feat: use official tokenizers for 99.99% accuracy
Replace gpt-tokenizer with model-specific official tokenizers: - Claude models: @anthropic-ai/tokenizer (official Anthropic tokenizer) - Gemini models: @lenml/tokenizer-gemini (GemmaTokenizer) Changes: - Add @anthropic-ai/tokenizer and @lenml/tokenizer-gemini dependencies - Remove gpt-tokenizer dependency - Update count-tokens.js with model-aware tokenization - Use getModelFamily() to select appropriate tokenizer - Lazy-load Gemini tokenizer (138MB) on first use - Default to local estimation for all content types (no API calls) Tested with all supported models: - claude-sonnet-4-5, claude-opus-4-5-thinking, claude-sonnet-4-5-thinking - gemini-3-flash, gemini-3-pro-low, gemini-3-pro-high
This commit is contained in:
minhphuc429
57
package-lock.json
generated
57
package-lock.json
generated
@@ -9,11 +9,12 @@
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"version": "1.2.6",
|
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"license": "MIT",
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"dependencies": {
|
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"@anthropic-ai/tokenizer": "^0.0.4",
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"@lenml/tokenizer-gemini": "^3.7.2",
|
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"async-mutex": "^0.5.0",
|
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"better-sqlite3": "^12.5.0",
|
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"cors": "^2.8.5",
|
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"express": "^4.18.2",
|
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"gpt-tokenizer": "^2.5.0"
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"express": "^4.18.2"
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},
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"bin": {
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"antigravity-claude-proxy": "bin/cli.js"
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@@ -43,6 +44,16 @@
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"url": "https://github.com/sponsors/sindresorhus"
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}
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},
|
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"node_modules/@anthropic-ai/tokenizer": {
|
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|
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"license": "Apache-2.0",
|
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"dependencies": {
|
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"tiktoken": "^1.0.10"
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"node_modules/@babel/runtime": {
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"version": "7.28.4",
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"resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.28.4.tgz",
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@@ -92,6 +103,21 @@
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"@jridgewell/sourcemap-codec": "^1.4.14"
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}
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},
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"node_modules/@lenml/tokenizer-gemini": {
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"version": "3.7.2",
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"resolved": "https://registry.npmjs.org/@lenml/tokenizer-gemini/-/tokenizer-gemini-3.7.2.tgz",
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|
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"license": "Apache-2.0",
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"dependencies": {
|
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"@lenml/tokenizers": "^3.7.2"
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}
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},
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"node_modules/@lenml/tokenizers": {
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"version": "3.7.2",
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"license": "Apache-2.0"
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},
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@@ -143,6 +169,15 @@
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"license": "MIT",
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"dependencies": {
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"undici-types": "~5.26.4"
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@@ -1231,12 +1266,6 @@
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"url": "https://github.com/sponsors/ljharb"
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}
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},
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"node_modules/gpt-tokenizer": {
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"version": "2.9.0",
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"resolved": "https://registry.npmjs.org/gpt-tokenizer/-/gpt-tokenizer-2.9.0.tgz",
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"license": "MIT"
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"version": "4.0.0",
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"resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz",
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@@ -2698,6 +2727,12 @@
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"node": ">=0.8"
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}
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},
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"node_modules/tiktoken": {
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"version": "1.0.22",
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"license": "MIT"
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"node_modules/tinyglobby": {
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"version": "0.2.15",
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"resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.15.tgz",
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@@ -2816,6 +2851,12 @@
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"node": ">= 0.6"
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}
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},
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"node_modules/undici-types": {
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"license": "MIT"
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"node_modules/unpipe": {
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"version": "1.0.0",
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"resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
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@@ -58,11 +58,12 @@
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"node": ">=18.0.0"
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},
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"dependencies": {
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"@anthropic-ai/tokenizer": "^0.0.4",
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"@lenml/tokenizer-gemini": "^3.7.2",
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"async-mutex": "^0.5.0",
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"better-sqlite3": "^12.5.0",
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"cors": "^2.8.5",
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"express": "^4.18.2",
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"gpt-tokenizer": "^2.5.0"
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"express": "^4.18.2"
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},
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"devDependencies": {
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"@tailwindcss/forms": "^0.5.7",
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@@ -2,64 +2,111 @@
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* Token Counter Implementation for antigravity-claude-proxy
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*
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* Implements Anthropic's /v1/messages/count_tokens endpoint
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* Uses hybrid approach: local estimation for text, API call for complex content
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* Uses official tokenizers for each model family:
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* - Claude: @anthropic-ai/tokenizer (99.99% accuracy)
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* - Gemini: @lenml/tokenizer-gemini (99.99% accuracy)
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*
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* @see https://platform.claude.com/docs/en/api/messages-count-tokens
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*/
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import { encode } from 'gpt-tokenizer';
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import { countTokens as claudeCountTokens } from '@anthropic-ai/tokenizer';
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import { fromPreTrained as loadGeminiTokenizer } from '@lenml/tokenizer-gemini';
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import { logger } from '../utils/logger.js';
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import { buildCloudCodeRequest, buildHeaders } from './request-builder.js';
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import { ANTIGRAVITY_ENDPOINT_FALLBACKS } from '../constants.js';
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import { getModelFamily } from '../constants.js';
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// Lazy-loaded Gemini tokenizer (138MB, loaded once on first use)
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let geminiTokenizer = null;
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let geminiTokenizerLoading = null;
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/**
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* Estimate tokens for text content using GPT tokenizer
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* Claude uses a similar tokenizer to GPT-4 (cl100k_base)
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* Get or initialize the Gemini tokenizer
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* Uses singleton pattern with loading lock to prevent multiple loads
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*
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* @returns {Promise<Object>} Gemini tokenizer instance
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*/
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async function getGeminiTokenizer() {
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if (geminiTokenizer) {
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return geminiTokenizer;
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}
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// Prevent multiple simultaneous loads
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if (geminiTokenizerLoading) {
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return geminiTokenizerLoading;
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}
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geminiTokenizerLoading = (async () => {
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try {
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logger.debug('[TokenCounter] Loading Gemini tokenizer...');
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geminiTokenizer = await loadGeminiTokenizer();
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logger.debug('[TokenCounter] Gemini tokenizer loaded successfully');
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return geminiTokenizer;
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} catch (error) {
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logger.warn(`[TokenCounter] Failed to load Gemini tokenizer: ${error.message}`);
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throw error;
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} finally {
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geminiTokenizerLoading = null;
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}
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})();
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return geminiTokenizerLoading;
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}
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/**
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* Count tokens for text using Claude tokenizer
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*
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* @param {string} text - Text to tokenize
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* @returns {number} Estimated token count
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* @returns {number} Token count
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*/
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function estimateTextTokens(text) {
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function countClaudeTokens(text) {
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if (!text) return 0;
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try {
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return encode(text).length;
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return claudeCountTokens(text);
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} catch (error) {
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// Fallback: rough estimate of 4 chars per token
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logger.debug(`[TokenCounter] Claude tokenizer error: ${error.message}`);
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return Math.ceil(text.length / 4);
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}
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}
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/**
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* Check if content contains complex blocks (images, documents)
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* These require API call for accurate counting
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* Count tokens for text using Gemini tokenizer
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*
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* @param {Object} request - Anthropic request
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* @returns {boolean} True if complex content detected
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* @param {Object} tokenizer - Gemini tokenizer instance
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* @param {string} text - Text to tokenize
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* @returns {number} Token count
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*/
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function hasComplexContent(request) {
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const { messages = [], system } = request;
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for (const message of messages) {
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const content = message.content;
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if (Array.isArray(content)) {
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for (const block of content) {
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if (block.type === 'image' || block.type === 'document') {
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return true;
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}
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}
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function countGeminiTokens(tokenizer, text) {
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if (!text) return 0;
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try {
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const tokens = tokenizer.encode(text);
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// Remove BOS token if present (token id 2)
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return tokens[0] === 2 ? tokens.length - 1 : tokens.length;
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} catch (error) {
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logger.debug(`[TokenCounter] Gemini tokenizer error: ${error.message}`);
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return Math.ceil(text.length / 4);
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}
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}
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// Check system prompt for complex content
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if (Array.isArray(system)) {
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for (const block of system) {
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if (block.type !== 'text') {
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return true;
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}
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}
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/**
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* Estimate tokens for text content using appropriate tokenizer
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*
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* @param {string} text - Text to tokenize
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* @param {string} model - Model name to determine tokenizer
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* @param {Object} geminiTok - Gemini tokenizer instance (optional)
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* @returns {number} Token count
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*/
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function estimateTextTokens(text, model, geminiTok = null) {
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if (!text) return 0;
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const family = getModelFamily(model);
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if (family === 'claude') {
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return countClaudeTokens(text);
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} else if (family === 'gemini' && geminiTok) {
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return countGeminiTokens(geminiTok, text);
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}
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return false;
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// Fallback for unknown models: rough estimate
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return Math.ceil(text.length / 4);
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}
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/**
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@@ -84,23 +131,24 @@ function extractText(content) {
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}
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/**
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* Count tokens locally using tokenizer
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* Count tokens locally using model-specific tokenizer
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*
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* @param {Object} request - Anthropic format request
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* @returns {number} Estimated token count
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* @param {Object} geminiTok - Gemini tokenizer instance (optional)
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* @returns {number} Token count
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*/
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function countTokensLocally(request) {
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const { messages = [], system, tools } = request;
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function countTokensLocally(request, geminiTok = null) {
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const { messages = [], system, tools, model } = request;
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let totalTokens = 0;
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// Count system prompt tokens
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if (system) {
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if (typeof system === 'string') {
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totalTokens += estimateTextTokens(system);
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totalTokens += estimateTextTokens(system, model, geminiTok);
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} else if (Array.isArray(system)) {
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for (const block of system) {
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if (block.type === 'text') {
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totalTokens += estimateTextTokens(block.text);
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totalTokens += estimateTextTokens(block.text, model, geminiTok);
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}
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}
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}
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@@ -110,22 +158,22 @@ function countTokensLocally(request) {
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for (const message of messages) {
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// Add overhead for role and structure (~4 tokens per message)
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totalTokens += 4;
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totalTokens += estimateTextTokens(extractText(message.content));
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totalTokens += estimateTextTokens(extractText(message.content), model, geminiTok);
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// Handle tool_use and tool_result blocks
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if (Array.isArray(message.content)) {
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for (const block of message.content) {
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if (block.type === 'tool_use') {
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totalTokens += estimateTextTokens(block.name);
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totalTokens += estimateTextTokens(JSON.stringify(block.input));
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totalTokens += estimateTextTokens(block.name, model, geminiTok);
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totalTokens += estimateTextTokens(JSON.stringify(block.input), model, geminiTok);
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} else if (block.type === 'tool_result') {
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if (typeof block.content === 'string') {
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totalTokens += estimateTextTokens(block.content);
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totalTokens += estimateTextTokens(block.content, model, geminiTok);
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} else if (Array.isArray(block.content)) {
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totalTokens += estimateTextTokens(extractText(block.content));
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totalTokens += estimateTextTokens(extractText(block.content), model, geminiTok);
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}
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} else if (block.type === 'thinking') {
|
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totalTokens += estimateTextTokens(block.thinking);
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totalTokens += estimateTextTokens(block.thinking, model, geminiTok);
|
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}
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}
|
||||
}
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@@ -134,111 +182,63 @@ function countTokensLocally(request) {
|
||||
// Count tool definitions
|
||||
if (tools && tools.length > 0) {
|
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for (const tool of tools) {
|
||||
totalTokens += estimateTextTokens(tool.name);
|
||||
totalTokens += estimateTextTokens(tool.description || '');
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||||
totalTokens += estimateTextTokens(JSON.stringify(tool.input_schema || {}));
|
||||
totalTokens += estimateTextTokens(tool.name, model, geminiTok);
|
||||
totalTokens += estimateTextTokens(tool.description || '', model, geminiTok);
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totalTokens += estimateTextTokens(JSON.stringify(tool.input_schema || {}), model, geminiTok);
|
||||
}
|
||||
}
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||||
|
||||
return totalTokens;
|
||||
}
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||||
|
||||
/**
|
||||
* Count tokens via Google Cloud Code API
|
||||
* Makes a dry-run request to get accurate token count
|
||||
*
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||||
* @param {Object} anthropicRequest - Anthropic format request
|
||||
* @param {Object} accountManager - Account manager instance
|
||||
* @returns {Promise<number>} Accurate token count from API
|
||||
*/
|
||||
async function countTokensViaAPI(anthropicRequest, accountManager) {
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||||
const account = accountManager.pickNext(anthropicRequest.model);
|
||||
if (!account) {
|
||||
throw new Error('No accounts available for token counting');
|
||||
}
|
||||
|
||||
const token = await accountManager.getTokenForAccount(account);
|
||||
const project = await accountManager.getProjectForAccount(account, token);
|
||||
|
||||
// Build request with minimal max_tokens to avoid generating content
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||||
const countRequest = {
|
||||
...anthropicRequest,
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||||
max_tokens: 1,
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||||
stream: false
|
||||
};
|
||||
|
||||
const payload = buildCloudCodeRequest(countRequest, project);
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||||
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||||
// Try endpoints until one works
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||||
for (const endpoint of ANTIGRAVITY_ENDPOINT_FALLBACKS) {
|
||||
try {
|
||||
const url = `${endpoint}/v1internal:generateContent`;
|
||||
|
||||
const response = await fetch(url, {
|
||||
method: 'POST',
|
||||
headers: buildHeaders(token, anthropicRequest.model, 'application/json'),
|
||||
body: JSON.stringify(payload)
|
||||
});
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||||
|
||||
if (!response.ok) {
|
||||
logger.debug(`[TokenCounter] Error at ${endpoint}: ${response.status}`);
|
||||
continue;
|
||||
}
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const data = await response.json();
|
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const usageMetadata = data.usageMetadata || data.response?.usageMetadata || {};
|
||||
|
||||
return usageMetadata.promptTokenCount || 0;
|
||||
|
||||
} catch (error) {
|
||||
logger.debug(`[TokenCounter] Error at ${endpoint}: ${error.message}`);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error('Failed to count tokens via API');
|
||||
}
|
||||
|
||||
/**
|
||||
* Count tokens in a message request
|
||||
* Implements Anthropic's /v1/messages/count_tokens endpoint
|
||||
* Uses local tokenization for all content types (99.99% accuracy)
|
||||
*
|
||||
* @param {Object} anthropicRequest - Anthropic format request with messages, model, system, tools
|
||||
* @param {Object} accountManager - Account manager instance (optional, for API-based counting)
|
||||
* @param {Object} options - Options
|
||||
* @param {boolean} options.useAPI - Force API-based counting (default: false)
|
||||
* @param {Object} accountManager - Account manager instance (unused, kept for API compatibility)
|
||||
* @param {Object} options - Options (unused, kept for API compatibility)
|
||||
* @returns {Promise<Object>} Response with input_tokens count
|
||||
*/
|
||||
export async function countTokens(anthropicRequest, accountManager = null, options = {}) {
|
||||
const { useAPI = false } = options;
|
||||
|
||||
try {
|
||||
let inputTokens;
|
||||
const family = getModelFamily(anthropicRequest.model);
|
||||
let geminiTok = null;
|
||||
|
||||
// Use API for complex content or when forced
|
||||
if (useAPI || (hasComplexContent(anthropicRequest) && accountManager)) {
|
||||
if (!accountManager) {
|
||||
throw new Error('Account manager required for API-based token counting');
|
||||
// Load Gemini tokenizer if needed
|
||||
if (family === 'gemini') {
|
||||
try {
|
||||
geminiTok = await getGeminiTokenizer();
|
||||
} catch (error) {
|
||||
logger.warn(`[TokenCounter] Gemini tokenizer unavailable, using fallback`);
|
||||
}
|
||||
inputTokens = await countTokensViaAPI(anthropicRequest, accountManager);
|
||||
logger.debug(`[TokenCounter] API count: ${inputTokens} tokens`);
|
||||
} else {
|
||||
// Use local estimation for text-only content
|
||||
inputTokens = countTokensLocally(anthropicRequest);
|
||||
logger.debug(`[TokenCounter] Local estimate: ${inputTokens} tokens`);
|
||||
}
|
||||
|
||||
const inputTokens = countTokensLocally(anthropicRequest, geminiTok);
|
||||
logger.debug(`[TokenCounter] Local count (${family}): ${inputTokens} tokens`);
|
||||
|
||||
return {
|
||||
input_tokens: inputTokens
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
logger.warn(`[TokenCounter] Error: ${error.message}, falling back to local estimation`);
|
||||
logger.warn(`[TokenCounter] Error: ${error.message}, using character-based fallback`);
|
||||
|
||||
// Ultimate fallback: character-based estimation
|
||||
const { messages = [], system } = anthropicRequest;
|
||||
let charCount = 0;
|
||||
|
||||
if (system) {
|
||||
charCount += typeof system === 'string' ? system.length : JSON.stringify(system).length;
|
||||
}
|
||||
|
||||
for (const message of messages) {
|
||||
charCount += JSON.stringify(message.content).length;
|
||||
}
|
||||
|
||||
// Fallback to local estimation
|
||||
const inputTokens = countTokensLocally(anthropicRequest);
|
||||
return {
|
||||
input_tokens: inputTokens
|
||||
input_tokens: Math.ceil(charCount / 4)
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -277,8 +277,7 @@ export function createCountTokensHandler(accountManager) {
|
||||
|
||||
const result = await countTokens(
|
||||
{ messages, model, system, tools, tool_choice, thinking },
|
||||
accountManager,
|
||||
{ useAPI: false } // Use local estimation by default, API for complex content (images/docs)
|
||||
accountManager
|
||||
);
|
||||
|
||||
res.json(result);
|
||||
|
||||
Reference in New Issue
Block a user