Hoan/GenericAgent
1
0 Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

refactor(configure): 重构配置向导 — 16 家厂商、启动流程优化、粘贴修复、新增微信平台 (#364)

Browse Source 
* feat: add cross-platform GA command entry system

* fix: hide code runner console window

* fix(tgapp): preserve streaming overflow segments (#330)

Keep Telegram streaming overflow messages tied to the primary live message, reuse them across edits, and delete stale overflow replies when streamed content shrinks.

* refactor: rename command/ to ga_cli/ to avoid namespace collision

* feat(tgapp): add /btw command handling (#335)

* fix(launch): skip paste interception when clipboard contains text

PPT/Word copies include both image/png and text/plain items.
Only intercept paste when no text item is present (pure screenshot/image).

* docs: update WeChat group 17 QR code (#338)

* feat(tui): multi-line input with Enter submit, Ctrl+Enter newline, paste-safe

* feat(reflect): add init() for runtime args with fallback to env/config

* feat(tui): add tuiapp_v2.py — refined Textual frontend (#350)

新版 TUI 与 tuiapp.py 并存，共享 GenericAgent 调度/队列/线程协议，
重做视觉与交互：
- 多行 InputArea：≤3 行自适应、>3 行三行滑动窗口、粘贴占位符
- 命令面板（OptionList + 模糊匹配 + Tab 补全）
- /llm、command 选择走 ChoiceList，选完区域收缩
- Markdown HardBreak 渲染 + ANSI 缓存
- 响应式布局（窄屏隐藏侧栏 / 宽屏按比例放大）
- selection-aware 自动滚动（选文本时不抢滚动）
- 新增 /btw、/continue、/export、/restore

不改 cli / hub / README / tuiapp.py 入口。

* docs: add Evaluation — Five Dimensions section (EN & CN) (#339)

* feat: dynamic tool_result maxlen based on parallel tool count

* feat(configure): major rewrite — 16 providers, new startup flow, paste fix

- Expand model providers from 12 to 16 (add Qwen, Stepfun, Tencent,
  Volcengine, Baidu Qianfan, Xiaomi MiMo, Zhipu Coding Plan, GMI)
- Merge duplicate entries (MiniMax/Kimi/Qwen/CRS) with protocol choice
- Add WeChat to messaging platforms (frontends/wechatapp.py existed but
  was not discoverable via configure.py)
- Add coding plan / token plan endpoint selection for applicable providers
- Rework startup flow: no mykey.py → suggest both; has mykey.py → modify/new
- Fix paste race condition in masked_input (batch read + deferred repaint)
- Fix _configure_advanced using provider['type'] instead of cfg['type']
- Fix backup filename containing '/' (invalid on Windows paths)
- Simplify Python version check to recommend 3.12 only
- Add manual model name input option regardless of probe result
- Remove dead code (_read_char, import time)

---------

Co-authored-by: Liang Jiaqing <l.j.q.light@gmail.com>
Co-authored-by: manaskarra <106862611+manaskarra@users.noreply.github.com>
Co-authored-by: YooooEX <me@yooooex.com>
Co-authored-by: AspasZhang <24210980075@m.fudan.edu.cn>
Co-authored-by: nianyucatfish <156216359+nianyucatfish@users.noreply.github.com>
Co-authored-by: Jinyi Han <15224975562@163.com>
This commit is contained in:
WindCatLiang JiaqingmanaskarraYooooEXAspasZhangnianyucatfishJinyi Han
 authored and GitHub committed 2026-05-13 23:10:28 +08:00
1 parent 508d5692c5
commit a60c938e89
1 file changed
+494 -209
Download Patch File Download Diff File Expand all files Collapse all files
assets/configure_mykey.py
+494 -209
View File
@@ -9,10 +9,10 @@ GenericAgent — 交互式初始化向导 (configure.py)
import os
import sys
import re
import shutil
import json
import urllib.request
import time
from datetime import datetime
# ── ANSI 颜色 ──────────────────────────────────────────────────────────────
@@ -28,37 +28,11 @@ MYKPY_PATH = os.path.join(PROJECT_ROOT, 'mykey.py')
# ── 模型厂商定义 ───────────────────────────────────────────────────────────
LLM_PROVIDERS = [
{
'id': 'deepseek',
'name': 'DeepSeek V4 Flash (推荐首选)',
'desc': '国产开源模型，速度快、性价比高，原生 OAI 协议',
'type': 'native_oai',
'template': {
'name': 'deepseek-flash', 'apikey': 'sk-<your-deepseek-key>',
'apibase': 'https://api.deepseek.com', 'model': 'deepseek-v4-flash',
'api_mode': 'chat_completions', 'reasoning_effort': 'high',
},
'key_hint': '在 https://platform.deepseek.com/api_keys 获取',
'model_choices': ['deepseek-v4-flash', 'deepseek-v3-premium'],
},
{
'id': 'openai',
'name': 'OpenAI GPT-5 / o 系列',
'desc': 'OpenAI 官方，支持 GPT-5、o 系列推理模型',
'type': 'native_oai',
'template': {
'name': 'gpt-native', 'apikey': 'sk-<your-openai-key>',
'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.4',
'api_mode': 'chat_completions', 'reasoning_effort': 'high',
'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,
},
'key_hint': '在 https://platform.openai.com/api-keys 获取',
'model_choices': ['gpt-5.4', 'o4-mini-high', 'o4-mini'],
},
# ═══════════════════════════ 直连 API（按旗舰能力降序）═══════════════════════════
{
'id': 'anthropic',
'name': 'Anthropic Claude 官方直连',
'desc': 'Claude 官方 API，sk-ant- 开头，原生 tool 协议',
'desc': 'Claude Opus #1 最强模型，原生 tool 协议，sk-ant- 开头',
'type': 'native_claude',
'template': {
'name': 'anthropic-direct', 'apikey': 'sk-ant-<your-anthropic-key>',
@@ -68,10 +42,234 @@ LLM_PROVIDERS = [
'key_hint': '在 https://console.anthropic.com/ 获取',
'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],
},
{
'id': 'openai',
'name': 'OpenAI GPT-5.5 / o 系列',
'desc': 'GPT-5.5 旗舰，与 Claude Opus 同级',
'type': 'native_oai',
'template': {
'name': 'gpt-native', 'apikey': 'sk-<your-openai-key>',
'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',
'api_mode': 'chat_completions', 'reasoning_effort': 'high',
'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,
},
'key_hint': '在 https://platform.openai.com/api-keys 获取',
'model_choices': ['gpt-5.5', 'gpt-5.4'],
},
{
'id': 'deepseek',
'name': 'DeepSeek (v4-Pro / Flash)',
'desc': '最强开源模型，v4-Pro 旗舰 1M 上下文',
'type': 'native_oai',
'template': {
'name': 'deepseek', 'apikey': 'sk-<your-deepseek-key>',
'apibase': 'https://api.deepseek.com', 'model': 'deepseek-v4-flash',
'api_mode': 'chat_completions', 'reasoning_effort': 'high',
},
'key_hint': '在 https://platform.deepseek.com/api_keys 获取',
'model_choices': ['deepseek-v4-pro', 'deepseek-v4-flash'],
},
{
'id': 'kimi',
'name': 'Kimi (k2.6 / k2.5) 双协议',
'desc': '月之暗面，国内顶尖，支持 Anthropic 和 OAI 双协议',
'type': 'native_claude',
'template': {
'name': 'kimi', 'apikey': 'sk-kimi-<your-key>',
'apibase': 'https://api.kimi.com/coding',
'model': 'kimi-for-coding', 'fake_cc_system_prompt': True,
'thinking_type': 'adaptive',
},
'key_hint': '在 https://kimi.com/code 或 https://platform.moonshot.cn/ 获取',
'model_choices': ['kimi-k2.6', 'kimi-k2.5'],
'extra_fields': [
{
'key': '_protocol', 'label': '选择 API 协议',
'type': 'choice',
'options': [
{'id': 'native_claude', 'name': 'Anthropic 兼容 (推荐)', 'desc': 'kimi-for-coding 端点，CC 兼容', 'apibase': 'https://api.kimi.com/coding', 'fake_cc_system_prompt': True, 'model': 'kimi-for-coding'},
{'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': 'Moonshot OAI 端点，kimi-k2 系列', 'apibase': 'https://api.moonshot.cn/v1', 'model': 'kimi-k2.6'},
],
},
],
},
{
'id': 'qwen',
'name': '阿里通义千问 (Qwen3.5 / 百炼)',
'desc': '阿里云百炼，Qwen3 系列百万级上下文',
'type': 'native_oai',
'template': {
'name': 'qwen', 'apikey': 'sk-<your-dashscope-key>',
'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
'model': 'qwen3.5-plus',
'api_mode': 'chat_completions',
},
'key_hint': '在 https://bailian.console.aliyun.com/ 获取 API Key',
'model_choices': ['qwen3.5-plus', 'qwen3-coder-plus'],
'extra_fields': [
{
'key': '_endpoint', 'label': '选择端点',
'type': 'choice',
'options': [
{'id': 'standard', 'name': '标准按量付费', 'desc': 'dashscope.aliyuncs.com，兼容模式', 'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1'},
{'id': 'coding_plan', 'name': '百炼 Coding Plan (订阅)', 'desc': 'coding-intl.dashscope.aliyuncs.com，100万上下文', 'apibase': 'https://coding-intl.dashscope.aliyuncs.com/v1', 'context_win': 1000000},
],
},
],
},
{
'id': 'zhipu',
'name': '智谱 GLM-5.1 (Coding Plan)',
'desc': '智谱 GLM，支持 Coding Plan CN (Anthropic) 和 Global (OAI) 双端点',
'type': 'native_claude',
'template': {
'name': 'zhipu-glm', 'apikey': 'sk-<your-zhipu-key>',
'apibase': 'https://open.bigmodel.cn/api/anthropic',
'model': 'GLM-5.1-Cloud', 'fake_cc_system_prompt': False,
'thinking_type': 'adaptive', 'max_retries': 3,
'connect_timeout': 10, 'read_timeout': 180,
},
'key_hint': 'CN 在 https://open.bigmodel.cn/ 获取；Global 在 https://z.ai/ 获取',
'model_choices': ['GLM-5.1-Cloud', 'glm-4.7'],
'extra_fields': [
{
'key': '_plan', 'label': '选择 Coding Plan',
'type': 'choice',
'options': [
{'id': 'native_claude', 'name': 'Coding Plan CN (Anthropic)', 'desc': 'open.bigmodel.cn，推荐国内用户', 'apibase': 'https://open.bigmodel.cn/api/anthropic', 'fake_cc_system_prompt': False},
{'id': 'native_oai', 'name': 'Coding Plan Global (OAI)', 'desc': 'api.z.ai，OpenAI 协议，全球可用', 'apibase': 'https://api.z.ai/api/paas/v4'},
],
},
],
},
{
'id': 'minimax',
'name': 'MiniMax M2.7 (双协议)',
'desc': 'MiniMax M2.7，支持 Anthropic 和 OpenAI 双协议',
'type': 'native_claude',
'template': {
'name': 'minimax', 'apikey': 'eyJh...<your-minimax-key>',
'apibase': 'https://api.minimaxi.com/anthropic',
'model': 'MiniMax-M2.7', 'max_retries': 3,
},
'key_hint': '在 https://platform.minimaxi.com/user-center/basic-information 获取',
'model_choices': ['MiniMax-M2.7', 'MiniMax-M2.5'],
'extra_fields': [
{
'key': '_protocol', 'label': '选择 API 协议',
'type': 'choice',
'options': [
{'id': 'native_claude', 'name': 'Anthropic 协议 (推荐)', 'desc': '无 <think> 标签，原生 Claude 兼容', 'apibase': 'https://api.minimaxi.com/anthropic'},
{'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': '走 /v1/chat/completions', 'apibase': 'https://api.minimaxi.com/v1', 'context_win': 50000},
],
},
],
},
{
'id': 'stepfun',
'name': '阶跃星辰 Step-3.5 (推理强)',
'desc': '阶跃星辰 Step 系列，支持标准和 Step Plan 双端点',
'type': 'native_oai',
'template': {
'name': 'stepfun', 'apikey': 'sk-<your-stepfun-key>',
'apibase': 'https://api.stepfun.com/v1',
'model': 'step-3.5-flash',
'api_mode': 'chat_completions',
'context_win': 262144,
},
'key_hint': '在 https://platform.stepfun.com/ 获取 API Key',
'model_choices': ['step-3.5-flash', 'step-3.5-flash-2603'],
'extra_fields': [
{
'key': '_endpoint', 'label': '选择端点',
'type': 'choice',
'options': [
{'id': 'standard', 'name': '标准端点', 'desc': 'api.stepfun.com/v1，按量付费', 'apibase': 'https://api.stepfun.com/v1', 'context_win': 262144},
{'id': 'step_plan', 'name': 'Step Plan (订阅)', 'desc': 'api.stepfun.com/step_plan/v1，订阅制', 'apibase': 'https://api.stepfun.com/step_plan/v1', 'context_win': 262144},
],
},
],
},
{
'id': 'qianfan',
'name': '百度千帆 (ERNIE 5.0 / 第三方)',
'desc': '百度智能云千帆，文心一言 ERNIE 5.0 + DeepSeek 等',
'type': 'native_oai',
'template': {
'name': 'baidu-qianfan', 'apikey': '<your-qianfan-key>',
'apibase': 'https://qianfan.baidubce.com/v2',
'model': 'ernie-5.0-thinking-preview',
'api_mode': 'chat_completions',
},
'key_hint': '在 https://console.bce.baidu.com/qianfan/ 创建应用获取 API Key',
'model_choices': ['ernie-5.0-thinking-preview', 'deepseek-v3.2'],
'extra_fields': [
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://qianfan.baidubce.com/v2'},
],
},
{
'id': 'volcengine',
'name': '火山引擎 (豆包 / Ark)',
'desc': '字节跳动火山引擎，支持标准 Ark 和 Ark Coding Plan',
'type': 'native_oai',
'template': {
'name': 'volc-ark', 'apikey': '<your-ark-api-key>',
'apibase': 'https://ark.cn-beijing.volces.com/api/v3',
'model': 'doubao-seed-code-preview-251028',
'api_mode': 'chat_completions',
},
'key_hint': '在 https://console.volcengine.com/ark/ 创建推理接入点后获取 API Key',
'model_choices': ['doubao-seed-code-preview-251028', 'doubao-seed-1-8-251228'],
'extra_fields': [
{
'key': '_endpoint', 'label': '选择端点',
'type': 'choice',
'options': [
{'id': 'standard', 'name': '标准 Ark', 'desc': 'ark.cn-beijing.volces.com/api/v3，按量付费', 'apibase': 'https://ark.cn-beijing.volces.com/api/v3'},
{'id': 'coding_plan', 'name': 'Ark Coding Plan (订阅)', 'desc': 'ark.cn-beijing.volces.com/api/coding/v3', 'apibase': 'https://ark.cn-beijing.volces.com/api/coding/v3'},
],
},
],
},
{
'id': 'xiaomi',
'name': '小米 MiMo (MiMo 2.5 Pro / TokenPlan)',
'desc': '小米 MiMo 系列，超大上下文窗口，支持 TokenPlan 预付费',
'type': 'native_oai',
'template': {
'name': 'xiaomi-mimo', 'apikey': 'sk-<your-xiaomi-key>',
'apibase': 'https://api.xiaomimimo.com/v1',
'model': 'mimo-v2.5-pro',
'api_mode': 'chat_completions',
},
'key_hint': '在 https://x.xiaomi.com/ 获取 API Key',
'model_choices': ['mimo-v2.5-pro', 'mimo-v2-flash'],
'extra_fields': [
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.xiaomimimo.com/v1'},
],
},
{
'id': 'tencent_tokenhub',
'name': '腾讯混元 TokenHub (Hy3 / TokenPlan)',
'desc': '腾讯云 TokenHub，混元 Hy3 系列，TokenPlan 预付费',
'type': 'native_oai',
'template': {
'name': 'tencent-tokenhub', 'apikey': 'sk-<your-tokenhub-key>',
'apibase': 'https://tokenhub.tencentmaas.com/v1',
'model': 'hy3-preview',
'api_mode': 'chat_completions',
},
'key_hint': '在 https://console.cloud.tencent.com/tokenhub 获取 API Key',
'model_choices': ['hy3-preview'],
'extra_fields': [
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://tokenhub.tencentmaas.com/v1'},
],
},
# ═══════════════════════════ 代理 / 中继（支持 Claude/GPT 等顶级模型）══════════
{
'id': 'cc_relay',
'name': 'CC Switch 透传 (社区常用)',
'desc': '社区 Claude Code 透传渠道，需要 fake_cc_system_prompt=True',
'desc': '社区 Claude Code 透传渠道，可接入 Claude Opus',
'type': 'native_claude',
'template': {
'name': 'cc-relay', 'apikey': 'sk-user-<your-relay-key>',
@@ -86,77 +284,10 @@ LLM_PROVIDERS = [
{'key': 'fake_cc_system_prompt', 'label': 'fake_cc_system_prompt', 'type': 'bool', 'default': True},
],
},
{
'id': 'zhipu',
'name': '智谱 GLM (Anthropic 兼容)',
'desc': '智谱 GLM-5.1，走 Anthropic 兼容协议',
'type': 'native_claude',
'template': {
'name': 'zhipu-glm', 'apikey': 'sk-<your-zhipu-key>',
'apibase': 'https://open.bigmodel.cn/api/anthropic',
'model': 'GLM-5.1-Cloud', 'fake_cc_system_prompt': False,
'thinking_type': 'adaptive', 'max_retries': 3,
'connect_timeout': 10, 'read_timeout': 180,
},
'key_hint': '在 https://open.bigmodel.cn/usercenter/apikeys 获取',
'model_choices': ['GLM-5.1-Cloud', 'GLM-5.1-Edge'],
},
{
'id': 'minimax',
'name': 'MiniMax (推荐 Anthropic 路径)',
'desc': 'MiniMax M2.7，Anthropic 路径无 <think> 标签',
'type': 'native_claude',
'template': {
'name': 'minimax-anthropic', 'apikey': 'eyJh...<your-minimax-key>',
'apibase': 'https://api.minimaxi.com/anthropic',
'model': 'MiniMax-M2.7', 'max_retries': 3,
},
'key_hint': '在 https://platform.minimaxi.com/user-center/basic-information 获取',
'model_choices': ['MiniMax-M2.7', 'MiniMax-M2.5'],
},
{
'id': 'minimax_oai',
'name': 'MiniMax (OpenAI 兼容路径)',
'desc': 'MiniMax M2.7，走 /v1/chat/completions',
'type': 'native_oai',
'template': {
'name': 'minimax-oai', 'apikey': 'eyJh...<your-minimax-key>',
'apibase': 'https://api.minimaxi.com/v1', 'model': 'MiniMax-M2.7',
'context_win': 50000,
},
'key_hint': '在 https://platform.minimaxi.com/user-center/basic-information 获取',
'model_choices': ['MiniMax-M2.7', 'MiniMax-M2.5'],
},
{
'id': 'kimi',
'name': 'Kimi for Coding (Anthropic 兼容)',
'desc': 'Kimi 官方 CC 兼容端点，kimi-for-coding 模型',
'type': 'native_claude',
'template': {
'name': 'kimi-coding', 'apikey': 'sk-kimi-<your-key>',
'apibase': 'https://api.kimi.com/coding',
'model': 'kimi-for-coding', 'fake_cc_system_prompt': True,
'thinking_type': 'adaptive',
},
'key_hint': '在 https://kimi.com/code 获取 API Key',
'model_choices': ['kimi-for-coding', 'kimi-thinking-plus'],
},
{
'id': 'moonshot_oai',
'name': 'Kimi / Moonshot (OAI 兼容)',
'desc': 'Moonshot OAI 端点，kimi-k2 系列，温度强制 1.0',
'type': 'native_oai',
'template': {
'name': 'kimi-k2', 'apikey': 'sk-<your-moonshot-key>',
'apibase': 'https://api.moonshot.cn/v1', 'model': 'kimi-k2-turbo-preview',
},
'key_hint': '在 https://platform.moonshot.cn/ 获取',
'model_choices': ['kimi-k2-turbo-preview', 'kimi-k2'],
},
{
'id': 'openrouter',
'name': 'OpenRouter (多模型中继)',
'desc': '一个 Key 用所有模型，支持 Claude/GPT/Gemini 等',
'desc': '一个 Key 通吃 Claude/GPT/DeepSeek/Qwen 等',
'type': 'native_oai',
'template': {
'name': 'openrouter', 'apikey': 'sk-or-<your-openrouter-key>',
@@ -165,15 +296,15 @@ LLM_PROVIDERS = [
'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,
},
'key_hint': '在 https://openrouter.ai/keys 获取',
'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.4'],
'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.5'],
},
{
'id': 'crs',
'name': 'CRS 反代 Claude Max',
'desc': 'CRS 协议的反代 Claude，需要 fake_cc_system_prompt=True',
'name': 'CRS 反代 (Claude Max 多通道)',
'desc': 'CRS 协议的反代服务，支持 Claude Max / Gemini Ultra 通道',
'type': 'native_claude',
'template': {
'name': 'crs-claude-max', 'apikey': 'cr_<your-crs-key>',
'name': 'crs', 'apikey': 'cr_<your-crs-key>',
'apibase': 'https://<your-crs-host>/api',
'model': 'claude-opus-4-7[1m]', 'fake_cc_system_prompt': True,
'thinking_type': 'adaptive', 'max_tokens': 32768,
@@ -182,24 +313,31 @@ LLM_PROVIDERS = [
'key_hint': '从你的 CRS 服务商获取 key 和 host',
'model_choices': ['claude-opus-4-7[1m]', 'claude-sonnet-4-6'],
'extra_fields': [
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://your-crs-host/api'},
{
'key': '_channel', 'label': '选择 CRS 通道',
'type': 'choice',
'options': [
{'id': 'claude_max', 'name': 'Claude Max (默认)', 'desc': '标准 CRS Claude 通道', 'apibase': 'https://<your-crs-host>/api'},
{'id': 'gemini_ultra', 'name': 'Gemini Ultra (Antigravity)', 'desc': 'CRS 包装的 Google Antigravity，不支持 SSE 流式', 'apibase': 'https://<your-crs-gemini-host>/antigravity/api', 'model': 'claude-opus-4-7-thinking', 'stream': False},
],
},
],
},
{
'id': 'crs_gemini',
'name': 'CRS Gemini Ultra (Antigravity 通道)',
'desc': 'CRS 包装的 Google Antigravity，不支持 SSE 流式，必须 stream=False',
'type': 'native_claude',
'id': 'gmi',
'name': 'GMI Serving (通用模型中继)',
'desc': 'GMI 通用模型推理服务，支持多种开源/闭源（手动输入模型名）',
'type': 'native_oai',
'template': {
'name': 'crs-gemini-ultra', 'apikey': 'cr_<your-crs-gemini-key>',
'apibase': 'https://<your-crs-gemini-host>/antigravity/api',
'model': 'claude-opus-4-7-thinking', 'stream': False,
'max_tokens': 32768, 'max_retries': 3, 'read_timeout': 180,
'name': 'gmi', 'apikey': '<your-gmi-key>',
'apibase': 'https://api.gmi-serving.com/v1',
'model': 'gmi-default',
'api_mode': 'chat_completions',
},
'key_hint': '从你的 CRS 服务商获取 Gemini Ultra key 和 host',
'model_choices': ['claude-opus-4-7-thinking', 'claude-opus-4-7[1m]', 'claude-opus-4-7'],
'key_hint': '从 GMI 服务商获取 API Key，探测失败时手动输入模型名',
'model_choices': [], # 中继服务，模型由服务商提供，探测失败时手动输入
'extra_fields': [
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://your-crs-gemini-host/antigravity/api'},
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.gmi-serving.com/v1'},
],
},
]
@@ -282,25 +420,17 @@ PLATFORMS = [
{'key': 'dc_allowed_users', 'label': '允许的用户 ID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},
],
},
{
'id': 'wechat',
'name': '微信 (iLink 协议)',
'desc': '通过微信个人号与 Agent 对话，扫码自动登录',
'file': 'frontends/wechatapp.py',
'deps': ['requests', 'qrcode', 'pycryptodome'],
'env_vars': [],
},
]
def _read_char():
"""跨平台读取单个字符（Windows 用 getwch 避免 CRLF 拆字节问题）。"""
if os.name == 'nt':
import msvcrt
return msvcrt.getwch()
else:
import tty
import termios
fd = sys.stdin.fileno()
old = termios.tcgetattr(fd)
try:
tty.setraw(fd)
return sys.stdin.read(1)
finally:
termios.tcsetattr(fd, termios.TCSADRAIN, old)
def _masked(v, reveal, tail):
"""生成脱敏字符串：前 reveal 位明文 + * + 后 tail 位明文"""
if len(v) > reveal + tail:
@@ -310,7 +440,7 @@ def _masked(v, reveal, tail):
return v
def masked_input(prompt, reveal=6, tail=4):
"""密文输入：每输入一个字符实时显示脱敏版本，支持逐字输入和粘贴。
"""密文输入，支持粘贴：批读取 + 延迟重绘，避免快速键入时丢字符。
prompt 必须为单行（不含 \\n）。
"""
@@ -320,23 +450,71 @@ def masked_input(prompt, reveal=6, tail=4):
def _repaint():
m = _masked(''.join(chars), reveal, tail)
# \r → 行首；写 prompt+m；多余空格覆盖前次更长渲染的残留字符
sys.stdout.write(f'\r{prompt}{m} \r{prompt}{m}')
sys.stdout.flush()
while True:
c = _read_char()
def _process(c):
"""处理单个字符，返回 True 表示应退出。"""
if c in ('\r', '\n'):
break
return True
if c in ('\x03', '\x04'):
raise KeyboardInterrupt
if c in ('\x08', '\x7f'):
if chars:
chars.pop()
_repaint()
elif c.isprintable() or c == ' ':
chars.append(c)
return False
if os.name == 'nt':
import msvcrt
while True:
c = msvcrt.getwch()
if _process(c):
break
if c in ('\x08', '\x7f'):
_repaint() # 退格立即重绘
continue
if not (c.isprintable() or c == ' '):
continue
# 批量读取：粘贴时一次取完
while msvcrt.kbhit():
c2 = msvcrt.getwch()
if _process(c2):
value = ''.join(chars)
_repaint()
sys.stdout.write('\n')
sys.stdout.flush()
return value
_repaint()
else:
import tty, termios, select
fd = sys.stdin.fileno()
old = termios.tcgetattr(fd)
try:
tty.setraw(fd)
while True:
c = sys.stdin.read(1)
if _process(c):
break
if c in ('\x08', '\x7f'):
_repaint() # 退格立即重绘
continue
if not (c.isprintable() or c == ' '):
continue
# 批量读取：只要 stdin 有数据就继续读，不重绘
while select.select([sys.stdin], [], [], 0) == ([sys.stdin], [], []):
c2 = sys.stdin.read(1)
if _process(c2):
value = ''.join(chars)
_repaint()
termios.tcsetattr(fd, termios.TCSADRAIN, old)
sys.stdout.write('\n')
sys.stdout.flush()
return value
_repaint()
finally:
termios.tcsetattr(fd, termios.TCSADRAIN, old)
value = ''.join(chars)
_repaint()
@@ -361,7 +539,7 @@ def banner():
print('\033[2J\033[H', end='') # ANSI 清屏，跨平台
print(f"{C['cyan']}{C['bold']}")
print(" ╔═══════════════════════════════════════════════════════════╗")
print(" ║ GenericAgent — 交互式初始化向导 v1.1 ║")
print(" ║ GenericAgent — 交互式初始化向导 v1.2 ║")
print(" ║ 一键配置 LLM 模型 + 消息平台，自动生成 mykey.py ║")
print(" ╚═══════════════════════════════════════════════════════════╝")
print(f"{C['reset']}")
@@ -373,9 +551,9 @@ def _check_python():
vi = sys.version_info
if vi < (3, 10):
return False, f"Python {vi.major}.{vi.minor} 不满足最低要求 (≥ 3.10)"
if vi >= (3, 14):
return True, f"⚠ Python {vi.major}.{vi.minor} 可能与 pywebview 等依赖不兼容，推荐 3.11/3.12"
return True, f"✓ Python {vi.major}.{vi.minor}.{vi.micro}"
if vi[:2] == (3, 12):
return True, ''
return True, f"⚠ 当前 Python {vi.major}.{vi.minor}，推荐使用 Python 3.12"
def ask_choice(prompt, choices, allow_multi=False, default=None):
"""交互式选择，返回 selected_id 或 [selected_ids]"""
@@ -416,12 +594,10 @@ def ask_choice(prompt, choices, allow_multi=False, default=None):
def ask_input(prompt, default=None, secret=False, hint=None):
"""交互式输入。secret=True 时使用脱敏输入。"""
# 提示信息先打印（不放进 prompt，保证 prompt 单行）
if hint:
cprint(f" {hint}", 'dim')
if default is not None:
cprint(f" [默认: {default}]", 'dim')
# 单行 prompt，\r 能正确回行首
prompt_line = f" {C['yellow']}►{C['reset']} {prompt}: "
while True:
if secret:
@@ -460,15 +636,15 @@ def probe_models(provider, apikey, apibase=None):
base = (apibase or provider['template'].get('apibase', '')).rstrip('/')
if ptype == 'native_claude':
# Anthropic 协议: 尝试 /v1/models (多数中继兼容此路径)
url = f"{base}/v1/models"
headers = {'x-api-key': apikey, 'anthropic-version': '2023-06-01'}
headers = {'x-api-key': apikey, 'anthropic-version': '2023-06-01', 'User-Agent': 'GenericAgent/1.0'}
else:
url = f"{base}/models"
headers = {'Authorization': f'Bearer {apikey}'}
headers = {'Authorization': f'Bearer {apikey}', 'User-Agent': 'GenericAgent/1.0'}
print(f"\n {C['dim']}🔍 正在探测可用模型 ({url})...{C['reset']}", end='', flush=True)
time.sleep(0.3)
print(f"\n {C['dim']}🔍 正在探测可用模型 ({base}/models)...{C['reset']}", end='', flush=True)
if ptype == 'native_claude':
print(f" {C['dim']}(Anthropic 协议，探测可能失败){C['reset']}", end='', flush=True)
opener = urllib.request.build_opener()
ph = _get_proxy_handler()
@@ -476,21 +652,25 @@ def probe_models(provider, apikey, apibase=None):
opener = urllib.request.build_opener(ph)
print(f" {C['dim']}(via proxy){C['reset']}", end='', flush=True)
try:
req = urllib.request.Request(url, headers=headers, method='GET')
with opener.open(req, timeout=8) as resp:
data = json.loads(resp.read().decode())
# 兼容两种响应格式: {data: [{id: ...}]} 与 {object: 'list', data: [...]}
models = data.get('data', [])
ids = sorted(set(m['id'] for m in models if isinstance(m, dict) and m.get('id')))
if ids:
print(f" {C['green']}✓ 发现 {len(ids)} 个模型{C['reset']}")
return ids
print(f" {C['yellow']}⚠ 返回为空{C['reset']}")
for attempt in range(2):
try:
req = urllib.request.Request(url, headers=headers, method='GET')
with opener.open(req, timeout=10) as resp:
data = json.loads(resp.read().decode())
models = data.get('data', [])
ids = sorted(set(m['id'] for m in models if isinstance(m, dict) and m.get('id')))
if ids:
print(f" {C['green']}✓ 发现 {len(ids)} 个模型{C['reset']}")
return ids
print(f" {C['yellow']}⚠ 返回为空{C['reset']}")
return None
except Exception as e:
if attempt == 0 and 'timeout' in type(e).__name__.lower():
print(f" {C['yellow']}⏱ 超时，重试...{C['reset']}", end='', flush=True)
continue
print(f" {C['yellow']}⚠ 探测失败: {type(e).__name__}（将使用预设列表）{C['reset']}")
return None
except Exception as e:
print(f" {C['yellow']}⚠ 探测失败: {type(e).__name__}（将使用预设列表）{C['reset']}")
return None
return None
def _normalize_model_choices(choices):
"""统一 model_choices 格式为 [{'id': str, 'name': str}]"""
@@ -519,7 +699,7 @@ def _configure_advanced(provider, cfg):
tbt = ask_input("thinking_budget_tokens", default='', hint='low≈4096, medium≈10240, high≈32768')
if tbt:
cfg['thinking_budget_tokens'] = int(tbt)
if provider['type'] == 'native_claude':
if cfg.get('type', provider['type']) == 'native_claude':
ua = ask_input("User-Agent 版本号", default='', hint='某些中转按 UA 白名单校验，pin 老版本用')
if ua:
cfg['user_agent'] = ua
@@ -557,27 +737,37 @@ def configure_llm(provider):
field['label'],
default=field.get('default', True)
)
elif field.get('type') == 'choice':
picked = ask_choice(field['label'], field['options'])
chosen = next(o for o in field['options'] if o['id'] == picked)
for opt_key, opt_val in chosen.items():
if opt_key not in ('id', 'name', 'desc'):
cfg[opt_key] = opt_val
# 模型选择
manual_choice = {'id': '__manual__', 'name': '✏️ 手动输入模型名', 'desc': '自定义模型 ID，不依赖探测结果'}
model_list = probe_models(provider, cfg['apikey'], cfg.get('apibase'))
if model_list:
refresh_choice = {'id': '__refresh__', 'name': '🔄 重新探测模型列表'}
choices = [refresh_choice] + [{'id': m, 'name': m} for m in model_list]
refresh_choice = {'id': '__refresh__', 'name': '🔄 重新探测'}
choices = [refresh_choice, manual_choice] + [{'id': m, 'name': m} for m in model_list]
while True:
picked = ask_choice("API 探测到以下可用模型，请选择:", choices)
picked = ask_choice("API 探测到以下可用模型（或手动输入）:", choices)
if picked == '__refresh__':
print(f" {C['dim']}再次探测...{C['reset']}")
model_list = probe_models(provider, cfg['apikey'], cfg.get('apibase'))
if not model_list:
print(f" {C['yellow']}⚠ 再次探测失败，回退到预设列表{C['reset']}")
picked = _fallback_model(provider)
print(f" {C['yellow']}⚠ 再次探测失败{C['reset']}")
picked = _fallback_model(provider, manual_choice)
break
choices = [refresh_choice] + [{'id': m, 'name': m} for m in model_list]
choices = [refresh_choice, manual_choice] + [{'id': m, 'name': m} for m in model_list]
elif picked == '__manual__':
picked = ask_input("请输入模型名", default=cfg.get('model', ''))
break
else:
break
cfg['model'] = picked
else:
cfg['model'] = _fallback_model(provider)
cfg['model'] = _fallback_model(provider, manual_choice)
# 别名
default_name = cfg.get('name', provider['id'])
@@ -591,12 +781,17 @@ def configure_llm(provider):
return cfg
def _fallback_model(provider):
"""使用预设模型列表让用户选择"""
def _fallback_model(provider, manual_choice=None):
"""使用预设模型列表让用户选择，始终提供手动输入选项"""
manual_choice = manual_choice or {'id': '__manual__', 'name': '✏️ 手动输入模型名', 'desc': '自定义模型 ID'}
normalized = _normalize_model_choices(provider.get('model_choices', []))
if normalized:
return ask_choice("选择模型:", normalized)
return ask_input("请输入模型名称", default=provider['template'].get('model', ''))
choices = [manual_choice] + normalized
picked = ask_choice("选择模型（或手动输入）:", choices)
if picked == '__manual__':
return ask_input("请输入模型名", default=provider['template'].get('model', ''))
return picked
return ask_input("请输入模型名", default=provider['template'].get('model', ''))
def configure_llms():
"""配置 LLM 模型"""
@@ -660,16 +855,13 @@ def configure_platforms():
env_vals = {}
# 飞书扫码创建
if pid == 'feishu' and ask_yesno("使用一键扫码创建应用？（推荐）", default=True):
env_vals = _feishu_scan(platform)
# 补充扫码未获取的字段（或扫码失败时全手动填写）
for var in platform['env_vars']:
if var['key'] not in env_vals:
env_vals.update(_manual_platform_var(var))
# 企业微信专属：欢迎消息
if pid == 'wecom' and ask_yesno("设置欢迎消息？", default=False):
env_vals['wecom_welcome_message'] = ask_input("欢迎消息内容", default='你好，我在线上。')
@@ -688,10 +880,10 @@ def _manual_platform_var(var):
def _feishu_scan(platform):
"""飞书一键扫码创建应用，返回 env_vals 或空 dict"""
from io import StringIO
try:
import lark_oapi as lark
import qrcode, threading
from io import StringIO
except ImportError:
print(f"\n {C['yellow']}⚠ lark-oapi 未安装，降级为手动配置{C['reset']}")
return {}
@@ -794,7 +986,6 @@ def generate_mykey(llm_cfgs, platform_configs):
lines.append("")
# 各模型配置
# 同类型多实例时加上数字后缀
type_counts = {}
for cfg in llm_cfgs:
cfg_type = cfg.get('type', 'native_oai')
@@ -807,7 +998,6 @@ def generate_mykey(llm_cfgs, platform_configs):
idx = type_indices.get(cfg_type, 0)
type_indices[cfg_type] = idx + 1
# 同类型只有一个时不加后缀；多个时加数字后缀
if type_counts[cfg_type] > 1:
var_name = f"{var_prefix}_{idx}"
else:
@@ -873,6 +1063,57 @@ def _write_platform_value(lines, key, val):
lines.append(f"{key} = {repr(val)}")
def _parse_existing_mykey():
"""解析已有 mykey.py，返回 (model_names, platform_infos)
llm_cfgs: [{'name': str, 'type': str, ...}] — 模型配置字典列表
platform_infos: [{'id': str, 'vars': [{'key': str, 'val': ...}]}] — 平台信息
解析失败时返回 ([], [])
"""
if not os.path.exists(MYKPY_PATH):
return [], []
with open(MYKPY_PATH, 'r', encoding='utf-8') as f:
content = f.read()
# 解析模型名
model_names = []
m = re.search(r"'llm_nos':\s*\[([^\]]*)\]", content)
if m:
model_names = re.findall(r"'([^']+)'", m.group(1))
# 解析平台变量 → 平台 ID
platform_id_map = {
'tg_bot_token': 'telegram', 'qq_app_id': 'qq',
'fs_app_id': 'feishu', 'wecom_bot_id': 'wecom',
'dingtalk_client_id': 'dingtalk', 'dc_bot_token': 'discord',
}
platform_infos = []
for var_key, pid in platform_id_map.items():
m_var = re.search(rf"^{var_key}\s*=\s*'([^']*)'", content, re.MULTILINE)
if m_var:
platform_infos.append({'id': pid, 'vars': [{'key': var_key, 'val': m_var.group(1)}]})
return model_names, platform_infos
def _backup_with_name(model_names, platform_ids):
"""按 mykey+模型名+机器人名 格式备份旧 mykey.py"""
parts = ['mykey']
for m in model_names[:3]:
parts.append(m.replace('/', '-').replace('\\', '-'))
for pid in platform_ids:
pid_clean = pid.replace('_', '')
if pid_clean not in parts:
parts.append(pid_clean)
safe_name = '_'.join(parts)
if len(safe_name) > 100:
safe_name = safe_name[:100]
backup_path = os.path.join(PROJECT_ROOT, f'{safe_name}.py')
shutil.copy2(MYKPY_PATH, backup_path)
return backup_path
# ═══════════════════════════════════════════════════════════════════════════
# Main
# ═══════════════════════════════════════════════════════════════════════════
@@ -885,46 +1126,90 @@ def main():
if not ok:
print(f" {C['red']}✗ {msg}{C['reset']}")
sys.exit(1)
color = 'yellow' if '⚠' in msg else 'green'
print(f" {C[color]}{msg}{C['reset']}\n")
# 检测已有配置
if os.path.exists(MYKPY_PATH):
print(f" {C['yellow']}⚠ 检测到已有 mykey.py{C['reset']}")
if not ask_yesno("是否重新配置？", default=False):
print(f"\n {C['dim']} 退出。如需重新配置请删除 mykey.py 后重试。{C['reset']}\n")
sys.exit(0)
# ── 顶层菜单 ──
scope = ask_choice(
"你想配置什么？",
[
{'id': 'llm', 'name': 'LLM 模型', 'desc': '选择厂商、填写 API Key、探测模型列表'},
{'id': 'platform', 'name': '消息平台 (Telegram/QQ/飞书等)', 'desc': '配置聊天机器人接入'},
{'id': 'both', 'name': '两项都配置 (推荐)', 'desc': 'LLM + 平台，完整初始化'},
],
default='both',
)
if msg:
color = 'yellow' if '⚠' in msg else 'green'
print(f" {C[color]}{msg}{C['reset']}\n")
# ── 决策流程 ──
llm_cfgs = []
platform_configs = []
platform_deps = set()
is_modify = False
is_new = False
# ── 执行 ──
if os.path.exists(MYKPY_PATH):
model_names, platform_infos = _parse_existing_mykey()
cur_models = ', '.join(model_names) if model_names else '(未知)'
cur_platforms = ', '.join(p['id'] for p in platform_infos) if platform_infos else '(无)'
print(f" {C['dim']} 当前: 模型=[{cur_models}], 平台=[{cur_platforms}]{C['reset']}")
if scope in ('llm', 'both'):
llm_cfgs = _do_llm()
if scope == 'llm':
if ask_yesno("是否继续配置消息平台？", default=True):
mode = ask_choice(
"检测到已有 mykey.py，请选择操作",
[
{'id': 'modify', 'name': '修改现有配置', 'desc': '保留未改部分，只重新配置选定项'},
{'id': 'new', 'name': '新建配置（备份旧文件）', 'desc': '备份为 mykey+模型+平台.py，然后全新配置'},
],
default=None,
)
if mode == 'new':
backup_path = _backup_with_name(model_names, [p['id'] for p in platform_infos])
print(f" {C['green']}✓ 旧配置已备份至:{C['reset']} {C['dim']}{backup}{C['reset']}")
is_new = True
else:
is_modify = True
scope = ask_choice(
"你要修改什么？",
[
{'id': 'both', 'name': '两项都重新配置', 'desc': 'LLM + 平台全部更新'},
{'id': 'llm', 'name': '重新配置 LLM 模型', 'desc': f'当前: {cur_models}'},
{'id': 'platform', 'name': '重新配置消息平台', 'desc': f'当前: {cur_platforms}'},
],
)
if scope in ('llm', 'both'):
llm_cfgs = _do_llm()
if scope in ('platform', 'both'):
platform_configs, platform_deps = configure_platforms()
if scope == 'llm' and platform_infos:
for pi in platform_infos:
p = next((x for x in PLATFORMS if x['id'] == pi['id']), None)
if p:
config_dict = {v['key']: v['val'] for v in pi['vars']}
platform_configs.append({'platform': p, 'config': config_dict})
elif scope == 'platform' and model_names:
print(f"\n {C['yellow']}⚠ 只修改平台时若未提供 LLM 配置将无法使用。{C['reset']}")
cprint(f" 建议两项都重新配置。", 'dim')
if scope == 'both':
platform_configs, platform_deps = configure_platforms()
if not is_modify:
if is_new:
hint = "已备份旧配置，请完成全新设置"
else:
hint = "首次配置，建议同时设置模型和消息平台"
print(f" {C['dim']} {hint}。{C['reset']}")
if scope == 'platform':
platform_configs, platform_deps = configure_platforms()
if ask_yesno("是否继续配置 LLM 模型？", default=True):
scope = ask_choice(
"你想配置什么？",
[
{'id': 'both', 'name': '两项都配置 (推荐)', 'desc': 'LLM 模型 + 消息平台，完整初始化'},
{'id': 'llm', 'name': '仅 LLM 模型', 'desc': '只配置模型，稍后再配平台'},
{'id': 'platform', 'name': '仅消息平台', 'desc': '只配平台，稍后再配模型'},
],
default='both',
)
if scope in ('llm', 'both'):
llm_cfgs = _do_llm()
if scope == 'llm':
if ask_yesno("是否继续配置消息平台？", default=True):
platform_configs, platform_deps = configure_platforms()
if scope == 'both':
platform_configs, platform_deps = configure_platforms()
if scope == 'platform':
platform_configs, platform_deps = configure_platforms()
if ask_yesno("是否继续配置 LLM 模型？", default=True):
llm_cfgs = _do_llm()
# ── 生成 mykey.py ──
if not llm_cfgs and not platform_configs: