TypeChat.NET

TypeChat.NET brings the ideas of TypeChat to .NET.

TypeChat.NET provides cross platform libraries that help you build natural language interfaces with language models using strong types, type validation and simple type safe programs (plans). Strong typing may help make software that uses language models more deterministic and reliable.

// Translates user intent into strongly typed Calendar Actions
var model = new LanguageModel(Config.LoadOpenAI());
var translator = new JsonTranslator<CalendarActions>(model);

// Translate natural language request 
CalendarActions actions = await translator.TranslateAsync(requestText);

TypeChat.NET is in active development with frequent updates. The framework will evolve as the team explores the space and incorporates feedback. Supported scenarios are shown in the included Examples. Documentation will also continue to improve. When in doubt, please look at the code.

Assemblies

TypeChat.NET currently consists of the following assemblies:

• Microsoft.TypeChat: Classes that translate user intent into strongly typed and validated objects.

• Microsoft.TypeChat.Program: Classes to synthesize, validate and run JSON programs.

• Microsoft.TypeChat.SemanticKernel: Integration with Microsoft Semantic Kernel for models, plugins, embeddings and other features.

Microsoft.TypeChat

TypeChat uses language models to translate user intent into JSON that conforms to a schema. This JSON is then validated and deserialized into a typed object. Additional constraint checking is applied as needed. Validation errors are sent back to the language model, which uses them to repair the Json it originally returned.

TypeChat provides:

• Json translation, validation, repair and deserialization.
• Extensibility: interfaces for customizing schemas, validators and prompts.
• Schema export: classes to generate schema for the .NET Type you want to translate to. Exported schema includes dependencies and base classes. The exported schema is specified using Typescript, which can concisely express schema for JSON objects.
  • Support for common scenarios shown in TypeChat examples. When you encounter limitations (such as how generics are currently exported), you can supply schema text, such as Typescript authored by hand.
  • Helper attributes for Vocabularies and Comments. Vocabularies are string tables that constrain the values that can be assigned to string properties. Dynamic loading of vocabularies enables scenarios where they vary at runtime.
  • Note: Like TypeChat, TypeChat.NET has only been tested with schema specified in Typescript.

[Comment("Milks currently in stock")]
public class Milks
{
    [JsonVocab("whole milk | two percent milk | nonfat milk | soy milk | almond milk")]
    public string Name { get; set; }
}

Microsoft.TypeChat.Program

TypeChat.Program translates natural language requests into simple programs (Plans), represented as JSON.

JSON programs can be thought of as a DSL or Plan, expressed in JSON, with an associated grammar that is enforced. JSON programs can be type checked against the APIs they target. They can then be run using an interpreter, or compiled into .NET code.

TypeChat.Program includes:

• Program Translator: translates user intent into programs that follow the Program Grammar
• Program Interpreter: runs programs generated by ProgramTranslator using an interpreter.
• Program Compiler: uses the dynamic language runtime (DLR) to compile programs/plans with type checking. Compilation diagnostics are used to repair programs.

// Translates user intent into typed Programs that call methods on a Math API
var model = new LanguageModel(Config.LoadOpenAI());
var api = new MathAPI();
var translator = new ProgramTranslator<IMathAPI>(model, api);

// Translate natural language request
Program program = await translator.TranslateAsync(requestText);
// Run the program
program.Run(api);

Microsoft.TypeChat.SemanticKernel

TypeChat.SemanticKernel uses Semantic Kernel 1.0. It includes classes for:

• Json Programs for Plugins: turn registered plugins into APIs that Json programs can target. See the Plugins Example.
• Language model and embeddings access using Semantic Kernel.

Prior versions of TypeChat.NET used the pre-release programming model of Semantic Kernel. You can access this deprecated version using the sk_prerelease branch.

Getting Started

Prerequisite: OpenAI

• OpenAI Language Models: TypeChat.NET and its examples currently require familiarity with and access to language models from OpenAI.
• TypeChat.NET has been tested with and supports the following models:
  • gpt-35-turbo
  • gpt-4
  • ada-002
• Some examples and scenarios will work best with gpt-4

Building

• Visual Studio 2022.
  • Load typechat.sln from the root directory.
  • Restore packages
  • Build
• dotnet build
  • Launch a command prompt / terminal
  • Go to the root directory of the project
  • dotnet build Typechat.sln

Nuget Packages

• Microsoft.Typechat

dotnet add package Microsoft.TypeChat

• Microsoft.TypeChat.Program

dotnet add package Microsoft.TypeChat.Program

• Microsoft.Typechat.SemanticKernel

dotnet add package Microsoft.TypeChat.SemanticKernel

Examples

To see TypeChat.NET in action, explore the Example projects and TypeChat.Examples Library.

Each example includes an input.txt with sample input. Pass the input file as an argument to run the example in batch mode.

The sections below describe which examples will best introduce which concept. Some examples or scenarios may work best with gpt-4.

Hello World

The Sentiment example is TypeChat's Hello World and a minimal introduction to JsonTranslator.

JsonTranslator

The following examples demonstrate how to use JsonTranslator, Schemas and Vocabularies:

• CoffeeShop: Natural language ordering at a coffee shop. Demonstrates a complex schema with polymorphic deserialzation.
• Calendar: Transform language into calendar actions
• Restaurant: Order processing at a pizza restaurant

Hierarchical schemas

• MultiSchema: dynamically route user intent to other 'sub-apps'
• SchemaHierarchy: A Json Translator than uses multiple child JsonTranslators. For each user request, it picks the semantically nearest child translator and routes the input to it.
• TextClassifier and VectorTextIndex show how to build a simple classifiers to route input.

Json Programs

• Math: How to turn user requests into simple calculator programs
• Plugins: How to translate user intent into programs programs that call Semantic Kernel Plugins

Interactive agents

• HealthData: how to use an interactive bot to collect a user's health information.
• Agent Classes for working with interactive agents that have history. These classes emonstrate how TypeChat.NET may be used for strongly typed interactions with message passing agents or bots. These agents can include features such as built in interaction history.

Api Key and Configuration

To use TypeChat.net or run the examples, you need an API key for an OpenAI service. Azure OpenAI and the OpenAI service are both supported.

Configure Api Key for examples

• Go to the examples folder in the solution
• Make a copy of the appSettings.json file and name it appSettings.Development.json. Ensure it is in the same folder as appSettings.json
• appSettings.Development.json is a local development only override of the settings in appSettings.json and is never checked in.
• Add your Api Key to appSettings.Development.json.

A typical appSettings.Development.json will look like this:

// For Azure OpenAI service
{
  "OpenAI": {
    "Azure": true,
    "ApiKey": "YOUR API KEY",
    "Endpoint": "https://YOUR_RESOURCE_NAME.openai.azure.com",
    "Model": "gpt-35-turbo"  // Name of Azure deployment
  }
}

// For OpenAI Service:
{
  "OpenAI": {
    "Azure": false,
    "ApiKey": "YOUR API KEY",
    "Endpoint": "https://api.openai.com/v1/chat/completions",
    "Model": "gpt-3.5-turbo"  // Name of OpenAI model
  }
}

OpenAIConfig

TypeChat examples accesses language models using the LanguageModel class. The OpenAIConfig class supplies configuration for LanguageModel. You initialize OpenAIConfig from your application's configuration, from a Json file or from environment variables.

See OpenAIConfig.cs for a list of :

• Configurable properties
• Supported environment variables.

// Your configuration 
OpenAIConfig config = Config.LoadOpenAI();
// Or Json file
OpenAIConfig config = OpenAIConfig.LoadFromJsonFile(...);
// Or from config
config = OpenAIConfig.FromEnvironment();

var model = new LanguageModel(config);

Using Semantic Kernel

You can also access a LanguageModel using the SemanticKernel Kernel object you created using a KernelBuilder.

const string modelName = "gpt-35-turbo";
new ChatLanguageModel(_kernel.GetService<IChatCompletionService>(modelName), modelName);

Using your own client

TypeChat accesses language models using the ILanguageModel interface. LanguageModel implements ILanguageModel.

You can use your own model client by implementing ILanguageModel. If you don't use one of the Open AI models listed above, you will likely also need to supply JsonTranslatorPrompts that work best with your model.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

License

Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT license.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.