once only task to run out-of-band before using the SpeechToText provider
    Task InitAsync(List<string> phrases, CancellationToken token = default);
    
    // Transcribe the Audio at recordingPath and return a JSON API Result
    Task<TranscriptResult> TranscribeAsync(string request, CancellationToken token);
}

Which has 5 different Speech-to-Text implementations to choose from:

Virtual File System Providers​

Likewise file storage is also easily substitutable with Virtual File System providers allowing Audio Voice Recordings to be uploaded to your preferred provider:

  • FileSystemVirtualFiles - stores uploads in local file system (default)
  • GoogleCloudVirtualFiles - stores uploads in Google Cloud Storage
  • S3VirtualFiles - stores uploads in AWS S3
  • AzureBlobVirtualFiles - stores uploads in Azure Blob Storage
  • R2VirtualFiles - stores uploads in Cloudflare R2

OpenAI Solution​

Since you'll likely be needing OpenAI's ChatGPT API for converting natural language requests into a machine readable request your App can process, the easiest solution is to also use OpenAI's Whisper API for your App's Speech-to-Text requirements, which you can configure your App to use in a Modular Startup config:

[assembly: HostingStartup(typeof(ConfigureOpenAi))]

public class ConfigureOpenAi : IHostingStartup
{
    const bool UseKernel = true;
    
    public void Configure(IWebHostBuilder builder) => builder
        .ConfigureServices((context, services) => {
            services.AddSingleton<ISpeechToText, WhisperApiSpeechToText>();
            
            // Call Open AI Chat API through node TypeChat
            services.AddSingleton<ITypeChat, NodeTypeChat>();
        })
        .ConfigureAppHost(afterConfigure:appHost => {
            
            if (appHost.TryResolve<ISpeechToText>() is IRequireVirtualFiles requireVirtualFiles)
                requireVirtualFiles.VirtualFiles = appHost.VirtualFiles;
        });
}

Open AI providers are maintained in the ServiceStack.AI NuGet Package:

<PackageReference Include="ServiceStack.AI" Version="8.*" />

Using Node TypeChat​

If you prefer to use Microsoft's node TypeChat library to utilize its auto schema validation and corrective auto-retry features, your Dockerfile will will need to have node installed:

# install node.js and ffmpeg
RUN apt-get clean && apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends curl gnupg ffmpeg \
    && curl -sL https://deb.nodesource.com/setup_current.x | bash - \
    && apt-get install nodejs -yq 

RUN npm install

package.json​

That installs TypeChat by listing it as a dependency in your App's package.json:

{
  "dependencies": {
    "typechat": "^0.0.10"
  }  
}

You'll also need a copy of typechat.mjs script that the .NET process invokes to utilize TypeChat, located in the Content Directory root of your Application which you can add to your project with x mix:

x mix typechat.mjs

Using Microsoft Semantic Kernel​

Alternatively you can avoid using TypeChat and node altogether by invoking ChatGPT through Microsoft's Semantic Kernel .NET Library:

var kernel = Kernel.Builder.WithOpenAIChatCompletionService(
        Environment.GetEnvironmentVariable("OPENAI_MODEL") ?? "gpt-3.5-turbo", 
        Environment.GetEnvironmentVariable("OPENAI_API_KEY")!)
    .Build();
services.AddSingleton(kernel);
services.AddSingleton<ITypeChat>(c => new KernelTypeChat(c.Resolve<IKernel>()));

If you're not using TypeChat you'll likely want to implement your own Custom Validation and Auto-correcting solution which can be more effective that TypeChat's schema validation errors approach.

Supporting Safari Web Audio​

If you're not using OpenAI's Whisper for transcribing you'll likely need to use ffmpeg to convert Convert Uploaded Files into a format your Speech-to-Text provider accepts.

Google Cloud Solution​

To use any of the Google Cloud providers your pc needs to be configured with GoogleCloud Credentials on a project with Speech-to-Text enabled.

You can then configure your App's appsettings.json with your Google Cloud Project and Storage where you would like uploads to persist to:

{
  "GoogleCloudConfig": {
    "Project": "servicestackdemo",
    "Location": "global",
    "Bucket": "servicestack-typechat"
  }
}

Which you'll be able to configure your App to use within a Modular Startup config:

[assembly: HostingStartup(typeof(ConfigureGoogleCloud))]

public class ConfigureGoogleCloud : IHostingStartup
{
    public void Configure(IWebHostBuilder builder) => builder
        .ConfigureServices((context, services) => {
            GoogleCloudConfig.AssertValidCredentials();

            var gcp = context.Configuration.GetSection(nameof(GoogleCloudConfig))
                .Get<GoogleCloudConfig>();
            services.AddSingleton(gcp);
            
            services.AddSingleton<ISpeechToText>(c => {
                return new GoogleCloudSpeechToText(SpeechClient.Create(),
                    gcp.ToSpeechToTextConfig(x => {
                        // x.PhraseSetId = PhraseSetId;
                    })
                ) {
                    VirtualFiles = HostContext.VirtualFiles
                };
            });
        })
        .ConfigureAppHost(afterConfigure:appHost => {
            appHost.VirtualFiles = new GoogleCloudVirtualFiles(
                StorageClient.Create(), appHost.Resolve<GoogleCloudConfig>().Bucket!);
        });
}

Google Cloud providers are maintained in the ServiceStack.GoogleCloud NuGet Package:

<PackageReference Include="ServiceStack.GoogleCloud" Version="8.*" />

Multiple custom Speech-to-Text configurations​

Google Cloud Speech-to-Text APIs supports the ability to improve transcription results by creating Custom PhraseSets and Recognizers where you can specify which phrases are likely to be used so you can boost their probability they'll be recognized correctly, they'll also let you configure which optimized model and languages to use.

If your App uses multiple Phrasesets or Recognizers they'll need return a different configured GoogleCloudSpeechToText provider dependent on the feature that's requested, which you can configure with a SpeechToTextFactory:

services.AddSingleton<ISpeechToTextFactory>(c => new SpeechToTextFactory
{
    Resolve = feature =>
    {
        var config = c.Resolve<AppConfig>();
        var gcp = c.Resolve<GoogleCloudConfig>();
        var siteConfig = config.GetSiteConfig(feature);

        return new GoogleCloudSpeechToText(
            SpeechClient.Create(),
            gcp.ToSpeechToTextConfig(x => {
                x.RecognizerId = siteConfig.RecognizerId;
                x.PhraseSetId = siteConfig.PhraseSetId;
            }))
        {
            VirtualFiles = HostContext.VirtualFiles
        };
    }
});

Creating Custom Recognizers​

The GoogleCloudSpeechToText provider also supports recreating a custom recognizer with a vocabulary of boosted phrases using the InitAsync API:

ISpeechToTextFactory SpeechToTextFactory { get; set; }

//...
List<KeyValuePair<string, int>> phraseWeights = ...;
var speechProvider = SpeechToTextFactory.Get(feature);
await speechProvider.InitAsync(new() {
    PhraseWeights = phraseWeights
});

This will re-create the PhraseSet and Recognizer using the PhraseSetId and RecognizerId identifiers that provider was configured with in its GoogleCloudConfig.

GoogleCloud Credential Deployments​

Google Cloud configures their Security credentials differently to other providers where instead of storing Bearer Tokens or connection strings in Environment Variables, their GOOGLE_APPLICATION_CREDENTIALS Environment Variable is used to instead specify the path where the actual JSON credentials are stored on disk.

Unfortunately passing the JSON configuration file as-is is incompatible with Docker Secrets for when you want Production App's credentials maintained outside of the Source Code repository.

A simple workaround you can do in your GitHub Actions release.yml instead is to Base64 encode the credentials which can then be passed as a Docker secret:

# Build and push new docker image, skip for manual redeploy other than 'latest'
- name: Build and push Docker images
  uses: docker/build-push-action@v3
  with:
    file: Dockerfile
    context: .
    push: true
    tags: ghcr.io/${‎{ env.image_repository_name }‎}:${‎{ env.TAG_NAME }‎}
    secrets: |
      googlecloud_credentials_base64=${‎{secrets.GOOGLE_APPLICATION_CREDENTIALS}‎}

Which your Dockerfile can then decode with Unix base64 tool before saving the credentials.json inside your Docker Container:

RUN --mount=type=secret,id=googlecloud_credentials_base64 \
    cat /run/secrets/googlecloud_credentials_base64 | base64 -d > /out/googlecloud-credentials.json

IDE Tooling​

A nice feature from using Cloud Services is the built-in tooling in IDEs like JetBrains Big Data Tools where you can inspect new Recordings and ChatGPT JSON responses from within your IDE, instead of SSH'ing into remote servers to inspect local volumes:

Azure Solution​

You can configure your App to use Azure AI Speech API to transcribe Web Audio Recordings that are persisted in Azure Blob Storage with the configuration below:

[assembly: HostingStartup(typeof(ConfigureAzure))]

public class ConfigureAzure : IHostingStartup
{
    public void Configure(IWebHostBuilder builder) => builder
        .ConfigureServices((context, services) => {

            var config = context.Configuration.GetSection(nameof(AzureConfig))
                .Get<AzureConfig>();
            services.AddSingleton(config);
            
            services.AddSingleton<ISpeechToText>(c => 
                new AzureSpeechToText(config.ToSpeechConfig()) {
                    VirtualFiles = HostContext.VirtualFiles
                });
        })
        .ConfigureAppHost(afterConfigure:appHost => {

            var config = appHost.Resolve<AzureConfig>();
            appHost.VirtualFiles = new AzureBlobVirtualFiles(
                config.ConnectionString, config.ContainerName);
        });
}

Azure providers are maintained in the ServiceStack.Azure NuGet Package:

<PackageReference Include="ServiceStack.Azure" Version="8.*" />

Enable Web Audio Support​

As Azure AI Speech only supports limited Audio formats, it's recommended to have GStreamer installed along side your App to enable support for more popular compressed Web Audio formats.

Refer to the GStreamer configuration docs for how install GStreamer in different Operating Systems.

AWS Solution​

Organizations hosting on AWS can configure their App to use Amazon Transcribe for transcribing their Audio recordings to text that they can store in AWS S3 with the configuration below:

[assembly: HostingStartup(typeof(ConfigureAws))]

namespace GptProviders;

public class ConfigureAws : IHostingStartup
{
    public void Configure(IWebHostBuilder builder) => builder
        .ConfigureServices((context, services) => {

            var config = context.Configuration.GetSection(nameof(AwsConfig)).Get<AwsConfig>();
            services.AddSingleton(config);
            
            services.AddSingleton<ISpeechToText>(c => new AwsSpeechToText(
                new AmazonTranscribeServiceClient(
                    config.AccessKey, config.SecretKey, config.ToRegionEndpoint()),
                new AwsSpeechToTextConfig {
                    Bucket = config.Bucket,
                    VocabularyName = config.VocabularyName,
                }) {
                VirtualFiles = HostContext.VirtualFiles
            });
        })
        .ConfigureAppHost(afterConfigure:appHost => {

            var config = appHost.Resolve<AwsConfig>();
            appHost.VirtualFiles = new S3VirtualFiles(
                new AmazonS3Client(config.AccessKey, config.SecretKey, config.ToRegionEndpoint()), 
                config.Bucket);
        });
}

Amazon Web Services providers are maintained in the ServiceStack.Aws NuGet Package:

<PackageReference Include="ServiceStack.Aws" Version="8.*" />

AWS Speech-to-Text Factory​

If your App uses custom vocabularies for different features you'll instead want register factory instead so you can return the correct configured AwsSpeechToText for the request feature:

services.AddSingleton<ISpeechToTextFactory>(c => new SpeechToTextFactory
{
    Resolve = feature =>
    {
        var config = c.Resolve<AppConfig>();
        var aws = c.Resolve<AwsConfig>();
        var site = config.GetSiteConfig(feature);
        
        return new AwsSpeechToText(new AmazonTranscribeServiceClient(
                aws.AccessKey, aws.SecretKey, aws.ToRegionEndpoint()),
            aws.ToSpeechToTextConfig(x => x.VocabularyName = site.VocabularyName))
        {
            VirtualFiles = HostContext.VirtualFiles
        };
    }
});

Creating Custom Vocabulary​

The AwsSpeechToText provider also supports recreating a custom recognizer with a vocabulary of boosted phrases using the InitAsync API:

ISpeechToTextFactory SpeechToTextFactory { get; set; }

//...
List<KeyValuePair<string, int>> phraseWeights = ...;
var speechProvider = SpeechToTextFactory.Get(feature);
await speechProvider.InitAsync(new() {
    PhraseWeights = phraseWeights
});

This will re-create the Vocabulary with the VocabularyName the AwsSpeechToText was configured with:

Cloudflare Solution​

Cloudflare AI newly released AI Gateway offers a managed gateway over OpenAI's APIs to cache responses, limit and retry requests, and provide analytics to help you monitor and track usage which you can utilize by changing the OpenAiBaseUrl used.

You may also soon be able to use Cloudflare's OpenAI's Whisper model independently that's currently on available through their Workers AI solution.

In the meantime their Cloudflare R2 product is one of the best value managed storage solutions available, it's an attractive option to use to store Web Audio recordings for usage with other Speech-to-Text providers, which your App can be configured to use with:

[assembly: HostingStartup(typeof(ConfigureCloudflare))]

public class ConfigureCloudflare : IHostingStartup
{
    public void Configure(IWebHostBuilder builder) => builder
        .ConfigureServices((context, services) =>
        {
            var config = context.Configuration.GetSection(nameof(CloudflareConfig))
                .Get<CloudflareConfig>();
            services.AddSingleton(config);
            
            services.AddSingleton<ISpeechToText>(c => new WhisperApiSpeechToText {
                BaseUri = config.OpenAiBaseUrl!,
            });
        })
        .ConfigureAppHost(afterConfigure:appHost => {

            var config = appHost.Resolve<CloudflareConfig>();
            appHost.VirtualFiles = new R2VirtualFiles(
                new AmazonS3Client(config.AccessKey, config.SecretKey,
                new AmazonS3Config {
                    ServiceURL = config.ToServiceUrl(),
                }), config.Bucket);
        });
}

Cloudflare's R2VirtualFiles provider is maintained in the ServiceStack.Aws NuGet Package:

<PackageReference Include="ServiceStack.Aws" Version="8.*" />

Local OpenAI Whisper​

If your App's requirements and hardware supports it, you can save an infrastructure dependency by using a local install of OpenAI's Whisper, which after installing ffmpeg and Python, can be installed with:

pip install -U openai-whisper

This will enable you to transcribe Audio recordings by simply specifying the recording you want a transcription of:

whisper recording.webm

Usage Notes​

  • The --language flag helps speed up transcriptions by avoiding needing to run auto language detection
  • By default whisper will generate its transcription results in all supported .txt, .json, .tsv, .srt and .vtt formats
    • you can limit to just the format you want it in with --output_format, e.g. use txt if you're just interested in the transcribed text
  • The default install also had FP16 and Numba Deprecation warnings

We can resolve these issues by using the modified prompt:

export PYTHONWARNINGS="ignore"
whisper --language=en --fp16 False --output_format txt recording.webm

Which should now generate a clean output containing the recordings transcribed text, that's also written to recording.txt:

[00:00.000 --> 00:02.000]  A latte, please.

This 2s recording took 9 seconds to transcribe on an M2 Macbook Air, a fair bit longer than the 1-2 seconds it takes to upload and transcribe recordings to Google Cloud, but still within acceptable response times for real-time transcriptions.

Switch to local OpenAI Whisper​

You can configure your App to use a local whisper install by registering WhisperLocalSpeechToText:

services.AddSingleton<ISpeechToText>(c => new WhisperLocalSpeechToText {
   WhisperPath = ProcessUtils.FindExePath("whisper"),
   TimeoutMs = 120 * 1000,
});

Example Usage​

TypeChat Examples CreateRecording API implementation in GptServices.cs is used to transcribe the uploaded Web Audio recording for all its examples which it first creates an entry in the Recording RDBMS Table before invoking the Speech-to-Text API that is then updated after its successful or error response:

public class MyServices(IAutoQueryDb autoQuery, ISpeechToTextFactory speechToTextFactory)
{
    public async Task<Recording> Any(CreateRecording request)
    {
        var feature = request.Feature.ToLower();
        var recording = (Recording)await autoQuery.CreateAsync(request, Request);
        var speechToText = speechToTextFactory.Get(request.Feature);

        var transcribeStart = DateTime.UtcNow;
        await Db.UpdateOnlyAsync(() => new Recording { TranscribeStart=transcribeStart },
            where: x => x.Id == recording.Id);

        ResponseStatus? responseStatus = null;
        try
        {
            var response = await speechToText.TranscribeAsync(request.Path);
            var transcribeEnd = DateTime.UtcNow;
            await Db.UpdateOnlyAsync(() => new Recording
            {
                Feature = feature,
                Provider = speechToText.GetType().Name,
                Transcript = response.Transcript,
                TranscriptConfidence = response.Confidence,
                TranscriptResponse = response.ApiResponse,
                TranscribeEnd = transcribeEnd,
                TranscribeDurationMs = (transcribeEnd-transcribeStart).TotalMilliseconds,
                Error = response.ResponseStatus.ToJson(),
            }, where: x => x.Id == recording.Id);
            responseStatus = response.ResponseStatus;
        }
        catch (Exception e)
        {
            await Db.UpdateOnlyAsync(() => new Recording { Error = e.ToString() },
                where: x => x.Id == recording.Id);
            responseStatus = e.ToResponseStatus();
        }

        recording = await Db.SingleByIdAsync<Recording>(recording.Id);

        if (responseStatus != null)
            throw new HttpError(responseStatus, HttpStatusCode.BadRequest);

        return recording;
    }
}

Client Usage Example​

Now that our Server supports it we can start using this API to upload Audio Recordings by running the pre-configured npm script to update our App's Typed JavaScript DTOs:

npm run dtos

To simplify capturing Web Audio recordings we've the encapsulated reusable functionality within the AudioRecorder.mjs class whose start() method starts recording Audio from the Users microphone:

import { AudioRecorder } from "/mjs/AudioRecorder.mjs"

let audioRecorder = new AudioRecorder()
await audioRecorder.start()

Where it captures audio chunks until stop() is called that are then stitched together into a Blob and converted into a Blob DataURL that's returned within a populated Audio Media element:

const audio = await audioRecorder.stop()

That supports the HTMLMediaElement API allowing pause and playback of recordings:

audio.play()
audio.pause()

The AudioRecorder also maintains the Blob of its latest recording in its audioBlob field and the MimeType that it was captured with in audioExt field, which we can use to upload it to the CreateRecording API, which if successful will return a transcription of the Audio recording:

import { JsonServiceClient } from "@servicestack/client"

const client = new JsonServiceClient()

const formData = new FormData()
formData.append('path', audioRecorder.audioBlob, `file.${audioRecorder.audioExt}`)
const api = await client.apiForm(new CreateRecording({feature:'coffeeshop'}),formData)
if (api.succeeded) {
   transcript.value = api.response.transcript
}

This is used by all TypeChat Examples that's encapsulated in their UI Razor Pages:

Prompt Providers​

Whilst not required for usage with other providers, the IPromptProvider interface can improve code reuse by implementing a standard interface for generating their TypeScript Schema and Chat GPT Prompt texts.

// The App Provider to use to generate TypeChat Schema and Prompts
public interface IPromptProvider
{
    // Create a TypeChat TypeScript Schema from a TypeChatRequest
    Task<string> CreateSchemaAsync(CancellationToken token);

    // Create a TypeChat TypeScript Prompt from a User request
    Task<string> CreatePromptAsync(string userMessage, CancellationToken token);
}

Implementations contain the App-specific functionality for creating its TypeScript Schema and GPT Prompt, which each App in TypeChatExamples maintains in its *PromptProvider.cs classes in the TypeChatExamples.ServiceInterface project.

TypeChat API​

After receiving a text transcript of a Customer's natural language request you'll need to enlist the services of Chat GPT to convert it into an Order request that your App can understand.

ITypeChat​

Just as we've abstracted the substitutable Speech-to-text Services App binds to, we've also created an abstraction for the TypeChat provider App uses, which allows easily swap out and evaluate different solutions or Mocking in tests:

public interface ITypeChat
{
    Task<TypeChatResponse> TranslateMessageAsync(TypeChatRequest request, 
        CancellationToken token = default);
}

Whilst a simple API on the surface, different execution and customizations options are available in the TypeChatRequest which at a minimum requires the Schema & Prompt to use and the UserMessage to convert:

// Request to process a TypeChat Request
public class TypeChatRequest
{
    public TypeChatRequest(string schema, string prompt, string userMessage)
    {
        Schema = schema;
        Prompt = prompt;
        UserMessage = userMessage;
    }

    /// TypeScript Schema
    public string Schema { get; set; }
    
    /// TypeChat Prompt
    public string Prompt { get; set; }
    
    /// Chat Request
    public string UserMessage { get; }
    
    /// Path to node exe (default node in $PATH)
    public string? NodePath { get; set; }

    /// Timeout to wait for node script to complete (default 120s)
    public int NodeProcessTimeoutMs { get; set; } = 120 * 1000;

    /// Path to node TypeChat script (default typechat.mjs)
    public string? ScriptPath { get; set; }
    
    /// TypeChat Behavior we want to use (Json | Program)
    public TypeChatTranslator TypeChatTranslator { get; set; }

    /// Path to write TypeScript Schema to (default Temp File)
    public string? SchemaPath { get; set; }
    
    /// Which directory to execute the ScriptPath (default CurrentDirectory) 
    public string? WorkingDirectory { get; set; }
}

There are currently 2 different Chat GPT ITypeChat implementations registered in TypeChat Examples Configure.Gpt.cs.

Semantic Kernel TypeChat Provider​

The natural approach for interfacing with OpenAI's ChatGPT API in .NET is to use Microsoft's Semantic Kernel to call it directly, which can be registered with:

var kernel = Kernel.Builder.WithOpenAIChatCompletionService(
        Environment.GetEnvironmentVariable("OPENAI_MODEL") ?? "gpt-3.5-turbo", 
        Environment.GetEnvironmentVariable("OPENAI_API_KEY")!)
    .Build();
services.AddSingleton(kernel);
services.AddSingleton<ITypeChat>(c => new KernelTypeChat(c.Resolve<IKernel>()));

Node TypeChat Provider​

As the TypeChat library uses typescript it requires calling out to the node executable in order to be able to use it from .NET Apps, which can be configured withL

services.AddSingleton<ITypeChat>(c => new NodeTypeChat());

It works by executing an external process that invokes a typechat.mjs script wrapper around TypeChat's functionality to invoke it and return any error responses in a structured ResponseStatus format that our .NET App can understand, which can also be invoked manually from the command line with:

node typechat.mjs json gpt\coffeeshop\schema.ts "i wanna latte macchiato with vanilla"

TypeChat uses the OpenAI Environment Variables below to access ChatGPT APIs:

  • OPENAI_MODEL - The OpenAI model name (e.g. gpt-3.5-turbo or gpt-4)
  • OPENAI_API_KEY - Your OpenAI API key

Using Chat GPT to process Natural Language Orders​

We now have everything we need to start leveraging Chat GPT to convert our Customers Natural Language requests into Machine readable instructions that our App can understand, guided by the App's TypeChat TypeScript Schema.

TypeChat Examples does this for all its apps in GptServices.cs that just like CreateRecording is a custom AutoQuery CRUD Service that uses AutoQuery to create the initial Chat record, that's later updated with the GPT Chat API Response:

public class GptServices(
    IAutoQueryDb autoQuery,
    IPromptProviderFactory promptFactory,
    ITypeChat typeChat,
    IPromptProvider promptProvider) : Service
{
    //...
    public async Task<object> Any(CreateChat request)
    {
        var feature = request.Feature.ToLower();
        var promptProvider = promptFactory.Get(feature);
        var chat = (Chat)await autoQuery.CreateAsync(request, Request);

        var chatStart = DateTime.UtcNow;
        await Db.UpdateOnlyAsync(() => new Chat { ChatStart = chatStart },
            where: x => x.Id == chat.Id);

        ResponseStatus? responseStatus = null;
        try
        {
            var schema = await promptProvider.CreateSchemaAsync();
            var prompt = await promptProvider.CreatePromptAsync(request.UserMessage);
            var typeChatRequest = CreateTypeChatRequest(feature, schema, prompt, request.UserMessage);
            
            var response = await typeChat.TranslateMessageAsync(typeChatRequest);
            var chatEnd = DateTime.UtcNow;
            await Db.UpdateOnlyAsync(() => new Chat
            {
                Request = request.UserMessage,
                Feature = feature,
                Provider = TypeChat.GetType().Name,
                Schema = schema,
                Prompt = prompt,
                ChatResponse = response.Result,
                ChatEnd = chatEnd,
                ChatDurationMs = (int)(chatEnd - chatStart).TotalMilliseconds,
                Error = response.ResponseStatus.ToJson(),
            }, where: x => x.Id == chat.Id);
            responseStatus = response.ResponseStatus;
        }
        catch (Exception e)
        {
            await Db.UpdateOnlyAsync(() => new Chat { Error = e.ToString() },
                where: x => x.Id == chat.Id);
            responseStatus = e.ToResponseStatus();
        }

        chat = await Db.SingleByIdAsync<Chat>(chat.Id);

        if (responseStatus != null)
            throw new HttpError(responseStatus, HttpStatusCode.BadRequest);
        
        return chat;
    }
}

Client Usage​

Just like the CreateRecording client usage example we can invoke the APIs using the typed JavaScript DTOs to invoke the CreateChat API to returns Chat GPTs JSON Response directly to the client:

apiChat.value = await client.api(new CreateChat({
    feature: 'coffeeshop',
    userMessage: request.toLowerCase()
}))

if (apiChat.value.response) {
    processChatItems(JSON.parse(apiChat.value.response.chatResponse).items)
} else if (apiChat.value.error) {
    apiProcessed.value = apiChat.value
}

Which for the CoffeeShop example is in the structure of the TypeScript Schema's array of Cart LineItem's which are matched against the products and available customizations from the App's database before being added to the user's cart in the processChatItems(items) function.

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