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https://github.com/Nezumi-2711/9router.git
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feat: Update response handling and logging for improved usage tracking
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decolua
@@ -0,0 +1,249 @@
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/**
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* Token Usage Tracking - Extract, normalize, estimate and log token usage
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*/
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import { saveRequestUsage, appendRequestLog } from "@/lib/usageDb.js";
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import { FORMATS } from "../translator/formats.js";
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// ANSI color codes
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export const COLORS = {
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reset: "\x1b[0m",
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red: "\x1b[31m",
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green: "\x1b[32m",
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yellow: "\x1b[33m",
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blue: "\x1b[34m",
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cyan: "\x1b[36m"
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};
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// Get HH:MM:SS timestamp
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function getTimeString() {
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return new Date().toLocaleTimeString("en-US", { hour12: false, hour: "2-digit", minute: "2-digit", second: "2-digit" });
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}
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/**
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* Normalize usage object - ensure all values are valid numbers
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*/
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export function normalizeUsage(usage) {
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if (!usage || typeof usage !== "object" || Array.isArray(usage)) return null;
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const normalized = {};
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const assignNumber = (key, value) => {
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if (value === undefined || value === null) return;
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const numeric = Number(value);
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if (Number.isFinite(numeric)) normalized[key] = numeric;
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};
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assignNumber("prompt_tokens", usage?.prompt_tokens);
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assignNumber("completion_tokens", usage?.completion_tokens);
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assignNumber("cache_read_input_tokens", usage?.cache_read_input_tokens);
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assignNumber("cache_creation_input_tokens", usage?.cache_creation_input_tokens);
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assignNumber("cached_tokens", usage?.cached_tokens);
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assignNumber("reasoning_tokens", usage?.reasoning_tokens);
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if (Object.keys(normalized).length === 0) return null;
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return normalized;
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}
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/**
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* Check if usage has valid token data
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* Valid = has at least one token field with value > 0
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* Invalid = empty object {}, null, undefined, no token fields, or all zeros
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*/
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export function hasValidUsage(usage) {
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if (!usage || typeof usage !== "object") return false;
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// Check for any known token field with value > 0
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const tokenFields = [
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"prompt_tokens", "completion_tokens", "total_tokens", // OpenAI
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"input_tokens", "output_tokens", // Claude
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"promptTokenCount", "candidatesTokenCount" // Gemini
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];
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for (const field of tokenFields) {
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if (typeof usage[field] === "number" && usage[field] > 0) {
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return true;
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}
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}
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return false;
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}
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/**
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* Extract usage from any format (Claude, OpenAI, Gemini, Responses API)
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*/
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export function extractUsage(chunk) {
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if (!chunk || typeof chunk !== "object") return null;
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// Claude format (message_delta event)
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if (chunk.type === "message_delta" && chunk.usage && typeof chunk.usage === "object") {
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return normalizeUsage({
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prompt_tokens: chunk.usage.input_tokens || 0,
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completion_tokens: chunk.usage.output_tokens || 0,
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cache_read_input_tokens: chunk.usage.cache_read_input_tokens,
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cache_creation_input_tokens: chunk.usage.cache_creation_input_tokens
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});
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}
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// OpenAI Responses API format (response.completed or response.done)
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if ((chunk.type === "response.completed" || chunk.type === "response.done") && chunk.response?.usage && typeof chunk.response.usage === "object") {
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const usage = chunk.response.usage;
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return normalizeUsage({
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prompt_tokens: usage.input_tokens || usage.prompt_tokens || 0,
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completion_tokens: usage.output_tokens || usage.completion_tokens || 0,
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cached_tokens: usage.input_tokens_details?.cached_tokens,
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reasoning_tokens: usage.output_tokens_details?.reasoning_tokens
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});
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}
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// OpenAI format
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if (chunk.usage && typeof chunk.usage === "object" && chunk.usage.prompt_tokens !== undefined) {
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return normalizeUsage({
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prompt_tokens: chunk.usage.prompt_tokens,
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completion_tokens: chunk.usage.completion_tokens || 0,
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cached_tokens: chunk.usage.prompt_tokens_details?.cached_tokens,
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reasoning_tokens: chunk.usage.completion_tokens_details?.reasoning_tokens
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});
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}
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// Gemini format (Antigravity)
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if (chunk.usageMetadata && typeof chunk.usageMetadata === "object") {
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return normalizeUsage({
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prompt_tokens: chunk.usageMetadata?.promptTokenCount || 0,
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completion_tokens: chunk.usageMetadata?.candidatesTokenCount || 0,
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cached_tokens: chunk.usageMetadata?.cachedContentTokenCount,
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reasoning_tokens: chunk.usageMetadata?.thoughtsTokenCount
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});
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}
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return null;
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}
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/**
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* Estimate input tokens from request body
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*/
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export function estimateInputTokens(body) {
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if (!body || typeof body !== "object") return 0;
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let totalChars = 0;
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// Count messages
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if (Array.isArray(body.messages)) {
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for (const msg of body.messages) {
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if (msg.content) {
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if (typeof msg.content === "string") {
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totalChars += msg.content.length;
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} else if (Array.isArray(msg.content)) {
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for (const part of msg.content) {
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if (part.text) totalChars += part.text.length;
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if (part.type === "image_url") totalChars += 85; // Rough estimate for images
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}
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}
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}
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if (msg.role) totalChars += msg.role.length;
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}
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}
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// Count tools/functions
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if (Array.isArray(body.tools)) {
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totalChars += JSON.stringify(body.tools).length;
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} else if (Array.isArray(body.functions)) {
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totalChars += JSON.stringify(body.functions).length;
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}
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// Count system prompt
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if (body.system) {
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totalChars += typeof body.system === "string" ? body.system.length : JSON.stringify(body.system).length;
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}
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// Estimate: ~4 chars per token (rough average across all tokenizers)
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return Math.ceil(totalChars / 4);
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}
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/**
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* Estimate output tokens from content length
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*/
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export function estimateOutputTokens(contentLength) {
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if (!contentLength || contentLength <= 0) return 0;
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return Math.max(1, Math.floor(contentLength / 4));
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}
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/**
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* Format usage object based on target format
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* @param {number} inputTokens - Input/prompt tokens
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* @param {number} outputTokens - Output/completion tokens
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* @param {string} targetFormat - Target format from FORMATS
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*/
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export function formatUsage(inputTokens, outputTokens, targetFormat) {
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// Claude format uses input_tokens/output_tokens
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if (targetFormat === FORMATS.CLAUDE) {
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return { input_tokens: inputTokens, output_tokens: outputTokens, estimated: true };
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}
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// Default: OpenAI format (works for openai, gemini, responses, etc.)
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return {
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prompt_tokens: inputTokens,
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completion_tokens: outputTokens,
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total_tokens: inputTokens + outputTokens,
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estimated: true
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};
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}
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/**
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* Estimate full usage when provider doesn't return it
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* @param {object} body - Request body for input token estimation
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* @param {number} contentLength - Content length for output token estimation
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* @param {string} targetFormat - Target format from FORMATS constant
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*/
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export function estimateUsage(body, contentLength, targetFormat = FORMATS.OPENAI) {
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return formatUsage(
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estimateInputTokens(body),
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estimateOutputTokens(contentLength),
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targetFormat
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);
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}
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/**
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* Log usage with cache info (green color)
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*/
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export function logUsage(provider, usage, model = null, connectionId = null) {
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if (!usage || typeof usage !== "object") return;
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const p = provider?.toUpperCase() || "UNKNOWN";
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// Support both formats:
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// - OpenAI: prompt_tokens, completion_tokens
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// - Claude: input_tokens, output_tokens
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const inTokens = usage?.prompt_tokens || usage?.input_tokens || 0;
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const outTokens = usage?.completion_tokens || usage?.output_tokens || 0;
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const accountPrefix = connectionId ? connectionId.slice(0, 8) + "..." : "unknown";
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let msg = `[${getTimeString()}] 📊 ${COLORS.green}[USAGE] ${p} | in=${inTokens} | out=${outTokens} | account=${accountPrefix}${COLORS.reset}`;
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// Add estimated flag if present
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if (usage.estimated) {
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msg += ` ${COLORS.yellow}(estimated)${COLORS.reset}`;
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}
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// Add cache info if present (unified from different formats)
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const cacheRead = usage.cache_read_input_tokens || usage.cached_tokens;
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if (cacheRead) msg += ` | cache_read=${cacheRead}`;
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const cacheCreation = usage.cache_creation_input_tokens;
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if (cacheCreation) msg += ` | cache_create=${cacheCreation}`;
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const reasoning = usage.reasoning_tokens;
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if (reasoning) msg += ` | reasoning=${reasoning}`;
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console.log(msg);
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// Save to usage DB
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const tokens = {
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input: inTokens,
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output: outTokens,
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cacheRead: cacheRead || 0,
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cacheCreation: cacheCreation || 0,
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reasoning: reasoning || 0
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};
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saveRequestUsage({ model, provider, connectionId, tokens }).catch(() => { });
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appendRequestLog({ model, provider, connectionId, tokens, status: "200 OK" }).catch(() => { });
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}
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