242 lines
8.1 KiB
JavaScript
242 lines
8.1 KiB
JavaScript
#!/usr/bin/env node
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// kimi-eyes setup wizard — interactive one-time configuration.
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// Zero-dependency: raw-mode stdin input + native fetch.
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//
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// Flow: choose protocol → base URL → API key → pick multimodal model → verify → write config.
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import fs from 'node:fs';
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import os from 'node:os';
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import path from 'node:path';
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import { callVLM, modelsEndpoint, ONE_PX_PNG_BASE64, modelCapabilityTag } from './mcp/vision.mjs';
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// ---------------------------------------------------------------------------
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// Raw-mode line input (supports masked echo)
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// ---------------------------------------------------------------------------
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let pipeLines = null;
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function ensurePipeLines() {
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if (!pipeLines) {
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pipeLines = new Promise((resolve, reject) => {
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let s = '';
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process.stdin.setEncoding('utf8');
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process.stdin.on('data', (c) => (s += c));
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process.stdin.on('end', () => resolve(s.split(/\r?\n/)));
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process.stdin.on('error', reject);
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process.stdin.resume();
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});
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}
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return pipeLines;
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}
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function ask(query, { hidden = false } = {}) {
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const stdin = process.stdin;
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if (!stdin.isTTY) {
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// Non-TTY fallback (piped input in tests/CI): plain line read, no masking.
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process.stdout.write(query);
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return ensurePipeLines().then((lines) => lines.shift() ?? '');
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}
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process.stdout.write(query);
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stdin.setRawMode(true);
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stdin.resume();
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return new Promise((resolve) => {
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let val = '';
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let esc = false;
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const cleanup = () => {
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stdin.setRawMode(false);
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stdin.removeListener('data', onData);
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process.stdout.write('\n');
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};
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const onData = (chunk) => {
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for (const ch of chunk.toString('utf8')) {
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if (esc) {
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if ((ch >= 'A' && ch <= 'D') || ch === '~') esc = false;
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continue;
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}
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if (ch === '\u001b') {
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esc = true;
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continue;
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}
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if (ch === '\r' || ch === '\n') {
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cleanup();
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resolve(val);
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return;
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}
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if (ch === '\u0003') process.exit(130);
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if (ch === '\u007f' || ch === '\b') {
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if (val.length > 0) {
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val = val.slice(0, -1);
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process.stdout.write('\b \b');
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}
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continue;
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}
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val += ch;
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process.stdout.write(hidden ? '*' : ch);
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}
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};
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stdin.on('data', onData);
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});
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}
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async function askRequired(query, { hidden = false, validate = null } = {}) {
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for (;;) {
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const v = (await ask(query, { hidden })).trim();
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if (!v) {
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console.log(' 输入不能为空，请重试。');
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continue;
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}
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if (validate) {
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const err = validate(v);
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if (err) {
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console.log(` ${err}`);
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continue;
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}
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}
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return v;
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}
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}
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// ---------------------------------------------------------------------------
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// Helpers
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// ---------------------------------------------------------------------------
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const VISION_HINTS =
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/(vision|visual|vlm|multimodal|4o|omni|glm-4v|qwen-vl|qwen2.*?vl|internvl|minicpm|gpt-4v|gpt-5|claude-3|claude-sonnet|claude-opus|gemini)/i;
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async function fetchModels(protocol, baseUrl, apiKey) {
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const url = modelsEndpoint(baseUrl, protocol);
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const headers =
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protocol === 'anthropic'
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? { 'x-api-key': apiKey, 'anthropic-version': '2023-06-01' }
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: { Authorization: `Bearer ${apiKey}` };
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const res = await fetch(url, { headers, signal: AbortSignal.timeout(15000) });
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if (!res.ok) throw new Error(`HTTP ${res.status}`);
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const data = await res.json();
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const list = Array.isArray(data?.data)
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? data.data.map((m) => m.id || m.name).filter((x) => typeof x === 'string' && x)
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: [];
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if (list.length === 0) throw new Error('接口未返回任何模型');
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return [...new Set(list)];
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}
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async function chooseProtocol() {
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console.log(' 选择视觉 API 协议：');
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console.log(' [1] OpenAI 兼容 (Chat Completions)');
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console.log(' [2] Anthropic (Messages API)');
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for (;;) {
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const v = (await ask(' 输入序号 [1/2]: ')).trim();
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if (v === '1') return 'openai';
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if (v === '2') return 'anthropic';
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console.log(' 无效输入，请输入 1 或 2。');
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}
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}
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async function chooseModel(protocol, baseUrl, apiKey) {
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console.log(' 拉取模型列表 ...');
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let models = null;
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try {
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models = await fetchModels(protocol, baseUrl, apiKey);
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} catch (err) {
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console.log(` 拉取模型列表失败 (${err.message})，将改为手动输入模型名。`);
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}
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if (models) {
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const shown = models.slice(0, 50);
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console.log(
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` 可用模型 (✓视觉=支持识图 / 文本=纯文本 / ★疑似=未收录按关键词猜测): ` +
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`${models.length > 50 ? `显示前 50 / 共 ${models.length}` : ''}`
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);
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shown.forEach((m, i) => {
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const tag = modelCapabilityTag(m, VISION_HINTS);
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console.log(` [${String(i + 1).padStart(3)}] ${m}${tag ? ` ${tag}` : ''}`);
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});
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console.log(' 输入序号选择模型，或直接输入自定义模型名。');
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for (;;) {
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const v = (await ask(' 模型: ')).trim();
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if (v === '') {
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console.log(' 输入不能为空。');
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continue;
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}
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const idx = Number.parseInt(v, 10);
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if (!Number.isNaN(idx) && idx >= 1 && idx <= shown.length) return shown[idx - 1];
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return v; // treat as custom model name
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}
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}
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return askRequired(' 模型名: ');
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}
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async function verifyModel(cfg) {
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console.log(' 视觉验证：发送 1×1 测试图到模型 ...');
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try {
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const text = await callVLM(
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{ imageBase64: ONE_PX_PNG_BASE64, mediaType: 'image/png', prompt: 'Reply with the single word OK.' },
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cfg
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);
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console.log(` ✓ 通过，模型可接收图片 (返回: ${text.slice(0, 60)})`);
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return true;
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} catch (err) {
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console.log(` ✗ 失败: ${err.message.slice(0, 200)}`);
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return false;
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}
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}
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// ---------------------------------------------------------------------------
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// Main
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// ---------------------------------------------------------------------------
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async function main() {
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const home = process.env.KIMI_CODE_HOME || path.join(os.homedir(), '.kimi-code');
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const configPath = path.join(home, 'kimi-eyes', 'config.json');
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console.log('=== Kimi Eyes 配置向导 ===');
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console.log(`配置将写入: ${configPath}\n`);
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const protocol = await chooseProtocol();
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const baseUrl = await askRequired(' 视觉 API BaseUrl (如 https://api.openai.com/v1): ', {
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validate: (v) => (/^https?:\/\//i.test(v) ? null : 'BaseUrl 必须以 http:// 或 https:// 开头。'),
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});
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const apiKey = await askRequired(' API Key: ', { hidden: true });
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const model = await chooseModel(protocol, baseUrl, apiKey);
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const cfg = { protocol, baseUrl, apiKey, model };
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console.log('');
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for (let attempt = 0; attempt < 3; attempt++) {
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if (await verifyModel(cfg)) break;
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if (attempt === 2) {
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console.log(' 连续验证失败，跳过验证（配置仍将写入）。');
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break;
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}
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const act = (await ask(' [r] 重试 [c] 换模型 [s] 跳过验证: ')).trim().toLowerCase();
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if (act === 'c') {
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cfg.model = await chooseModel(protocol, baseUrl, apiKey);
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continue;
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}
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if (act === 's') break;
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// 'r' or anything else → retry the loop
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}
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const mainModel = (await ask(' 当前主模型名（可选，用于触发判断；留空跳过）: ')).trim();
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if (mainModel) cfg.mainModel = mainModel;
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const dir = path.dirname(configPath);
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fs.mkdirSync(dir, { recursive: true });
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fs.writeFileSync(configPath, `${JSON.stringify(cfg, null, 2)}\n`, 'utf8');
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if (process.platform !== 'win32') fs.chmodSync(configPath, 0o600);
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console.log('');
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console.log(`✓ 配置已写入: ${configPath}`);
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console.log(` protocol: ${cfg.protocol}`);
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console.log(` baseUrl : ${cfg.baseUrl}`);
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console.log(` model : ${cfg.model}`);
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if (cfg.mainModel) console.log(` mainModel: ${cfg.mainModel} (触发判断用)`);
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console.log('');
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console.log('下一步：');
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console.log(` 1. 安装插件: /plugins install ${process.cwd()}`);
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console.log(' 2. 启用插件: /reload');
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console.log(' 3. 开始使用: 非多模态模型下 @图片路径 或 截图后提问');
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}
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main().catch((err) => {
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console.error(`\n[setup] 出错: ${err.message}`);
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process.exit(1);
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});
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