@callmedayz/ai-prompt-toolkit/dist/utils.js

Professional AI prompt engineering toolkit with advanced template features, real-time dashboards, conditional logic, template inheritance, live monitoring, OpenRouter integration, and 310+ model support

"use strict";
// Utility functions for ai-prompt-toolkit
Object.defineProperty(exports, "__esModule", { value: true });
exports.getTokenCount = getTokenCount;
exports.getDetailedTokenCount = getDetailedTokenCount;
exports.compareTokenCounts = compareTokenCounts;
exports.estimateTokens = estimateTokens;
exports.chunkText = chunkText;
exports.validatePrompt = validatePrompt;
exports.optimizePrompt = optimizePrompt;
exports.fitsInModel = fitsInModel;
exports.recommendModel = recommendModel;
exports.calculateCost = calculateCost;
exports.getPromptQuality = getPromptQuality;
exports.chunkForModel = chunkForModel;
exports.optimizeToTarget = optimizeToTarget;
exports.analyzePrompt = analyzePrompt;
const token_counter_1 = require("./token-counter");
const text_chunker_1 = require("./text-chunker");
const prompt_validator_1 = require("./prompt-validator");
const prompt_optimizer_1 = require("./prompt-optimizer");
const openrouter_types_1 = require("./openrouter-types");
/**
 * Get accurate token count using OpenRouter API (async)
 */
async function getTokenCount(text, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return token_counter_1.TokenCounter.getTokenCount(text, model);
}
/**
 * Get detailed token count with native tokenizer information
 */
async function getDetailedTokenCount(text, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return token_counter_1.TokenCounter.getDetailedTokenCount(text, model);
}
/**
 * Compare estimation accuracy against real API tokenization
 */
async function compareTokenCounts(text, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return token_counter_1.TokenCounter.compareTokenCounts(text, model);
}
/**
 * Quick token estimation utility function (synchronous fallback)
 */
function estimateTokens(text, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return token_counter_1.TokenCounter.estimateTokens(text, model);
}
/**
 * Quick text chunking utility function
 */
function chunkText(text, options) {
    return text_chunker_1.TextChunker.chunkText(text, options);
}
/**
 * Quick prompt validation utility function
 */
function validatePrompt(prompt, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return prompt_validator_1.PromptValidator.validate(prompt, model);
}
/**
 * Quick prompt optimization utility function
 */
function optimizePrompt(prompt, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return prompt_optimizer_1.PromptOptimizer.optimize(prompt, model);
}
/**
 * Check if text fits in a specific model's context window
 */
function fitsInModel(text, model) {
    return token_counter_1.TokenCounter.fitsInModel(text, model);
}
/**
 * Get the best model recommendation for a given text
 */
function recommendModel(text) {
    return token_counter_1.TokenCounter.recommendModel(text);
}
/**
 * Calculate the cost of processing text with a specific model
 */
function calculateCost(text, model) {
    return token_counter_1.TokenCounter.calculateCost(text, model);
}
/**
 * Get a quality score for a prompt (0-100)
 */
function getPromptQuality(prompt, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return prompt_validator_1.PromptValidator.getQualityScore(prompt, model);
}
/**
 * Chunk text specifically for a model with optimal settings
 */
function chunkForModel(text, model, overlapPercent = 10) {
    return text_chunker_1.TextChunker.chunkForModel(text, model, overlapPercent);
}
/**
 * Optimize prompt to fit within a specific token limit
 */
function optimizeToTarget(prompt, targetTokens, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    return prompt_optimizer_1.PromptOptimizer.optimizeToTarget(prompt, targetTokens, model);
}
/**
 * Get comprehensive analysis of a prompt
 */
function analyzePrompt(prompt, model = openrouter_types_1.DEFAULT_FREE_MODEL) {
    const tokens = estimateTokens(prompt, model);
    const validation = validatePrompt(prompt, model);
    const quality = getPromptQuality(prompt, model);
    const recommendation = recommendModel(prompt);
    return {
        tokens,
        validation,
        quality,
        recommendation,
        fitsInModel: fitsInModel(prompt, model),
        cost: calculateCost(prompt, model)
    };
}
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