AI

One Fluent/Functional API to access Large Language Models in BoxLang

Welcome

Welcome to the BoxLang AI Module. This module provides AI generation capabilities to your BoxLang applications in an easy to use and abstracted API, so you can interact with ANY AI provider in a consistent manner.

We also have an bx-aiplus module that enhances this module with more AI providers, capabilities and features. The bx-aiplus module is part of our BoxLang +/++ subscriptions.

License

BoxLang is open source and licensed under the Apache 2 license.

Getting Started

You can easily get started with BoxLang AI by using the module installer:

install-bx-module bx-ai

If you would like to leverage it in your CommandBox Based Web applications, make sure you add it to your server.json or use box install bx-ai.

Once installed you can leverage the global functions (BIFs) in your BoxLang code. Here is a simple example:

// chat.bxs
answer = aiChat( "How amazing is BoxLang?" )
println( answer )

Providers

The following are the AI providers supported by this module. Please note that in order to interact with these providers you will need to have an account with them and an API key.

• OpenAI
• DeepSeek
• Gemini
• Grok
• Perplexity

More providers are available in our bx-aiplus module.

Features

Here are some of the features of this module:

• Integration with multiple AI providers

• Easily generate AI promts

• Advanced prompts via chat requests

• Build complex message objects

• Create AI service objects

• Enable models to fetch data and take actions

• Fluent API

• Asynchronous chat requests

• Global defaults

• And much more

Settings

Here are the settings you can place in your boxlang.json file:

{
	"modules" : {
		"bxai" : {
			// The provider to use: openai, deepseek, gemini, grok, perplexity, etc
			provider : "openai",
			// The API Key for the provider
			apiKey : "",
			// The default request params to use when calling a provider
			// Ex: { temperature: 0.5, max_tokens: 100, model: "gpt-3.5-turbo" }
			defaultParams = {
				// model: "gpt-3.5-turbo"
			}
		}
	}
}

Global Functions (BIFs)

This module exposes the following BoxLang global functions (BIFs) for you to interact with the AI providers:

• aiChat( messages, struct params={}, struct options={} ) : This function will allow you to chat with the AI provider and get responses back. This is the easiest way to interact with the AI providers.

• aiChatAsync( messages, struct params={}, struct options={} ) : This function will allow you to chat with the AI provider and get a BoxLang future back so you can build fluent asynchronous code pipelines.

• aiChatRequest( messages, struct params, struct options, struct headers) - This allows you to compose a raw chat request that you can then later send to an AI service. The return is a ChatRequest object that you can then send to the AI service.

• aiMessage( message ) - Allows you to build a message object that you can then use to send to the aiChat() or aiChatRequest() functions. It allows you to fluently build up messages as well.

• aiService( provider, apiKey ) - Creates a reference to an AI Service provider that you can then use to interact with the AI service. This is useful if you want to create a service object and then use it multiple times. You can pass in optional provider and apiKey to override the global settings.

• aiTool( name, description, callable) - Creates a tool object that you can use to add to a chat request for real-time system processing. This is useful if you want to create a tool that can be used in multiple chat requests against localized resources. You can then pass in the tool to the aiChat() or aiChatRequest() functions.

aiChat()/aiChatAsync() - Chat with the AI

The aiChat(), aiChatAsync() functions are the easiest way to interact with the AI providers in a consistent and abstracted way. Here are the signatures of the function:

aiChat( messages, struct params={}, struct options={} )
aiChatAsync( messages, struct params={}, struct options={} )

Here are the parameters:

• messages : This can be any of the following

  • A string : A message with a default role of user will be used

  • A struct : A struct with a role and content key message

  • An array of structs : An array of messages that must have a role and a content keys

  • A ChatMessage object

• params : This is a struct of request parameters that will be passed to the AI provider. This can be anything the provider supports. Usually this is the model, temperature, max_tokens, etc.

• options : This is a struct of options that can be used to control the behavior of the AI provider. The available options are:

  • provider:string : The provider to use, if not passed it will use the global setting

  • apiKey:string : The API Key to use, if not passed it will use the global setting

  • timeout:numeric : The timeout in milliseconds for the request. Default is 30 seconds.

  • logRequest:boolean : Log the request to the ai.log. Default is false

  • logResponse:boolean : Log the response to the ai.log. Default is false

  • returnFormat:string : The format of the response. The default is a single message. The available formats are:

    • single : A single message

    • all : An array of messages

    • raw : The raw response from the AI provider

The aiChat() function will return a message according to the options.returnFormat type. If you use aiChatAsync() it will return a BoxLang future so you can build fluent asynchronous code pipelines.

Don't worry that you must do a role and content in your messages if you use a struct or an array of structs. The ai providers will understand the structure and process it accordingly.

System Messages

The messages argument as explained allows you to send 3 different types of messages. Another caveat is that there can only be one system message per request.

Examples

Here are some examples of chatting with the AI:

// Simple chat
aiChat( "Write a haiku about recursion in programming." );

// Structural chat
aiChat( {
	"role": "user",
	"content": "Write a haiku about recursion in programming."
} );

// Using an array of messages
aiChat( [
	{
		"role": "system",
		"content": "You are a helpful assistant."
	},
	{
		"role": "user",
		"content": "Write a haiku about recursion in programming."
	}
] );

// Analyze an image
aiChat( {
	"role": "user",
	"content": [
		{
			"type": "text",
			"text": "What is in this image?"
		},
		{
			"type": "image_url",
			"image_url": {
				"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
			}
		}
	]
} );

Now let's do some async chatting. The benefit of async chatting is that you can build fluent asynchronous code pipelines and not block the main thread. Once you are ready for retrieval of the results, then you can use the blocking get() method on the future.

var future = aiChatAsync( "Write a haiku about recursion in programming." )
    .then( result -> {
        println( "AI Response: " + result );
        return result;
    } )
    .onError( error -> {
        writeLog( text: "AI Chat failed: " + error.getMessage(), type: "error" );
        return "An error occurred. Please try again.";
    } );

// Later in the code, you can retrieve the result
<h2>Chat Response</h2>
<p>#future.get()#</p>

// Transforming and formatting the response
var future = aiChatAsync( "Write a haiku about recursion in programming." )
    .then( result -> "### AI-Generated Haiku
	<br>
	#result.trim()#
	" )
    .onError( error -> {
        writeLog( text: "AI Chat failed: " + error.getMessage(), type: "error" );
        return "### AI Error
		<p>#error.getMessage()#</p>
		<p>An unexpected error occurred</p>
		<p>Please try again</p>
		";
    } );

// Print or return the formatted result
println( future.get() );

aiChatRequest() - Compose a Chat Request

The aiChatRequest() function allows you to compose a raw chat request that you can then later send to an AI service. The return is a ChatRequest object that you can then send to the AI service.

aiChatRequest( messages, struct params, struct options, struct headers )

Here are the parameters:

• messages : This can be any of the following

  • A string : A message with a default role of user will be used

  • A struct : A struct with a role and content key message

  • An array of structs : An array of messages that must have a role and a content keys

  • A ChatMessage object

• params : This is a struct of request parameters that will be passed to the AI provider. This can be anything the provider supports. Usually this is the model, temperature, max_tokens, etc.

• options : This is a struct of options that can be used to control the behavior of the AI provider. The available options are:

  • provider:string : The provider to use, if not passed it will use the global setting

  • apiKey:string : The API Key to use, if not passed it will use the global setting

  • timeout:numeric : The timeout in milliseconds for the request. Default is 30 seconds.

  • logRequest:boolean : Log the request to the ai.log. Default is false

  • logResponse:boolean : Log the response to the ai.log. Default is false

  • returnFormat:string : The format of the response. The default is a single message. The available formats are:

    • single : A single message

    • all : An array of messages

    • raw : The raw response from the AI provider

• headers : This is a struct of headers that can be used to send to the AI provider.

ChatRequest Properties

The ChatRequest object has several properties that you can use to interact with the request. All of them have a getter and a setter.

• messages:array : The messages to send to the AI provider

• params:struct : The request parameters to send to the AI provider

• provider:string : The provider to use

• apiKey:string : The API Key to use

• logRequest:boolean : Log the request to the ai.log

• logResponse:boolean : Log the response to the ai.log

• returnFormat:string : The format of the response

• timeout:numeric : The timeout in milliseconds for the request. Default is 30 seconds.

• sendAuthHeader:boolean : Send the API Key as an Authorization header. Default is true

• headers:struct : The headers to send to the AI provider

ChatRequest Methods

The ChatRequest object has several methods that you can use to interact with the request apart from the aforementioned properties setters and getters.

• addHeader( name, value ):ChatRequest : Add a header to the request

• getTool( name ):Attempt : Get a tool from the defined params

• hasMessages():boolean : Check if the request has messages

• hasModel():boolean : Check if the request has a model

• setModelIfEmpty( model ):ChatRequest : Set the model if it is empty

• hasApiKey():boolean : Check if the request has an API Key

• setApiKeyIfEmpty( apiKey ):ChatRequest : Set the API Key if it is empty

Examples

Here are some examples of composing a chat request:

// Simple chat request
chatRequest = aiChatRequest( "Write a haiku about recursion in programming." )
response = aiService().invoke( chatRequest )

// Advanced request
chatRequest = aiChatRequest( "Write a haiku about recursion in programming.", {
		"model": "gpt-3.5-turbo",
		"temperature": 0.5,
		"max_tokens": 100
	},
	{
		"provider": "grok",
		"timeout": 10,
		"logRequest": true,
		"logResponse": true,
		"returnFormat": "raw"
	} );
response = aiService().invoke( chatRequest )

aiMessage() - Build a Message Object

This function allows you to build up messages that you can then use to send to the aiChat() or aiChatRequest() functions. It allows you to fluently build up messages as well as it implements onMissingMethod(). Meaning that any method call that is not found in the ChatMessage object will be treated as roled message: system( "message" ), user( "message" ), assistant( "message" ). This method returns a ChatMessage object.

This is also useful so you can keep track of your messages.

Please note that the ai-plus module supports chat memory and more.

The aiMessage() function has the following signature:

aiMessage( message )

Here are the parameters:

• message : This can be any of the following

  • A string : A message with a default role of user will be used

  • A struct : A struct with a role and content key message

  • An array of structs : An array of messages that must have a role and a content keys

  • A ChatMessage object itself.

ChatMessage Methods

The ChatMessage object has several methods that you can use to interact with the message.

• count():numeric : Get the count of messages

• getMessages():array : Get the messages

• setMessages( messagaes ):ChatMessage : Set the messages

• clear():ChatMessage : Clear the messages

• hasSystemMessage():boolean : Check if the message has a system message

• getSystemMessage():string : Get the system message, if any.

• replaceSystemMessage( content ) : Replace the system message with a new one

• add( content ):ChatMessage : Add a message to the messages array

ChatMessage Dynamic Methods

The ChatMessage object is dynamic and will treat any method call that is not found as a roled message according to the name of the method you call. This allows you to build up messages fluently.

aiMessage()
	.system( "You are a helpful assistant." )
	.user( "Write a haiku about recursion in programming." )
	.user( "What is the capital of France?" )

Examples

Here are a few examples of building up messages and sending them to the aiChat() or aiChatRequest() functions:

aiChat(
	aiMessage()
		.system( "You are a helpful assistant." )
		.user( "Write a haiku about recursion in programming." )
		.user( "What is the capital of France?" )
)

aiService() - Create an AI Service Object

This function allows you to create a reference to an AI Service provider that you can then use to interact with an AI service. This is useful when you need to interact with a specific implementation of our IAService interface.

The aiService() function has the following signature:

aiService( provider, apiKey )

Here are the parameters:

• provider : The provider to use, if not passed it will use the global setting

• apiKey : The API Key to use, if not passed it will use the global setting

Service Methods

Here are some useful methods each provider implements and gets via the BaseService abstract class.

• getName():string : Get the name of the AI Service

• configure( apiKey ):IService : Configure the service with an override API key

• invoke( chatRequest ):any : Invoke the provider service with a ChatRequest object

• getChatURL():string : Get the chat URL of the provider

• setChatURL( url ):IService : Set the chat URL of the provider

• defaults( struct params ):IService : Set the default parameters for the provider

IAiService Interface

Here is the interface that all AI Service providers must implement:

/**
 * Interface for all AI Service classes
 */
interface{

	/**
	 * Get the name of the LLM
	 */
	function getName();

	/**
	 * Configure the service with an override API key
	 *
	 * @apiKey - The API key to use with the provider
	 *
	 * @return The service instance
	 */
	IService function configure( required any apiKey );

	/**
	 * Invoke the provider service with a ChatRequest object
	 *
	 * @chatRequest The ChatRequest object to send to the provider
	 *
	 * @return The response from the service, which can be anything according to their specs: string, or struct, or whatever
	 */
	function invoke( required ChatRequest chatRequest );

}

BaseService

We have also provided a BaseService that implements the interface using the OpenAI standard. This is a great starting point for you to create your own AI Service provider if needed.

Examples

Here are a few examples of creating an AI Service object and interacting with it:

// Create a service object
service = aiProvider( "grok" )
	.configure( "myApiKey" )
	.defaults( { model: "gpt-3.5-turbo", temperature: 0.5, max_tokens: 100 } )

// Invoke the service
response = service.invoke( aiChatRequest( "Write a haiku about recursion in programming." ) )
// Or
response = service.invoke(
	aiChatRequest( "Write a haiku about recursion in programming.", { model: "gpt-3.5-turbo", temperature: 0.5, max_tokens: 100 } )
)

aiTool() - Function Calling

Function calling is one of the most powerful features of an AI chat model. It allows you to connect the chat to a localized service that you can call and interface with real-time systems. You can combine the power of the LLM with your own systems to bring further intelligence and context.

The aiTool() function allows you to create a tool object that you can use to add to a chat request for real-time system processing. This is useful if you want to create a tool that can be used in multiple chat requests against localized/externalized resources. You can then pass in the tool to the aiChat() or aiChatRequest() functions.

Function calling provides a powerful and flexible way for AI models to interface with your code or external services. https://platform.openai.com/docs/guides/function-calling?api-mode=chat#overview

The aiTool() function has the following signature:

aiTool( name, description, callable )

Here are the parameters:

• name : The name of the tool sent to the AI provider

• description : Describe the function. This is used by the AI to communicate the purpose of the function.

• callable : A closure/lambda to call when the tool is invoked that talks to your real-time system.

The arguments you designate in your closure/lambda will be used to build an automatic schema for you.

tool1 = aiTool(
	"myTool",
	// I will expect a name and age arguments
	( name, age ) -> {
		return "Hello World";
	} )
	.describe( "My Tool Function" )
	.describeName( "The name of the person" )
	.describeAge( "The age of the person" )

Once a tool object is made, you can pass them into a chat's or chat request's params via the tools array.

result = aiChat( messages = "How hot is it in Kansas City? What about San Salvador? Answer with only the name of the warmer city, nothing else.", params = {
	tools: [ tool1, tool2, tool3 ],
	seed: 27
} )

Tool Properties

The Tool object has several properties that you can use to interact with once built. Each of the properies has a getter/setter.

• name:string : The name of the tool

• description:string : The description of the tool

• callable:function : The closure/lambda to call when the tool is invoked

• schema:struct : The schema of the tool

• argDescriptions:struc : The argument descriptions of the tool

Tool Methods

The Tool object has several methods that you can use to interact with once built.

• describeFunction( description ):Tool : Describe the function of the tool

• describeArg( name, description ):Tool : Describe an argument of the tool

• call( callable ):Tool : Set the callable closure/lambda of the tool

Dynamic Tool Methods

The Tool object also listens to dynamic methods so you can build fluent descriptions of the function or arguments using the describe{argument}() methods.

aiTool(
	"myTool",
	( args ) -> {
		return "Hello World";
	} )
	.describe( "My Tool Function" )
	.describeName( "The name of the person" )
	.describeAge( "The age of the person" )

Examples

Let's build a sample AI tool that can be used in a chat request and talk to our local runtime to get realtime weather information.

weatherTool = aiTool(
	"get_weather",
	"Get current temperature for a given location.",
	( location ) -> {
	if( location contains "Kansas City" ) {
		return "85"
	}

	if( location contains "San Salvador" ){
		return "90"
	}

	return "unknown";
	} )
	.describeLocation( "City and country e.g. Bogotá, Colombia" )

result = aiChat( 
  "How hot is it in Kansas City? What about San Salvador? Answer with only the name of the warmer city, nothing else.", 
  {
    tools: [ weatherTool ],
    seed: 27
  } )

println( result )

GitHub Repository and Reporting Issues

Visit the GitHub repository for release notes. You can also file a bug report or improvement suggestion via Jira.