lsdefine--genericagent
wehub-resource-sync
1431 行
63 KiB
Python
1431 行
63 KiB
Python
#!/usr/bin/env python3
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"""
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GenericAgent — 交互式初始化向导 (configure.py)
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一键配置 LLM 模型 + 消息平台，自动生成 mykey.py
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用法:
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python configure.py
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"""
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import ast
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import os
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import sys
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import re
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import shutil
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import json
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import urllib.request
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from datetime import datetime
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# ── ANSI 颜色 ──────────────────────────────────────────────────────────────
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C = {
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'reset': '\033[0m', 'bold': '\033[1m', 'dim': '\033[2m',
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'red': '\033[91m', 'green': '\033[92m', 'yellow': '\033[93m',
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'blue': '\033[94m', 'magenta': '\033[95m', 'cyan': '\033[96m', 'white': '\033[97m',
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}
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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MYKPY_PATH = os.path.join(PROJECT_ROOT, 'mykey.py')
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# ── 模型厂商定义 ───────────────────────────────────────────────────────────
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LLM_PROVIDERS = [
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# ═══════════════════════════ 通用协议（官方直连或任意兼容中转）═══════════════════════════
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{
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'id': 'oai_chat',
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'name': 'OpenAI Chat Completions 协议',
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'desc': '官方直连或任意 OAI 兼容中转/网关，自填 apibase（回车=OpenAI 官方）',
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'type': 'native_oai',
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'template': {
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'name': 'gpt-native', 'apikey': 'sk-<your-key>',
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'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',
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'api_mode': 'chat_completions', 'reasoning_effort': 'high',
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'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,
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},
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'key_hint': '官方在 https://platform.openai.com/api-keys 获取；中转站填其提供的 Key',
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'model_choices': ['gpt-5.5', 'gpt-5.4'],
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'extra_fields': [
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{'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.openai.com/v1'},
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],
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},
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{
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'id': 'oai_responses',
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'name': 'OpenAI Responses 协议',
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'desc': 'Responses API（o 系列/GPT-5.5 推荐端点），官方或兼容网关，自填 apibase',
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'type': 'native_oai',
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'template': {
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'name': 'gpt-responses', 'apikey': 'sk-<your-key>',
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'apibase': 'https://api.openai.com/v1', 'model': 'gpt-5.5',
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'api_mode': 'responses', 'reasoning_effort': 'high',
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'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,
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},
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'key_hint': '官方在 https://platform.openai.com/api-keys 获取；中转站填其提供的 Key',
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'model_choices': ['gpt-5.5', 'gpt-5.4'],
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'extra_fields': [
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{'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.openai.com/v1'},
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],
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},
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{
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'id': 'claude_messages',
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'name': 'Claude Messages 协议',
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'desc': 'Anthropic 官方直连或任意 Claude 兼容中转，自填 apibase（回车=官方）',
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'type': 'native_claude',
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'template': {
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'name': 'anthropic-direct', 'apikey': 'sk-ant-<your-key>',
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'apibase': 'https://api.anthropic.com', 'model': 'claude-opus-4-7',
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'thinking_type': 'adaptive', 'max_tokens': 32768, 'temperature': 1,
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},
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'key_hint': '官方在 https://console.anthropic.com/ 获取；中转站填其提供的 Key',
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'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],
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'extra_fields': [
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{'key': 'apibase', 'label': 'API Base（官方或中转地址）', 'default': 'https://api.anthropic.com'},
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],
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},
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# ═══════════════════════════ 直连 API（按旗舰能力降序）═══════════════════════════
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{
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'id': 'deepseek',
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'name': 'DeepSeek (v4-Pro / Flash)',
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'desc': '开源模型，v4-Pro 旗舰 1M 上下文',
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'type': 'native_oai',
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'template': {
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'name': 'deepseek', 'apikey': 'sk-<your-deepseek-key>',
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'apibase': 'https://api.deepseek.com', 'model': 'deepseek-v4-pro',
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'api_mode': 'chat_completions', 'reasoning_effort': 'high',
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},
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'key_hint': '在 https://platform.deepseek.com/api_keys 获取',
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'model_choices': ['deepseek-v4-pro', 'deepseek-v4-flash'],
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},
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{
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'id': 'kimi',
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'name': 'Kimi (k2.6 / k2.5) 双协议',
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'desc': '月之暗面，支持 Anthropic 和 OAI 双协议',
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'type': 'native_claude',
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'template': {
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'name': 'kimi', 'apikey': 'sk-kimi-<your-key>',
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'apibase': 'https://api.kimi.com/coding',
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'model': 'kimi-for-coding', 'fake_cc_system_prompt': True,
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'thinking_type': 'adaptive',
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},
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'key_hint': '在 https://kimi.com/code 或 https://platform.moonshot.cn/ 获取',
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'model_choices': ['kimi-k2.6', 'kimi-k2.5'],
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'extra_fields': [
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{
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'key': '_protocol', 'label': '选择 API 协议',
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'type': 'choice',
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'options': [
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{'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'},
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{'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'},
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],
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},
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],
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},
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{
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'id': 'qwen',
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'name': '阿里通义千问 (Qwen3.5 / 百炼)',
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'desc': '阿里云百炼，Qwen3 系列百万级上下文',
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'type': 'native_oai',
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'template': {
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'name': 'qwen', 'apikey': 'sk-<your-dashscope-key>',
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'apibase': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
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'model': 'qwen3.6-max-preview',
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'api_mode': 'chat_completions',
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},
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'key_hint': '在 https://bailian.console.aliyun.com/ 获取 API Key',
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'model_choices': ['qwen3.6-max-preview', 'qwen3.5-plus', 'qwen3-coder-plus'],
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'extra_fields': [
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{
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'key': '_endpoint', 'label': '选择端点',
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'type': 'choice',
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'options': [
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{'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'},
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{'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},
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],
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},
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],
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},
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{
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'id': 'zhipu',
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'name': '智谱 GLM-5.1 (Coding Plan)',
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'desc': '智谱 GLM，支持 Coding Plan CN (Anthropic) 和 Global (OAI) 双端点',
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'type': 'native_claude',
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'template': {
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'name': 'zhipu-glm', 'apikey': 'sk-<your-zhipu-key>',
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'apibase': 'https://open.bigmodel.cn/api/anthropic',
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'model': 'GLM-5.1-Cloud', 'fake_cc_system_prompt': False,
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'thinking_type': 'adaptive', 'max_retries': 3,
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'connect_timeout': 10, 'read_timeout': 180,
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},
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'key_hint': 'CN 在 https://open.bigmodel.cn/ 获取；Global 在 https://z.ai/ 获取',
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'model_choices': ['GLM-5.1-Cloud', 'glm-4.7'],
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'extra_fields': [
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{
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'key': '_plan', 'label': '选择 Coding Plan',
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'type': 'choice',
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'options': [
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{'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},
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{'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'},
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],
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},
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],
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},
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{
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'id': 'minimax',
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'name': 'MiniMax M3 (双协议)',
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'desc': 'MiniMax M3，支持 Anthropic 和 OpenAI 双协议',
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'type': 'native_claude',
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'template': {
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'name': 'minimax', 'apikey': 'eyJh...<your-minimax-key>',
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'apibase': 'https://api.minimaxi.com/anthropic',
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'model': 'MiniMax-M3', 'max_retries': 3,
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},
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'key_hint': '在 https://platform.minimaxi.com/user-center/basic-information 获取',
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'model_choices': ['MiniMax-M3', 'MiniMax-M2.7', 'MiniMax-M2.7-highspeed'],
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'extra_fields': [
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{
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'key': '_protocol', 'label': '选择 API 协议',
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'type': 'choice',
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'options': [
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{'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'},
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{'id': 'native_oai', 'name': 'OpenAI 协议', 'desc': '走 /v1/chat/completions', 'apibase': 'https://api.minimaxi.com/v1', 'context_win': 50000},
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],
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},
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],
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},
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{
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'id': 'stepfun',
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'name': '阶跃星辰 Step-3.5 (推理强)',
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'desc': '阶跃星辰 Step 系列，支持标准和 Step Plan 双端点',
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'type': 'native_oai',
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'template': {
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'name': 'stepfun', 'apikey': 'sk-<your-stepfun-key>',
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'apibase': 'https://api.stepfun.com/v1',
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'model': 'step-3.5-flash',
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'api_mode': 'chat_completions',
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'context_win': 262144,
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},
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'key_hint': '在 https://platform.stepfun.com/ 获取 API Key',
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'model_choices': ['step-3.5-flash', 'step-3.5-flash-2603'],
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'extra_fields': [
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{
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'key': '_endpoint', 'label': '选择端点',
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'type': 'choice',
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'options': [
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{'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},
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{'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},
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],
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},
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],
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},
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{
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'id': 'qianfan',
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'name': '百度千帆 (ERNIE 5.0 / 第三方)',
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'desc': '百度智能云千帆，文心一言 ERNIE 5.0 + DeepSeek 等',
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'type': 'native_oai',
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'template': {
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'name': 'baidu-qianfan', 'apikey': '<your-qianfan-key>',
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'apibase': 'https://qianfan.baidubce.com/v2',
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'model': 'ernie-5.0-thinking-preview',
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'api_mode': 'chat_completions',
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},
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'key_hint': '在 https://console.bce.baidu.com/qianfan/ 创建应用获取 API Key',
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'model_choices': ['ernie-5.0-thinking-preview', 'deepseek-v3.2'],
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'extra_fields': [
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{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://qianfan.baidubce.com/v2'},
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],
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},
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{
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'id': 'volcengine',
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'name': '火山引擎 (豆包 / Ark)',
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'desc': '字节跳动火山引擎，支持标准 Ark 和 Ark Coding Plan',
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'type': 'native_oai',
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'template': {
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'name': 'volc-ark', 'apikey': '<your-ark-api-key>',
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'apibase': 'https://ark.cn-beijing.volces.com/api/v3',
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'model': 'doubao-seed-code-preview-251028',
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'api_mode': 'chat_completions',
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},
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'key_hint': '在 https://console.volcengine.com/ark/ 创建推理接入点后获取 API Key',
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'model_choices': ['doubao-seed-code-preview-251028', 'doubao-seed-1-8-251228'],
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'extra_fields': [
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{
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'key': '_endpoint', 'label': '选择端点',
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'type': 'choice',
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'options': [
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{'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'},
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{'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'},
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],
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},
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],
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},
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{
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'id': 'xiaomi',
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'name': '小米 MiMo (MiMo 2.5 Pro / TokenPlan)',
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'desc': '小米 MiMo 系列，超大上下文窗口，支持 TokenPlan 预付费',
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'type': 'native_oai',
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'template': {
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'name': 'xiaomi-mimo', 'apikey': 'sk-<your-xiaomi-key>',
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'apibase': 'https://api.xiaomimimo.com/v1',
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'model': 'mimo-v2.5-pro',
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'api_mode': 'chat_completions',
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},
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'key_hint': '在 https://x.xiaomi.com/ 获取 API Key',
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'model_choices': ['mimo-v2.5-pro', 'mimo-v2-flash'],
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'extra_fields': [
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{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://api.xiaomimimo.com/v1'},
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],
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},
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{
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'id': 'tencent_tokenhub',
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'name': '腾讯混元 TokenHub (Hy3 / TokenPlan)',
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'desc': '腾讯云 TokenHub，混元 Hy3 系列，TokenPlan 预付费',
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'type': 'native_oai',
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'template': {
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'name': 'tencent-tokenhub', 'apikey': 'sk-<your-tokenhub-key>',
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'apibase': 'https://tokenhub.tencentmaas.com/v1',
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'model': 'hy3-preview',
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'api_mode': 'chat_completions',
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},
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||
'key_hint': '在 https://console.cloud.tencent.com/tokenhub 获取 API Key',
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||
'model_choices': ['hy3-preview'],
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||
'extra_fields': [
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{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://tokenhub.tencentmaas.com/v1'},
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||
],
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||
},
|
||
# ═══════════════════════════ 代理 / 中继（支持 Claude/GPT 等顶级模型）══════════
|
||
{
|
||
'id': 'cc_relay',
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||
'name': 'CC Switch 透传 (社区常用)',
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||
'desc': '社区 Claude Code 透传渠道，可接入 Claude Opus',
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||
'type': 'native_claude',
|
||
'template': {
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||
'name': 'cc-relay', 'apikey': 'sk-user-<your-relay-key>',
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||
'apibase': 'https://<your-cc-switch-host>/claude/office',
|
||
'model': 'claude-opus-4-7', 'fake_cc_system_prompt': True,
|
||
'thinking_type': 'adaptive',
|
||
},
|
||
'key_hint': '从你的 CC Switch 服务商获取 apikey 和 apibase',
|
||
'model_choices': ['claude-opus-4-7', 'claude-sonnet-4-6'],
|
||
'extra_fields': [
|
||
{'key': 'apibase', 'label': 'API 地址 (apibase)', 'default': 'https://your-host/claude/office'},
|
||
{'key': 'fake_cc_system_prompt', 'label': 'fake_cc_system_prompt', 'type': 'bool', 'default': True},
|
||
],
|
||
},
|
||
{
|
||
'id': 'openrouter',
|
||
'name': 'OpenRouter (多模型中继)',
|
||
'desc': '一个 Key 通吃 Claude/GPT/DeepSeek/Qwen 等',
|
||
'type': 'native_oai',
|
||
'template': {
|
||
'name': 'openrouter', 'apikey': 'sk-or-<your-openrouter-key>',
|
||
'apibase': 'https://openrouter.ai/api/v1',
|
||
'model': 'anthropic/claude-opus-4-7',
|
||
'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-5.5'],
|
||
},
|
||
{
|
||
'id': 'commonstack',
|
||
'name': 'CommonStack (统一网关)',
|
||
'desc': '一个 Key 通吃 Claude/GPT/Gemini/DeepSeek/MiniMax/Zhipu/xAI 等',
|
||
'type': 'native_oai',
|
||
'template': {
|
||
'name': 'commonstack', 'apikey': 'sk-<your-commonstack-key>',
|
||
'apibase': 'https://api.commonstack.ai/v1',
|
||
'model': 'anthropic/claude-opus-4-7',
|
||
'api_mode': 'chat_completions',
|
||
'max_retries': 3, 'connect_timeout': 10, 'read_timeout': 120,
|
||
},
|
||
'key_hint': '在 https://commonstack.ai 注册后从 Dashboard 获取 API Key',
|
||
'model_choices': ['anthropic/claude-opus-4-7', 'openai/gpt-5.5'],
|
||
},
|
||
{
|
||
'id': 'crs',
|
||
'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', '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,
|
||
'max_retries': 3, 'read_timeout': 180,
|
||
},
|
||
'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': '_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': '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': 'gmi', 'apikey': '<your-gmi-key>',
|
||
'apibase': 'https://api.gmi-serving.com/v1',
|
||
'model': 'gmi-default',
|
||
'api_mode': 'chat_completions',
|
||
},
|
||
'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://api.gmi-serving.com/v1'},
|
||
],
|
||
},
|
||
]
|
||
|
||
# ── 消息平台定义 ────────────────────────────────────────────────────────────
|
||
PLATFORMS = [
|
||
{
|
||
'id': 'none',
|
||
'name': '不使用消息平台（纯终端 REPL）',
|
||
'desc': '直接用 python agentmain.py 在终端交互',
|
||
'deps': [],
|
||
},
|
||
{
|
||
'id': 'telegram',
|
||
'name': 'Telegram 机器人',
|
||
'desc': '通过 Telegram Bot 与 Agent 对话',
|
||
'file': 'frontends/tgapp.py',
|
||
'deps': ['python-telegram-bot'],
|
||
'env_vars': [
|
||
{'key': 'tg_bot_token', 'label': 'Bot Token', 'hint': '从 @BotFather 获取'},
|
||
{'key': 'tg_allowed_users', 'label': '允许的用户 ID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},
|
||
],
|
||
},
|
||
{
|
||
'id': 'qq',
|
||
'name': 'QQ 机器人',
|
||
'desc': '通过 QQ 官方机器人 API 接入',
|
||
'file': 'frontends/qqapp.py',
|
||
'deps': ['qq-botpy'],
|
||
'env_vars': [
|
||
{'key': 'qq_app_id', 'label': 'App ID', 'hint': 'QQ 开放平台获取'},
|
||
{'key': 'qq_app_secret', 'label': 'App Secret'},
|
||
{'key': 'qq_allowed_users', 'label': '允许的用户 OpenID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},
|
||
],
|
||
},
|
||
{
|
||
'id': 'feishu',
|
||
'name': '飞书机器人',
|
||
'desc': '通过飞书应用与 Agent 对话',
|
||
'file': 'frontends/fsapp.py',
|
||
'deps': ['lark-oapi'],
|
||
'env_vars': [
|
||
{'key': 'fs_app_id', 'label': 'App ID', 'hint': '飞书开放平台获取'},
|
||
{'key': 'fs_app_secret', 'label': 'App Secret'},
|
||
{'key': 'fs_allowed_users', 'label': '允许的用户（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},
|
||
],
|
||
},
|
||
{
|
||
'id': 'wecom',
|
||
'name': '企业微信机器人',
|
||
'desc': '通过企业微信 Bot 接入',
|
||
'file': 'frontends/wecomapp.py',
|
||
'deps': ['wecombot'],
|
||
'env_vars': [
|
||
{'key': 'wecom_bot_id', 'label': 'Bot ID'},
|
||
{'key': 'wecom_secret', 'label': 'Bot Secret'},
|
||
{'key': 'wecom_allowed_users', 'label': '允许的用户（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},
|
||
],
|
||
},
|
||
{
|
||
'id': 'dingtalk',
|
||
'name': '钉钉机器人',
|
||
'desc': '通过钉钉应用接入',
|
||
'file': 'frontends/dingtalkapp.py',
|
||
'deps': ['dingtalk-sdk'],
|
||
'env_vars': [
|
||
{'key': 'dingtalk_client_id', 'label': 'Client ID (App Key)'},
|
||
{'key': 'dingtalk_client_secret', 'label': 'Client Secret (App Secret)'},
|
||
{'key': 'dingtalk_allowed_users', 'label': '允许的用户 StaffID（逗号分隔, 留空=所有人）', 'default': '[]', 'is_list': True},
|
||
],
|
||
},
|
||
{
|
||
'id': 'discord',
|
||
'name': 'Discord 机器人',
|
||
'desc': '通过 Discord Bot 接入',
|
||
'file': 'frontends/dcapp.py',
|
||
'deps': ['discord.py'],
|
||
'env_vars': [
|
||
{'key': 'dc_bot_token', 'label': 'Bot Token', 'hint': 'Discord Developer Portal 获取'},
|
||
{'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 _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:
|
||
return v[:reveal] + '*' * min(len(v) - reveal - tail, 8) + v[-tail:]
|
||
elif len(v) > reveal:
|
||
return v[:reveal] + '*' * (len(v) - reveal)
|
||
return v
|
||
|
||
def masked_input(prompt, reveal=6, tail=4):
|
||
"""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）。
|
||
"""
|
||
sys.stdout.write(prompt)
|
||
sys.stdout.flush()
|
||
chars = []
|
||
|
||
def _repaint():
|
||
m = _masked(''.join(chars), reveal, tail)
|
||
sys.stdout.write(f'\r{prompt}{m} \r{prompt}{m}')
|
||
sys.stdout.flush()
|
||
|
||
def _process(c):
|
||
"""处理单个字符，返回 True 表示应退出。"""
|
||
if c in ('\r', '\n'):
|
||
return True
|
||
if c in ('\x03', '\x04'):
|
||
raise KeyboardInterrupt
|
||
if c in ('\x08', '\x7f'):
|
||
if chars:
|
||
chars.pop()
|
||
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()
|
||
sys.stdout.write('\n')
|
||
sys.stdout.flush()
|
||
return value
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
# UI Helpers
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def cprint(text, color=None, bold=False, end='\n'):
|
||
parts = []
|
||
if color: parts.append(C.get(color, ''))
|
||
if bold: parts.append(C['bold'])
|
||
parts.append(text)
|
||
parts.append(C['reset'])
|
||
print(''.join(parts), end=end)
|
||
|
||
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 v1.2 ║")
|
||
print(" ║ 一键配置 LLM 模型 + 消息平台，自动生成 mykey.py ║")
|
||
print(" ╚═══════════════════════════════════════════════════════════╝")
|
||
print(f"{C['reset']}")
|
||
print(f"{C['dim']} 项目目录: {PROJECT_ROOT}{C['reset']}")
|
||
print()
|
||
|
||
def _check_python():
|
||
"""检查 Python 版本，返回 (ok, msg)"""
|
||
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[: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]"""
|
||
print(f"\n{C['bold']}{prompt}{C['reset']}")
|
||
if allow_multi:
|
||
print(f"{C['dim']} (可多选，输入序号用逗号分隔，如: 1,3,5；输入 a 全选；回车跳过){C['reset']}")
|
||
else:
|
||
print(f"{C['dim']} (输入序号，如: 1){C['reset']}")
|
||
for i, c in enumerate(choices, 1):
|
||
desc = c.get('desc', '')
|
||
print(f" {C['green']}{i}.{C['reset']} {C['bold']}{c['name']}{C['reset']} {C['dim']}{desc}{C['reset']}")
|
||
while True:
|
||
raw = input(f"\n {C['yellow']}►{C['reset']} ").strip()
|
||
if not raw and default is not None:
|
||
return default
|
||
if allow_multi:
|
||
if raw.lower() == 'a':
|
||
return [c['id'] for c in choices]
|
||
parts = [p.strip() for p in raw.split(',') if p.strip()]
|
||
selected = []
|
||
for p in parts:
|
||
try:
|
||
idx = int(p) - 1
|
||
if 0 <= idx < len(choices):
|
||
selected.append(choices[idx]['id'])
|
||
except ValueError:
|
||
pass
|
||
if selected:
|
||
return selected
|
||
else:
|
||
try:
|
||
idx = int(raw) - 1
|
||
if 0 <= idx < len(choices):
|
||
return choices[idx]['id']
|
||
except ValueError:
|
||
pass
|
||
print(f" {C['red']}✗ 请输入有效序号{C['reset']}")
|
||
|
||
def ask_input(prompt, default=None, secret=False, hint=None):
|
||
"""交互式输入。secret=True 时使用脱敏输入。"""
|
||
if hint:
|
||
cprint(f" {hint}", 'dim')
|
||
if default is not None:
|
||
cprint(f" [默认: {default}]", 'dim')
|
||
prompt_line = f" {C['yellow']}►{C['reset']} {prompt}: "
|
||
while True:
|
||
if secret:
|
||
val = masked_input(prompt_line).strip()
|
||
else:
|
||
val = input(prompt_line).strip()
|
||
if not val and default is not None:
|
||
return default
|
||
if val:
|
||
return val
|
||
cprint("✗ 此项不能为空", 'red')
|
||
|
||
def ask_yesno(prompt, default=True):
|
||
hint = "Y/N"
|
||
raw = input(f"\n {C['yellow']}►{C['reset']} {prompt} ({hint}): ").strip().lower()
|
||
if not raw:
|
||
return default
|
||
return raw.startswith('y')
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
# LLM 配置逻辑
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def _get_proxy_handler():
|
||
"""从环境变量读取代理配置，返回 ProxyHandler 或 None"""
|
||
for var in ('HTTPS_PROXY', 'https_proxy', 'HTTP_PROXY', 'http_proxy'):
|
||
url = os.environ.get(var)
|
||
if url:
|
||
return urllib.request.ProxyHandler({'https': url, 'http': url})
|
||
return None
|
||
|
||
def probe_models(provider, apikey, apibase=None):
|
||
"""调用 API 探测可用模型列表，返回模型 ID 列表或 None"""
|
||
ptype = provider.get('type', 'native_oai')
|
||
base = (apibase or provider['template'].get('apibase', '')).rstrip('/')
|
||
|
||
if ptype == 'native_claude':
|
||
url = f"{base}/v1/models"
|
||
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}', 'User-Agent': 'GenericAgent/1.0'}
|
||
|
||
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()
|
||
if ph:
|
||
opener = urllib.request.build_opener(ph)
|
||
print(f" {C['dim']}(via proxy){C['reset']}", end='', flush=True)
|
||
|
||
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
|
||
return None
|
||
|
||
def _normalize_model_choices(choices):
|
||
"""统一 model_choices 格式为 [{'id': str, 'name': str}]"""
|
||
if not choices:
|
||
return []
|
||
result = []
|
||
for item in choices:
|
||
if isinstance(item, str):
|
||
result.append({'id': item, 'name': item})
|
||
elif isinstance(item, dict):
|
||
result.append(item)
|
||
elif isinstance(item, (tuple, list)) and len(item) >= 1:
|
||
result.append({'id': item[0], 'name': item[1] if len(item) > 1 else item[0]})
|
||
return result
|
||
|
||
def _configure_advanced(provider, cfg):
|
||
"""配置高级可选字段: proxy, context_win, stream, user_agent, thinking_budget_tokens"""
|
||
print(f"\n {C['dim']}── 高级选项（回车跳过，使用默认值）{C['reset']}")
|
||
proxy = ask_input("HTTP 代理地址 (proxy)", default='', hint='如 http://127.0.0.1:2082，留空跳过')
|
||
if proxy:
|
||
cfg['proxy'] = proxy
|
||
cw = ask_input("上下文窗口阈值 (context_win)", default='', hint='NativeClaude 默认 28000，其他默认 24000')
|
||
if cw:
|
||
cfg['context_win'] = int(cw)
|
||
if cfg.get('thinking_type') == 'enabled':
|
||
tbt = ask_input("thinking_budget_tokens", default='', hint='low≈4096, medium≈10240, high≈32768')
|
||
if tbt:
|
||
cfg['thinking_budget_tokens'] = int(tbt)
|
||
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
|
||
stream_default = cfg.get('stream', True)
|
||
if ask_yesno("启用 SSE 流式 (stream)", default=stream_default):
|
||
cfg['stream'] = True
|
||
else:
|
||
cfg['stream'] = False
|
||
|
||
def configure_llm(provider):
|
||
"""引导用户配置单个模型"""
|
||
print(f"\n{C['cyan']}{'─'*60}{C['reset']}")
|
||
print(f"{C['bold']} 配置: {provider['name']}{C['reset']}")
|
||
print(f" {C['dim']}{provider['desc']}{C['reset']}")
|
||
print(f"{C['cyan']}{'─'*60}{C['reset']}")
|
||
|
||
cfg = dict(provider['template'])
|
||
|
||
# API Key（密文输入）
|
||
cfg['apikey'] = ask_input(
|
||
f"API Key",
|
||
hint=provider.get('key_hint', ''),
|
||
secret=True,
|
||
)
|
||
|
||
# 额外字段
|
||
for field in provider.get('extra_fields', []):
|
||
if field['key'] == 'apibase':
|
||
cfg['apibase'] = ask_input(
|
||
field['label'],
|
||
default=field.get('default', cfg.get('apibase', '')),
|
||
)
|
||
elif field.get('type') == 'bool':
|
||
cfg[field['key']] = ask_yesno(
|
||
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'}
|
||
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（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{C['reset']}")
|
||
picked = _fallback_model(provider, manual_choice)
|
||
break
|
||
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, manual_choice)
|
||
|
||
# 别名
|
||
default_name = cfg.get('name', provider['id'])
|
||
name = ask_input("此配置的别名 (name，Mixin 引用用)", default=default_name)
|
||
if name:
|
||
cfg['name'] = name
|
||
|
||
# 高级选项
|
||
if ask_yesno("配置高级选项（proxy / context_win / stream 等）？", default=False):
|
||
_configure_advanced(provider, cfg)
|
||
|
||
return cfg
|
||
|
||
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:
|
||
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"""
|
||
print(f"\n{C['bold']}{C['magenta']}╔══════════════════════════════════════╗")
|
||
print(f"║ 第一步: 配置 LLM 模型 ║")
|
||
print(f"╚══════════════════════════════════════╝{C['reset']}")
|
||
print(f"\n{C['dim']} 你可以配置最多 2 个模型组成故障转移 (Mixin) 列表。{C['reset']}")
|
||
|
||
all_cfgs = []
|
||
provider_id = ask_choice("选择模型厂商 (配置第 1 个模型):", LLM_PROVIDERS)
|
||
provider = next(p for p in LLM_PROVIDERS if p['id'] == provider_id)
|
||
cfg = configure_llm(provider)
|
||
all_cfgs.append(cfg)
|
||
|
||
if ask_yesno("再添加一个模型做故障转移？", default=False):
|
||
providers_ext = [{'id': '__stop__', 'name': '✓ 不需要备选了', 'desc': ''}] + LLM_PROVIDERS
|
||
provider_id = ask_choice(
|
||
"选择模型厂商 (配置第 2 个模型 — 或选「不需要备选了」跳过):",
|
||
providers_ext
|
||
)
|
||
if provider_id != '__stop__':
|
||
provider = next(p for p in LLM_PROVIDERS if p['id'] == provider_id)
|
||
cfg = configure_llm(provider)
|
||
all_cfgs.append(cfg)
|
||
|
||
return all_cfgs
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
# 消息平台配置逻辑
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def configure_platforms():
|
||
"""配置消息平台，返回 (platform_configs, pip_hints)"""
|
||
print(f"\n{C['bold']}{C['magenta']}╔══════════════════════════════════════╗")
|
||
print(f"║ 第二步: 配置消息平台 ║")
|
||
print(f"╚══════════════════════════════════════╝{C['reset']}")
|
||
print(f"\n{C['dim']} 消息平台用于从聊天软件与 Agent 交互。{C['reset']}")
|
||
print(f"{C['dim']} 你也可以跳过此步，直接用终端 REPL。{C['reset']}")
|
||
|
||
platform_ids = ask_choice(
|
||
"选择消息平台 (可多选，选 '不使用' 则跳过):",
|
||
PLATFORMS,
|
||
allow_multi=True,
|
||
default=['none']
|
||
)
|
||
|
||
if 'none' in platform_ids:
|
||
return [], set()
|
||
|
||
selected_platforms = []
|
||
pip_hints = set()
|
||
|
||
for pid in platform_ids:
|
||
platform = next(p for p in PLATFORMS if p['id'] == pid)
|
||
pip_hints.update(platform.get('deps', []))
|
||
|
||
print(f"\n{C['cyan']}{'─'*60}{C['reset']}")
|
||
print(f"{C['bold']} 配置: {platform['name']}{C['reset']}")
|
||
print(f"{C['cyan']}{'─'*60}{C['reset']}")
|
||
|
||
env_vals = {}
|
||
|
||
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)
|
||
if pid == 'wechat' and ask_yesno("扫码登录微信 iLink？（推荐）", default=True):
|
||
env_vals = _wechat_scan()
|
||
|
||
for var in platform['env_vars']:
|
||
if var['key'] not in env_vals:
|
||
env_vals.update(_manual_platform_var(var))
|
||
|
||
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。')
|
||
|
||
selected_platforms.append({'platform': platform, 'config': env_vals})
|
||
|
||
return selected_platforms, pip_hints
|
||
|
||
def _manual_platform_var(var):
|
||
"""手动填写单个平台变量"""
|
||
val = ask_input(var['label'], hint=var.get('hint', ''), default=var.get('default'))
|
||
if var.get('is_list'):
|
||
if val == '[]' or not val:
|
||
return {var['key']: []}
|
||
return {var['key']: [x.strip() for x in val.split(',') if x.strip()]}
|
||
return {var['key']: val}
|
||
|
||
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
|
||
except ImportError:
|
||
print(f"\n {C['yellow']}⚠ lark-oapi 未安装，降级为手动配置{C['reset']}")
|
||
return {}
|
||
|
||
print(f"\n {C['cyan']}📱 正在启动一键创建...{C['reset']}")
|
||
print(f" {C['dim']} 请用飞书 App 扫描终端二维码，完成授权后自动获取凭据。{C['reset']}\n")
|
||
|
||
qr_printed = threading.Event()
|
||
result_holder = {'data': None}
|
||
|
||
def handle_qr(info):
|
||
url = info['url']
|
||
expire = info['expire_in']
|
||
qr = qrcode.QRCode(border=1, box_size=1)
|
||
qr.add_data(url)
|
||
buf = StringIO()
|
||
qr.print_ascii(out=buf)
|
||
qr_art = buf.getvalue()
|
||
print(f"\n {C['bold']}请用飞书扫描下方二维码，或复制链接在浏览器打开:{C['reset']}")
|
||
print(f" {C['green']}{qr_art.replace(chr(27), '')}{C['reset']}")
|
||
print(f" {C['dim']} 链接: {url}{C['reset']}")
|
||
print(f" {C['dim']} 有效期 {expire} 秒{C['reset']}")
|
||
qr_printed.set()
|
||
|
||
def handle_status(info):
|
||
status = info['status']
|
||
if status == 'polling':
|
||
print(f" {C['yellow']}⏳ 等待扫码...{C['reset']}")
|
||
elif status == 'slow_down':
|
||
print(f" {C['yellow']}⏳ 等待中... (间隔 {info.get('interval', '?')}s){C['reset']}")
|
||
elif status == 'domain_switched':
|
||
print(f" {C['cyan']}🌐 已切换认证域名{C['reset']}")
|
||
|
||
def run_register():
|
||
try:
|
||
result = lark.register_app(
|
||
on_qr_code=handle_qr,
|
||
on_status_change=handle_status,
|
||
)
|
||
result_holder['data'] = result
|
||
except Exception as e:
|
||
print(f"\n {C['red']}✗ 创建失败: {e}{C['reset']}")
|
||
|
||
thread = threading.Thread(target=run_register, daemon=True)
|
||
thread.start()
|
||
qr_printed.wait(timeout=15)
|
||
thread.join(timeout=300)
|
||
|
||
if result_holder['data']:
|
||
result = result_holder['data']
|
||
print(f"\n {C['green']}✅ 应用创建成功！{C['reset']}")
|
||
print(f" App ID: {C['bold']}{result['client_id']}{C['reset']}")
|
||
print(f" App Secret: {C['bold']}{result['client_secret']}{C['reset']}")
|
||
return {
|
||
'fs_app_id': result['client_id'],
|
||
'fs_app_secret': result['client_secret'],
|
||
}
|
||
else:
|
||
print(f"\n {C['yellow']}⚠ 扫码创建未完成，降级为手动填写...{C['reset']}")
|
||
return {}
|
||
|
||
|
||
def _wechat_scan():
|
||
"""微信 iLink 扫码登录，保存 token 到 ~/.wxbot/token.json，返回 env_vals"""
|
||
print(f"\n {C['cyan']}📱 正在启动微信 iLink 扫码登录...{C['reset']}")
|
||
print(f" {C['dim']} 请用微信扫描终端二维码，完成授权后自动获取凭据。{C['reset']}\n")
|
||
|
||
# 确保项目根在路径中，以便导入 frontends/wechatapp
|
||
if PROJECT_ROOT not in sys.path:
|
||
sys.path.insert(0, PROJECT_ROOT)
|
||
try:
|
||
from frontends.wechatapp import WxBotClient
|
||
except ImportError as e:
|
||
print(f"\n {C['yellow']}⚠ 无法导入 WxBotClient: {e}{C['reset']}")
|
||
return {}
|
||
|
||
try:
|
||
bot = WxBotClient()
|
||
if bot.token:
|
||
print(f" {C['green']}✅ 已有有效 token (bot_id={bot.bot_id}){C['reset']}")
|
||
if ask_yesno("重新扫码登录？", default=False):
|
||
bot.token = ''
|
||
else:
|
||
return {}
|
||
bot.login_qr()
|
||
print(f"\n {C['green']}✅ 微信 iLink 扫码登录成功！{C['reset']}")
|
||
print(f" Bot ID: {C['bold']}{bot.bot_id}{C['reset']}")
|
||
print(f" Token 已保存到: {C['dim']}{bot._tf}{C['reset']}")
|
||
except Exception as e:
|
||
print(f"\n {C['red']}✗ 扫码登录失败: {e}{C['reset']}")
|
||
return {}
|
||
|
||
return {}
|
||
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
# 生成 mykey.py
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def _var_type_info(cfg):
|
||
"""根据配置类型返回 (var_prefix, session_type)"""
|
||
cfg_type = cfg.get('type', 'native_oai')
|
||
if cfg_type == 'native_claude':
|
||
return 'native_claude_config', 'NativeClaudeSession'
|
||
elif cfg_type == 'claude':
|
||
return 'claude_config', 'ClaudeSession'
|
||
elif cfg_type == 'oai':
|
||
return 'oai_config', 'LLMSession'
|
||
else:
|
||
return 'native_oai_config', 'NativeOAISession'
|
||
|
||
|
||
def generate_mykey(llm_cfgs, platform_configs):
|
||
"""生成 mykey.py 内容"""
|
||
lines = []
|
||
lines.append("# ══════════════════════════════════════════════════════════════════════════════")
|
||
lines.append(f"# GenericAgent — mykey.py (由 configure.py 自动生成 @ {datetime.now().strftime('%Y-%m-%d %H:%M')})")
|
||
lines.append("# ══════════════════════════════════════════════════════════════════════════════")
|
||
lines.append("")
|
||
lines.append("# ── 停止符 ──────────────────────────────────────────────────────────────────")
|
||
lines.append("_SETUP_DONE = 'configure.py' # 删除此行可重新触发配置向导")
|
||
lines.append("")
|
||
|
||
# Mixin 配置
|
||
names = [c['name'] for c in llm_cfgs]
|
||
lines.append("# ── Mixin 故障转移 ──────────────────────────────────────────────────────────")
|
||
lines.append("mixin_config = {")
|
||
lines.append(f" 'llm_nos': {names},")
|
||
lines.append(" 'max_retries': 10,")
|
||
lines.append(" 'base_delay': 0.5,")
|
||
lines.append("}")
|
||
lines.append("")
|
||
|
||
# 各模型配置
|
||
type_counts = {}
|
||
for cfg in llm_cfgs:
|
||
cfg_type = cfg.get('type', 'native_oai')
|
||
type_counts[cfg_type] = type_counts.get(cfg_type, 0) + 1
|
||
|
||
type_indices = {}
|
||
for i, cfg in enumerate(llm_cfgs):
|
||
cfg_type = cfg.get('type', 'native_oai')
|
||
var_prefix, session_type = _var_type_info(cfg)
|
||
idx = type_indices.get(cfg_type, 0)
|
||
type_indices[cfg_type] = idx + 1
|
||
|
||
if type_counts[cfg_type] > 1:
|
||
var_name = f"{var_prefix}_{idx}"
|
||
else:
|
||
var_name = var_prefix
|
||
|
||
lines.append(f"# ── {cfg['name']} ({session_type}) ─────────────────────────────────────────────")
|
||
lines.append(f"{var_name} = {{")
|
||
_write_config_fields(lines, cfg)
|
||
lines.append("}")
|
||
lines.append("")
|
||
|
||
# 平台配置
|
||
if platform_configs:
|
||
lines.append("# ══════════════════════════════════════════════════════════════════════════════")
|
||
lines.append("# 聊天平台集成")
|
||
lines.append("# ══════════════════════════════════════════════════════════════════════════════")
|
||
lines.append("")
|
||
for pc in platform_configs:
|
||
for key, val in pc['config'].items():
|
||
_write_platform_value(lines, key, val)
|
||
lines.append("")
|
||
|
||
# 尾部
|
||
lines.append("# ══════════════════════════════════════════════════════════════════════════════")
|
||
lines.append("# 配置完毕！运行: python agentmain.py (终端 REPL)")
|
||
if platform_configs:
|
||
for pc in platform_configs:
|
||
p = pc['platform']
|
||
lines.append(f"# 或: python {p['file']} ({p['name']})")
|
||
lines.append("# ══════════════════════════════════════════════════════════════════════════════")
|
||
|
||
return '\n'.join(lines)
|
||
|
||
def _write_config_fields(lines, cfg):
|
||
"""写入配置字典的键值对（缩进的 'key': value, 格式）"""
|
||
for key in ['name', 'type', 'apikey', 'apibase', 'model', 'api_mode',
|
||
'fake_cc_system_prompt', 'thinking_type', 'thinking_budget_tokens',
|
||
'reasoning_effort', 'max_tokens', 'max_retries', 'connect_timeout',
|
||
'read_timeout', 'temperature', 'context_win',
|
||
'proxy', 'user_agent', 'stream']:
|
||
if key not in cfg:
|
||
continue
|
||
val = cfg[key]
|
||
if isinstance(val, bool):
|
||
lines.append(f" '{key}': {str(val)},")
|
||
elif isinstance(val, (int, float)):
|
||
lines.append(f" '{key}': {val},")
|
||
elif isinstance(val, str):
|
||
lines.append(f" '{key}': '{val}',")
|
||
else:
|
||
lines.append(f" '{key}': {repr(val)},")
|
||
|
||
def _write_platform_value(lines, key, val):
|
||
"""写入顶级变量（平台配置等）"""
|
||
if isinstance(val, list):
|
||
if val:
|
||
lines.append(f"{key} = {repr(val)}")
|
||
else:
|
||
lines.append(f"{key} = [] # 允许所有用户")
|
||
elif isinstance(val, str):
|
||
lines.append(f"{key} = '{val}'")
|
||
else:
|
||
lines.append(f"{key} = {repr(val)}")
|
||
|
||
|
||
def _parse_existing_mykey():
|
||
"""解析已有 mykey.py，返回 (model_names, platform_infos)
|
||
|
||
model_names: [str] — 模型名列表
|
||
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))
|
||
|
||
# 先收集所有已知平台 env var key → 判断值类型
|
||
all_env_var_keys = {}
|
||
platform_env_keys = {} # pid -> [var_key]
|
||
for p in PLATFORMS:
|
||
pid = p['id']
|
||
platform_env_keys.setdefault(pid, [])
|
||
for var in p.get('env_vars', []):
|
||
vkey = var['key']
|
||
all_env_var_keys[vkey] = var
|
||
platform_env_keys[pid].append(vkey)
|
||
|
||
# 逐平台解析所有已知变量
|
||
platform_infos = []
|
||
for pid, env_keys in platform_env_keys.items():
|
||
vars_found = []
|
||
for vkey in env_keys:
|
||
var_def = all_env_var_keys[vkey]
|
||
val = None
|
||
if var_def.get('is_list'):
|
||
# 匹配 `xxx = [...]`
|
||
m_var = re.search(rf"^{vkey}\s*=\s*(\[[^\]]*\])", content, re.MULTILINE)
|
||
if m_var:
|
||
try:
|
||
val = ast.literal_eval(m_var.group(1))
|
||
except (ValueError, SyntaxError):
|
||
pass
|
||
else:
|
||
# 匹配 `xxx = '...'`
|
||
m_var = re.search(rf"^{vkey}\s*=\s*'([^']*)'", content, re.MULTILINE)
|
||
if m_var:
|
||
val = m_var.group(1)
|
||
if val is not None:
|
||
vars_found.append({'key': vkey, 'val': val})
|
||
if vars_found:
|
||
platform_infos.append({'id': pid, 'vars': vars_found})
|
||
|
||
return model_names, platform_infos
|
||
|
||
|
||
def _parse_existing_llm_cfgs():
|
||
"""解析已有 mykey.py，返回完整 LLM 配置字典列表 [{name, apikey, ...}]
|
||
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()
|
||
|
||
cfgs = []
|
||
# 匹配所有 `xxx = { ... }` 顶层字典赋值
|
||
# 用简单状态机: 找 `\w+ = {` 然后匹配花括号
|
||
pattern = re.compile(r'^(\w+)\s*=\s*\{', re.MULTILINE)
|
||
for m in pattern.finditer(content):
|
||
brace_start = m.end() - 1 # '{' 的位置
|
||
depth = 1
|
||
i = brace_start + 1
|
||
while i < len(content) and depth > 0:
|
||
if content[i] == '{':
|
||
depth += 1
|
||
elif content[i] == '}':
|
||
depth -= 1
|
||
i += 1
|
||
if depth == 0:
|
||
dict_text = content[m.end():i - 1]
|
||
try:
|
||
d = ast.literal_eval('{' + dict_text + '}')
|
||
if isinstance(d, dict) and 'name' in d:
|
||
cfgs.append(d)
|
||
except (ValueError, SyntaxError):
|
||
continue
|
||
|
||
return cfgs
|
||
|
||
|
||
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 safe_name == 'mykey':
|
||
safe_name = 'mykey_backup' # 避免和源文件同名
|
||
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
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def main():
|
||
banner()
|
||
|
||
# Python 版本检查
|
||
ok, msg = _check_python()
|
||
if not ok:
|
||
print(f" {C['red']}✗ {msg}{C['reset']}")
|
||
sys.exit(1)
|
||
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
|
||
|
||
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']}")
|
||
|
||
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_path}{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:
|
||
old_cfgs = _parse_existing_llm_cfgs()
|
||
if old_cfgs:
|
||
llm_cfgs = old_cfgs
|
||
print(f"\n {C['green']}✓ 已保留现有 LLM 配置: {', '.join(c['name'] for c in old_cfgs)}{C['reset']}")
|
||
else:
|
||
print(f"\n {C['yellow']}⚠ 保留 LLM 配置失败，将生成空配置。建议两项都重新配置。{C['reset']}")
|
||
|
||
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']}")
|
||
|
||
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()
|
||
elif os.path.exists(MYKPY_PATH):
|
||
# 新建+仅平台：从备份保留旧 LLM 配置
|
||
old_cfgs = _parse_existing_llm_cfgs()
|
||
if old_cfgs:
|
||
llm_cfgs = old_cfgs
|
||
print(f"\n {C['green']}✓ 已保留备份中的 LLM 配置: {', '.join(c['name'] for c in old_cfgs)}{C['reset']}")
|
||
|
||
# ── 生成 mykey.py ──
|
||
if not llm_cfgs and not platform_configs:
|
||
print(f"\n {C['yellow']}⚠ 没有配置任何内容，退出。{C['reset']}")
|
||
sys.exit(0)
|
||
|
||
content = generate_mykey(llm_cfgs, platform_configs)
|
||
|
||
# 备份旧文件（修改模式不备份，直接在原文件修改）
|
||
if os.path.exists(MYKPY_PATH) and not is_modify and not is_new:
|
||
backup = _backup_with_name(model_names, [p['id'] for p in platform_infos])
|
||
print(f"\n {C['green']}✓ 旧配置已备份至:{C['reset']} {C['dim']}{backup}{C['reset']}")
|
||
|
||
# 写入
|
||
with open(MYKPY_PATH, 'w', encoding='utf-8') as f:
|
||
f.write(content)
|
||
print(f"\n {C['green']}✓ mykey.py 已生成!{C['reset']}")
|
||
|
||
# ── 完成提示 ──
|
||
print(f"\n{C['bold']}{C['green']}╔══════════════════════════════════════╗")
|
||
print(f"║ 配置完成! ║")
|
||
print(f"╚══════════════════════════════════════╝{C['reset']}")
|
||
print()
|
||
if llm_cfgs:
|
||
print(f" {C['cyan']} 终端 REPL:{C['reset']} python agentmain.py")
|
||
if platform_configs:
|
||
for i, pc in enumerate(platform_configs, 1):
|
||
p = pc['platform']
|
||
print(f" {C['cyan']} 平台 {i} ({p['name']}):{C['reset']} python {p['file']}")
|
||
print()
|
||
|
||
# pip 依赖提示
|
||
all_deps = sorted(platform_deps)
|
||
if all_deps:
|
||
print(f" {C['yellow']}💡 提示：你需要安装以下依赖以使消息平台正常工作:{C['reset']}")
|
||
print(f" {C['cyan']}pip install {' '.join(all_deps)}{C['reset']}")
|
||
print()
|
||
|
||
# ── 入门示例 ──
|
||
print(f" {C['bold']}试试这些命令:{C['reset']}")
|
||
examples = [
|
||
"帮我在桌面创建一个 hello.txt，内容是 Hello World",
|
||
"请查看你的代码，安装所有用得上的 python 依赖",
|
||
"执行 web setup sop，解锁 web 工具",
|
||
"打开淘宝，搜索 iPhone 16，按价格排序",
|
||
"用rapidocr配置你的ocr能力并存入记忆",
|
||
"git 更新你的代码，然后看看 commit 有什么新功能",
|
||
"把这个记到你的记忆里",
|
||
]
|
||
for ex in examples:
|
||
print(f" {C['dim']}{ex}{C['reset']}")
|
||
print()
|
||
|
||
print(f" {C['green']}{C['bold']}合抱之木，生于毫末{C['reset']}\n")
|
||
|
||
|
||
def _do_llm():
|
||
"""配置 LLM 模型，失败则 exit。"""
|
||
cfgs = configure_llms()
|
||
if not cfgs:
|
||
print(f"\n {C['red']}✗ 至少需要配置一个模型才能使用。退出。{C['reset']}")
|
||
sys.exit(1)
|
||
return cfgs
|
||
|
||
|
||
if __name__ == '__main__':
|
||
try:
|
||
main()
|
||
except KeyboardInterrupt:
|
||
print(f"\n\n {C['yellow']}⚠ 用户中断{C['reset']}")
|
||
sys.exit(0)
|