/** * Token Usage Tracking - Extract, normalize, estimate and log token usage */ import { saveRequestUsage, appendRequestLog } from "@/lib/usageDb.js"; import { FORMATS } from "../translator/formats.js"; // ANSI color codes export const COLORS = { reset: "\x1b[0m", red: "\x1b[31m", green: "\x1b[32m", yellow: "\x1b[33m", blue: "\x1b[34m", cyan: "\x1b[36m" }; // Buffer tokens to prevent context errors const BUFFER_TOKENS = 2000; // Get HH:MM:SS timestamp function getTimeString() { return new Date().toLocaleTimeString("en-US", { hour12: false, hour: "2-digit", minute: "2-digit", second: "2-digit" }); } /** * Add buffer tokens to usage to prevent context errors * @param {object} usage - Usage object (any format) * @returns {object} Usage with buffer added */ export function addBufferToUsage(usage) { if (!usage || typeof usage !== "object") return usage; const result = { ...usage }; // Claude format if (result.input_tokens !== undefined) { result.input_tokens += BUFFER_TOKENS; } // OpenAI format if (result.prompt_tokens !== undefined) { result.prompt_tokens += BUFFER_TOKENS; } // Update total_tokens if exists if (result.total_tokens !== undefined) { result.total_tokens += BUFFER_TOKENS; } return result; } /** * Normalize usage object - ensure all values are valid numbers */ export function normalizeUsage(usage) { if (!usage || typeof usage !== "object" || Array.isArray(usage)) return null; const normalized = {}; const assignNumber = (key, value) => { if (value === undefined || value === null) return; const numeric = Number(value); if (Number.isFinite(numeric)) normalized[key] = numeric; }; assignNumber("prompt_tokens", usage?.prompt_tokens); assignNumber("completion_tokens", usage?.completion_tokens); assignNumber("cache_read_input_tokens", usage?.cache_read_input_tokens); assignNumber("cache_creation_input_tokens", usage?.cache_creation_input_tokens); assignNumber("cached_tokens", usage?.cached_tokens); assignNumber("reasoning_tokens", usage?.reasoning_tokens); if (Object.keys(normalized).length === 0) return null; return normalized; } /** * Check if usage has valid token data * Valid = has at least one token field with value > 0 * Invalid = empty object {}, null, undefined, no token fields, or all zeros */ export function hasValidUsage(usage) { if (!usage || typeof usage !== "object") return false; // Check for any known token field with value > 0 const tokenFields = [ "prompt_tokens", "completion_tokens", "total_tokens", // OpenAI "input_tokens", "output_tokens", // Claude "promptTokenCount", "candidatesTokenCount" // Gemini ]; for (const field of tokenFields) { if (typeof usage[field] === "number" && usage[field] > 0) { return true; } } return false; } /** * Extract usage from any format (Claude, OpenAI, Gemini, Responses API) */ export function extractUsage(chunk) { if (!chunk || typeof chunk !== "object") return null; // Claude format (message_delta event) if (chunk.type === "message_delta" && chunk.usage && typeof chunk.usage === "object") { return normalizeUsage({ prompt_tokens: chunk.usage.input_tokens || 0, completion_tokens: chunk.usage.output_tokens || 0, cache_read_input_tokens: chunk.usage.cache_read_input_tokens, cache_creation_input_tokens: chunk.usage.cache_creation_input_tokens }); } // OpenAI Responses API format (response.completed or response.done) if ((chunk.type === "response.completed" || chunk.type === "response.done") && chunk.response?.usage && typeof chunk.response.usage === "object") { const usage = chunk.response.usage; return normalizeUsage({ prompt_tokens: usage.input_tokens || usage.prompt_tokens || 0, completion_tokens: usage.output_tokens || usage.completion_tokens || 0, cached_tokens: usage.input_tokens_details?.cached_tokens, reasoning_tokens: usage.output_tokens_details?.reasoning_tokens }); } // OpenAI format if (chunk.usage && typeof chunk.usage === "object" && chunk.usage.prompt_tokens !== undefined) { return normalizeUsage({ prompt_tokens: chunk.usage.prompt_tokens, completion_tokens: chunk.usage.completion_tokens || 0, cached_tokens: chunk.usage.prompt_tokens_details?.cached_tokens, reasoning_tokens: chunk.usage.completion_tokens_details?.reasoning_tokens }); } // Gemini format (Antigravity) if (chunk.usageMetadata && typeof chunk.usageMetadata === "object") { return normalizeUsage({ prompt_tokens: chunk.usageMetadata?.promptTokenCount || 0, completion_tokens: chunk.usageMetadata?.candidatesTokenCount || 0, cached_tokens: chunk.usageMetadata?.cachedContentTokenCount, reasoning_tokens: chunk.usageMetadata?.thoughtsTokenCount }); } return null; } /** * Estimate input tokens from request body * Calculate total body size for more accurate estimation */ export function estimateInputTokens(body) { if (!body || typeof body !== "object") return 0; try { // Calculate total body size (includes messages, tools, system, thinking config, etc.) const bodyStr = JSON.stringify(body); const totalChars = bodyStr.length; // Estimate: ~4 chars per token (rough average across all tokenizers) return Math.ceil(totalChars / 4); } catch (err) { // Fallback if stringify fails return 0; } } /** * Estimate output tokens from content length */ export function estimateOutputTokens(contentLength) { if (!contentLength || contentLength <= 0) return 0; return Math.max(1, Math.floor(contentLength / 4)); } /** * Format usage object based on target format * @param {number} inputTokens - Input/prompt tokens * @param {number} outputTokens - Output/completion tokens * @param {string} targetFormat - Target format from FORMATS */ export function formatUsage(inputTokens, outputTokens, targetFormat) { // Claude format uses input_tokens/output_tokens if (targetFormat === FORMATS.CLAUDE) { return addBufferToUsage({ input_tokens: inputTokens, output_tokens: outputTokens, estimated: true }); } // Default: OpenAI format (works for openai, gemini, responses, etc.) return addBufferToUsage({ prompt_tokens: inputTokens, completion_tokens: outputTokens, total_tokens: inputTokens + outputTokens, estimated: true }); } /** * Estimate full usage when provider doesn't return it * @param {object} body - Request body for input token estimation * @param {number} contentLength - Content length for output token estimation * @param {string} targetFormat - Target format from FORMATS constant */ export function estimateUsage(body, contentLength, targetFormat = FORMATS.OPENAI) { return formatUsage( estimateInputTokens(body), estimateOutputTokens(contentLength), targetFormat ); } /** * Log usage with cache info (green color) */ export function logUsage(provider, usage, model = null, connectionId = null) { if (!usage || typeof usage !== "object") return; const p = provider?.toUpperCase() || "UNKNOWN"; // Support both formats: // - OpenAI: prompt_tokens, completion_tokens // - Claude: input_tokens, output_tokens const inTokens = usage?.prompt_tokens || usage?.input_tokens || 0; const outTokens = usage?.completion_tokens || usage?.output_tokens || 0; const accountPrefix = connectionId ? connectionId.slice(0, 8) + "..." : "unknown"; let msg = `[${getTimeString()}] 📊 ${COLORS.green}[USAGE] ${p} | in=${inTokens} | out=${outTokens} | account=${accountPrefix}${COLORS.reset}`; // Add estimated flag if present if (usage.estimated) { msg += ` ${COLORS.yellow}(estimated)${COLORS.reset}`; } // Add cache info if present (unified from different formats) const cacheRead = usage.cache_read_input_tokens || usage.cached_tokens; if (cacheRead) msg += ` | cache_read=${cacheRead}`; const cacheCreation = usage.cache_creation_input_tokens; if (cacheCreation) msg += ` | cache_create=${cacheCreation}`; const reasoning = usage.reasoning_tokens; if (reasoning) msg += ` | reasoning=${reasoning}`; console.log(msg); // Save to usage DB const tokens = { input: inTokens, output: outTokens, cacheRead: cacheRead || 0, cacheCreation: cacheCreation || 0, reasoning: reasoning || 0 }; saveRequestUsage({ model, provider, connectionId, tokens }).catch(() => { }); appendRequestLog({ model, provider, connectionId, tokens, status: "200 OK" }).catch(() => { }); }