const RWKV = require("RWKV-cpp-node");

// Load the module with the pre-qunatized cpp weights
const raven = new RWKV("<path-to-your-model-bin-files>")

// Call the completion API
let res = raven.completion("RWKV is a")

// And log, or do something with the result
console.log( res.completion )

// You can setup with the following parameters with a config object (instead of a string path)
const raven = new RWKV({
	// Path to your cpp weights
	path: "<path-to-your-model-bin-files>",

	// Threads count to use, this is auto detected based on your number of vCPU
	// if its not configured
	threads: 8,

	//
	// Cache size for the RKWV state, This help optimize the repeated RWKV calls
	// in use cases such as "conversation", allow it to skip the previous chat computation
	//
	// it is worth noting that the 7B model takes up about 2.64 MB for the state buffer, 
	// meaning you will need atleast 264 MB of RAM for a cachesize of 100
	//
	// This defaults to 50
	// Set to false or 0 to disable
	//
	stateCacheSize: 50
});

// Lets perform a completion, with more options
let res = raven.completion({

	// The prompt to use
	prompt: "<prompt str>",

	// Completion default settings
	// See openai docs for more details on what these do for your output if you do not understand them
	// https://platform.openai.com/docs/api-reference/completions
	max_tokens: 64,
	temperature: 1.0,
	top_p: 1.0,
	stop: [ "\n" ],

	// Streaming of output, either token by token, or the full complete output stream
	streamCallback: function(tokenStr, fullCompletionStr) {
		// ....
	},

	// Existing RWKV hidden state, represented as a Flaot32Array
	// do not use this unless you REALLY KNOW WHAT YOUR DOING
	//
	// This will skip the state caching logic 
	initState: (Special Float32Array)
});

// Additionally if you have a commonly reused instruction set prefix, you can preload this
// using either of the following (requires the stateCacheSize to not be disabled)
raven.preloadPrompt( "<prompt prefix string>" )
raven.completion({ prompt:"<prompt prefix string>", max_tokens:0 })

// The following is a sample of the result object format
let resFormat = {
	// Completion generated
	completion: '<completion string used>',

	// Prompt used
	prompt: '<prompt string used>',

	// Token usage numbers
	usage: {
		promptTokens: 41,
		completionTokens: 64,
		totalTokens: 105,
		// number of tokens in the prompt that was previously cached
		promptTokensCached: 39 
	},

	// Performance statistics of the completion operation
	//
	// the following perf numbers is from a single 
	// `Intel(R) Xeon(R) CPU E5-2695 v3 @ 2.30GHz`
	// an old 2014 processor, with 28 vCPU 
	// with the 14B model Q8_0 quantized
	// 
	perf: {
		// Time taken in ms for each segment
		promptTime: 954,
		completionTime: 35907,
		totalTime: 36861,

		// Time taken in ms to process each token at the respective phase
		timePerPrompt: 477, // This excludes cached tokens
		timePerCompletion: 561.046875,
		timePerFullPrompt: 23.26829268292683, // This includes cached tokens (if any)

		// The average tokens per second
		promptPerSecond: 2.0964360587002098, // This excludes cached tokens
		completionPerSecond: 1.7823822652964603,
		fullPromptPerSecond: 42.9769392033543 // This includes cached tokens (if any)
	}
}