534 lines
22 KiB
Python
534 lines
22 KiB
Python
import json
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import os
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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def _env(name: str, default: str = "") -> str:
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return os.environ.get(name, default)
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def _env_optional(name: str) -> Optional[str]:
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value = os.environ.get(name)
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if value is None:
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# 回退读取 .env（支持运行中更新）
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env_path = Path(__file__).resolve().parents[1] / ".env"
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if env_path.exists():
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try:
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for raw_line in env_path.read_text(encoding="utf-8").splitlines():
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line = raw_line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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key, val = line.split("=", 1)
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if key.strip() == name:
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value = val.strip().strip('"').strip("'")
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break
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except Exception:
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value = None
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if value is None:
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return None
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value = value.strip()
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return value or None
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# 模型上下文窗口（单位: token）
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CONTEXT_WINDOWS = {
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"kimi": 256_000,
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"kimi-k2.5": 256_000,
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"qwen3-max": 256_000,
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"qwen3-vl-plus": 256_000,
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"minimax-m2.5": 204_800,
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"deepseek": 128_000,
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}
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# 默认（Kimi）
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KIMI_BASE = _env("API_BASE_KIMI", _env("AGENT_API_BASE_URL", "https://api.moonshot.cn/v1"))
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KIMI_KEY = _env("API_KEY_KIMI", _env("AGENT_API_KEY", ""))
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KIMI_BASE_OFFICIAL = _env_optional("API_BASE_KIMI_OFFICIAL")
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KIMI_KEY_OFFICIAL = _env_optional("API_KEY_KIMI_OFFICIAL")
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KIMI_FAST_MODEL = _env("MODEL_KIMI_FAST", _env("AGENT_MODEL_ID", "kimi-k2-0905-preview"))
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KIMI_THINK_MODEL = _env("MODEL_KIMI_THINK", _env("AGENT_THINKING_MODEL_ID", "kimi-k2-thinking"))
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KIMI_25_MODEL = _env("MODEL_KIMI_25", "kimi-k2.5")
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# DeepSeek
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DEEPSEEK_BASE = _env("API_BASE_DEEPSEEK", "https://api.deepseek.com")
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DEEPSEEK_KEY = _env("API_KEY_DEEPSEEK", _env("AGENT_DEEPSEEK_API_KEY", ""))
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DEEPSEEK_FAST_MODEL = _env("MODEL_DEEPSEEK_FAST", "deepseek-chat")
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DEEPSEEK_THINK_MODEL = _env("MODEL_DEEPSEEK_THINK", "deepseek-reasoner")
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# Qwen
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QWEN_BASE = _env("API_BASE_QWEN", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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QWEN_KEY = _env("API_KEY_QWEN", _env("DASHSCOPE_API_KEY", ""))
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QWEN_BASE_OFFICIAL = _env_optional("API_BASE_QWEN_OFFICIAL")
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QWEN_KEY_OFFICIAL = _env_optional("API_KEY_QWEN_OFFICIAL")
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QWEN_MAX_MODEL = _env("MODEL_QWEN_MAX", "qwen3-max")
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QWEN_VL_MODEL = _env("MODEL_QWEN_VL", "qwen3.5-plus")
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# MiniMax
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MINIMAX_BASE = _env("API_BASE_MINIMAX", "https://api.minimaxi.com/v1")
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MINIMAX_KEY = _env("API_KEY_MINIMAX", "")
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MINIMAX_BASE_OFFICIAL = _env_optional("API_BASE_MINIMAX_OFFICIAL")
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MINIMAX_KEY_OFFICIAL = _env_optional("API_KEY_MINIMAX_OFFICIAL")
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MINIMAX_MODEL = _env("MODEL_MINIMAX", "MiniMax-M2.5")
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MODEL_PROFILES = {
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"kimi": {
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"context_window": CONTEXT_WINDOWS["kimi"],
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"fast": {
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"base_url": KIMI_BASE,
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"api_key": KIMI_KEY,
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"model_id": KIMI_FAST_MODEL,
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"max_tokens": None,
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"context_window": CONTEXT_WINDOWS["kimi"],
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},
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"thinking": {
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"base_url": KIMI_BASE,
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"api_key": KIMI_KEY,
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"model_id": KIMI_THINK_MODEL,
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"max_tokens": None,
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"context_window": CONTEXT_WINDOWS["kimi"],
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},
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"supports_thinking": True,
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"fast_only": False,
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"name": "Kimi-k2",
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"description": "综合能力较强",
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"multimodal": "none",
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},
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"kimi-k2.5": {
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"context_window": CONTEXT_WINDOWS["kimi-k2.5"],
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"fast": {
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"base_url": KIMI_BASE,
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"api_key": KIMI_KEY,
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"model_id": KIMI_25_MODEL,
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"max_tokens": None,
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"context_window": CONTEXT_WINDOWS["kimi-k2.5"],
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"extra_params": {"thinking": {"type": "disabled"}}
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},
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"thinking": {
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"base_url": KIMI_BASE,
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"api_key": KIMI_KEY,
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"model_id": KIMI_25_MODEL,
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"max_tokens": None,
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"context_window": CONTEXT_WINDOWS["kimi-k2.5"],
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"extra_params": {"thinking": {"type": "enabled"}, "enable_thinking": True}
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},
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"supports_thinking": True,
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"fast_only": False,
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"name": "Kimi-k2.5",
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"description": "新版 Kimi，支持图文 & 思考开关",
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"multimodal": "image,video",
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},
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"deepseek": {
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"context_window": CONTEXT_WINDOWS["deepseek"],
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"fast": {
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"base_url": DEEPSEEK_BASE,
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"api_key": DEEPSEEK_KEY,
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"model_id": DEEPSEEK_FAST_MODEL,
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"max_tokens": 8192,
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"context_window": CONTEXT_WINDOWS["deepseek"]
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},
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"thinking": {
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"base_url": DEEPSEEK_BASE,
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"api_key": DEEPSEEK_KEY,
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"model_id": DEEPSEEK_THINK_MODEL,
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"max_tokens": 65536,
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"context_window": CONTEXT_WINDOWS["deepseek"]
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},
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"supports_thinking": True,
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"fast_only": False,
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"name": "DeepSeek",
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"description": "数学能力较强",
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"multimodal": "none",
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},
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"qwen3-max": {
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"context_window": CONTEXT_WINDOWS["qwen3-max"],
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"fast": {
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"base_url": QWEN_BASE,
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"api_key": QWEN_KEY,
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"model_id": QWEN_MAX_MODEL,
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"max_tokens": 65536,
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"context_window": CONTEXT_WINDOWS["qwen3-max"]
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},
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"thinking": None, # 不支持思考
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"supports_thinking": False,
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"fast_only": True,
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"name": "Qwen3-Max",
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"description": "仅快速模式",
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"multimodal": "none",
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"hidden": True,
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},
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"qwen3-vl-plus": {
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"context_window": CONTEXT_WINDOWS["qwen3-vl-plus"],
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"fast": {
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"base_url": QWEN_BASE,
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"api_key": QWEN_KEY,
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"model_id": QWEN_VL_MODEL,
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"max_tokens": 32768,
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"context_window": CONTEXT_WINDOWS["qwen3-vl-plus"],
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"extra_params": {}
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},
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"thinking": {
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"base_url": QWEN_BASE,
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"api_key": QWEN_KEY,
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"model_id": QWEN_VL_MODEL,
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"max_tokens": 32768,
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"context_window": CONTEXT_WINDOWS["qwen3-vl-plus"],
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"extra_params": {"enable_thinking": True}
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},
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"supports_thinking": True,
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"fast_only": False,
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"name": "Qwen3.5",
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"description": "图文视频多模态 + 深度思考",
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"multimodal": "image,video",
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},
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"minimax-m2.5": {
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"context_window": CONTEXT_WINDOWS["minimax-m2.5"],
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"fast": {
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"base_url": MINIMAX_BASE,
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"api_key": MINIMAX_KEY,
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"model_id": MINIMAX_MODEL,
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"max_tokens": 65536,
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"context_window": CONTEXT_WINDOWS["minimax-m2.5"],
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"extra_params": {"reasoning_split": True}
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},
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"thinking": {
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"base_url": MINIMAX_BASE,
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"api_key": MINIMAX_KEY,
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"model_id": MINIMAX_MODEL,
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"max_tokens": 65536,
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"context_window": CONTEXT_WINDOWS["minimax-m2.5"],
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"extra_params": {"reasoning_split": True}
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},
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"supports_thinking": True,
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"fast_only": False,
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"deep_only": True,
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"name": "MiniMax-M2.5",
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"description": "仅深度思考，超长上下文",
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"multimodal": "none",
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}
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}
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MODEL_PROMPT_OVERRIDES = {
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"kimi": {
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"model_description": "你的基础模型是 Kimi-k2，由月之暗面公司开发，是一个开源的 MoE 架构模型，拥有 1T 参数和 32B 激活参数，当前智能助手应用由火山引擎提供 API 服务。",
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"thinking_model_line": "思考模式时，第一次请求的模型不是 Kimi-k2，而是 Kimi-k2-Thinking，一个更善于分析复杂问题、规划复杂流程的模型，在后续请求时模型会换回 Kimi-k2。",
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"deep_thinking_line": "在深度思考模式中，请求的模型是 Kimi-k2-Thinking，一个更善于分析复杂问题、规划复杂流程的模型。"
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},
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"kimi-k2.5": {
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"model_description": "你的基础模型是 Kimi-k2.5，支持图文多模态，并通过 thinking 参数开启/关闭思考能力。",
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"thinking_model_line": "思考模式时使用同一个 Kimi-k2.5 模型，但会在请求中注入 thinking={\"type\": \"enabled\"} 来开启思考；快速模式则传递 thinking={\"type\": \"disabled\"}。",
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"deep_thinking_line": "深度思考模式下，所有请求都会携带 thinking={\"type\": \"enabled\"}，以获得持续的推理能力。"
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},
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"deepseek": {
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"model_description": "你的基础模型是 DeepSeek-V3.2（deepseek-chat），由 DeepSeek 提供，数学与推理能力较强，当前通过官方 API 调用。",
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"thinking_model_line": "思考模式时，第一次请求使用 DeepSeek-Reasoner，一个强化推理的模型，后续请求会切回 DeepSeek-V3.2。",
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"deep_thinking_line": "在深度思考模式中，请求的模型是 DeepSeek-Reasoner，用于深入分析复杂问题并规划步骤。"
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},
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"qwen3-max": {
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"model_description": "你的基础模型是 Qwen3-Max，由通义千问提供，当前仅支持快速模式，不提供思考或深度思考能力。",
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"thinking_model_line": "Qwen3-Max 仅支持快速模式，思考模式会被自动关闭。",
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"deep_thinking_line": "Qwen3-Max 不支持深度思考模式，将保持快速模式。"
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},
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"qwen3-vl-plus": {
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"model_description": "你的基础模型是 Qwen3.5，由通义千问提供，支持图文多模态理解。",
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"thinking_model_line": "思考模式时仍使用 Qwen3.5，并开启思考能力。",
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"deep_thinking_line": "深度思考模式下，所有请求都将启用思考能力，以获得更强的分析表现。"
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},
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"minimax-m2.5": {
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"model_description": "你的基础模型是 MiniMax-M2.5，支持超长上下文，当前仅以深度思考模式运行。",
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"thinking_model_line": "MiniMax-M2.5 为思考模型，快速模式不会使用。",
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"deep_thinking_line": "深度思考模式下，所有请求持续输出思考过程并给出最终回答。"
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}
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}
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def get_model_profile(key: str) -> dict:
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profiles = get_registered_model_profiles()
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if key not in profiles:
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raise ValueError(f"未知模型 key: {key}")
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profile = profiles[key]
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try:
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from utils.aliyun_fallback import is_fallback_active
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except Exception:
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is_fallback_active = None
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if is_fallback_active and is_fallback_active(key):
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if key == "kimi-k2.5":
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kimi_base_official = _env_optional("API_BASE_KIMI_OFFICIAL") or KIMI_BASE_OFFICIAL
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kimi_key_official = _env_optional("API_KEY_KIMI_OFFICIAL") or KIMI_KEY_OFFICIAL
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if kimi_base_official and kimi_key_official:
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profile = dict(profile)
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fast = dict(profile.get("fast") or {})
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thinking = dict(profile.get("thinking") or fast)
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fast.update({"base_url": kimi_base_official, "api_key": kimi_key_official})
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thinking.update({"base_url": kimi_base_official, "api_key": kimi_key_official})
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profile["fast"] = fast
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profile["thinking"] = thinking
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elif key == "qwen3-vl-plus":
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qwen_base_official = _env_optional("API_BASE_QWEN_OFFICIAL") or QWEN_BASE_OFFICIAL
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qwen_key_official = _env_optional("API_KEY_QWEN_OFFICIAL") or QWEN_KEY_OFFICIAL
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if qwen_base_official and qwen_key_official:
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profile = dict(profile)
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fast = dict(profile.get("fast") or {})
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thinking = dict(profile.get("thinking") or fast)
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fast.update({"base_url": qwen_base_official, "api_key": qwen_key_official})
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thinking.update({"base_url": qwen_base_official, "api_key": qwen_key_official})
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profile["fast"] = fast
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profile["thinking"] = thinking
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elif key == "minimax-m2.5":
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minimax_base_official = _env_optional("API_BASE_MINIMAX_OFFICIAL") or MINIMAX_BASE_OFFICIAL
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minimax_key_official = _env_optional("API_KEY_MINIMAX_OFFICIAL") or MINIMAX_KEY_OFFICIAL
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if minimax_base_official and minimax_key_official:
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profile = dict(profile)
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fast = dict(profile.get("fast") or {})
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thinking = dict(profile.get("thinking") or fast)
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fast.update({"base_url": minimax_base_official, "api_key": minimax_key_official})
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thinking.update({"base_url": minimax_base_official, "api_key": minimax_key_official})
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profile["fast"] = fast
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profile["thinking"] = thinking
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# 基础校验：必须有 fast 段且有 key
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fast = profile.get("fast") or {}
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if not fast.get("api_key"):
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raise ValueError(f"模型 {key} 缺少 API Key 配置")
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return profile
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def _normalize_multimodal(value: Any) -> str:
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text = str(value or "none").strip().lower()
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if text in {"image", "video", "none", "image,video", "video,image"}:
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return "image,video" if "image" in text and "video" in text else text
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return "none"
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def _parse_env_ref(raw: Any) -> Optional[str]:
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text = str(raw or "").strip()
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if not text:
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return None
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# 1) ${ENV_KEY}
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if text.startswith("${") and text.endswith("}") and len(text) > 3:
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key = text[2:-1].strip()
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return _env_optional(key)
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# 2) env:ENV_KEY
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if text.lower().startswith("env:") and len(text) > 4:
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key = text[4:].strip()
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resolved = _env_optional(key)
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return resolved if resolved is not None else None
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# 3) $ENV_KEY
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if text.startswith("$") and len(text) > 1:
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key = text[1:].strip()
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resolved = _env_optional(key)
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return resolved if resolved is not None else None
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# 4) 直接写 ENV_KEY（若环境/.env存在同名键则读取其值，否则按字面量）
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if text.replace("_", "").isalnum() and text.upper() == text:
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resolved = _env_optional(text)
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if resolved is not None:
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return resolved
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return text
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def _reasoning_flags(capability: str) -> Dict[str, bool]:
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normalized = str(capability or "fast").replace(" ", "").lower()
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if normalized == "thinking":
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return {"supports_thinking": True, "fast_only": False, "deep_only": True}
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if normalized == "fast,thinking":
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return {"supports_thinking": True, "fast_only": False, "deep_only": False}
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return {"supports_thinking": False, "fast_only": True, "deep_only": False}
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def _build_custom_profile(item: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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key = str(item.get("model_name") or "").strip()
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if not key:
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return None
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base_url = _parse_env_ref(item.get("url"))
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api_key = _parse_env_ref(item.get("apikey"))
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if not base_url or not api_key:
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return None
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context_window = item.get("context_window")
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max_output_tokens = item.get("max_output_tokens")
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try:
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context_window = int(context_window) if context_window is not None else None
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except (TypeError, ValueError):
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context_window = None
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try:
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max_output_tokens = int(max_output_tokens) if max_output_tokens is not None else None
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except (TypeError, ValueError):
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max_output_tokens = None
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capability = str(item.get("reasoning_capability") or "fast").replace(" ", "").lower()
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flags = _reasoning_flags(capability)
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thinkmode = item.get("thinkmode_status") or {}
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mode_type = str((thinkmode.get("type") or thinkmode.get("mode") or "")).strip().lower()
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extra_global = item.get("extra_parameter") if isinstance(item.get("extra_parameter"), dict) else {}
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fast_extra = thinkmode.get("fast_extra_parameter") if isinstance(thinkmode.get("fast_extra_parameter"), dict) else {}
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if not fast_extra and isinstance(thinkmode.get("fast_parameter"), dict):
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fast_extra = thinkmode.get("fast_parameter")
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thinking_extra = thinkmode.get("thinking_extra_parameter") if isinstance(thinkmode.get("thinking_extra_parameter"), dict) else {}
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if not thinking_extra and isinstance(thinkmode.get("thinking_parameter"), dict):
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thinking_extra = thinkmode.get("thinking_parameter")
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fast_model_id = None
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thinking_model_id = None
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if mode_type == "switch_model":
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fast_model_id = str(thinkmode.get("fast_model_id") or "").strip()
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thinking_model_id = str(thinkmode.get("thinking_model_id") or "").strip()
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elif mode_type == "param_toggle":
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common_model = str(thinkmode.get("model_id") or "").strip()
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fast_model_id = common_model
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thinking_model_id = common_model
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else:
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||
common_model = str(thinkmode.get("model_id") or "").strip()
|
||
if not common_model:
|
||
common_model = str(item.get("model_id") or "").strip()
|
||
fast_model_id = common_model
|
||
thinking_model_id = common_model
|
||
|
||
if not fast_model_id:
|
||
fast_model_id = thinking_model_id
|
||
if not thinking_model_id:
|
||
thinking_model_id = fast_model_id
|
||
if not fast_model_id:
|
||
return None
|
||
|
||
fast_config = {
|
||
"base_url": base_url,
|
||
"api_key": api_key,
|
||
"model_id": fast_model_id,
|
||
"max_tokens": max_output_tokens,
|
||
"context_window": context_window,
|
||
"extra_params": {**extra_global, **(fast_extra or {})},
|
||
}
|
||
profile: Dict[str, Any] = {
|
||
"name": key,
|
||
"description": str(item.get("description") or ""),
|
||
"model_description": str(item.get("model_description") or ""),
|
||
"is_custom_model": True,
|
||
"hidden": not bool(item.get("visible", True)),
|
||
"multimodal": _normalize_multimodal(item.get("multimodal")),
|
||
"context_window": context_window,
|
||
"fast": fast_config,
|
||
"supports_thinking": bool(flags["supports_thinking"]),
|
||
"fast_only": bool(flags["fast_only"]),
|
||
}
|
||
if flags["deep_only"]:
|
||
profile["deep_only"] = True
|
||
if flags["supports_thinking"]:
|
||
profile["thinking"] = {
|
||
"base_url": base_url,
|
||
"api_key": api_key,
|
||
"model_id": thinking_model_id or fast_model_id,
|
||
"max_tokens": max_output_tokens,
|
||
"context_window": context_window,
|
||
"extra_params": {**extra_global, **(thinking_extra or {})},
|
||
}
|
||
else:
|
||
profile["thinking"] = None
|
||
return profile
|
||
|
||
|
||
def _load_custom_models() -> Dict[str, Dict[str, Any]]:
|
||
custom_path = Path(__file__).resolve().parent / "custom_models.json"
|
||
if not custom_path.exists():
|
||
return {}
|
||
try:
|
||
data = json.loads(custom_path.read_text(encoding="utf-8"))
|
||
except Exception:
|
||
return {}
|
||
items = data if isinstance(data, list) else data.get("models", [])
|
||
if not isinstance(items, list):
|
||
return {}
|
||
output: Dict[str, Dict[str, Any]] = {}
|
||
for item in items:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
profile = _build_custom_profile(item)
|
||
if not profile:
|
||
continue
|
||
model_name = str(item.get("model_name") or "").strip()
|
||
if not model_name:
|
||
continue
|
||
output[model_name] = profile
|
||
return output
|
||
|
||
|
||
def get_registered_model_profiles() -> Dict[str, Dict[str, Any]]:
|
||
merged = dict(MODEL_PROFILES)
|
||
merged.update(_load_custom_models()) # 同名覆盖内置，不报错
|
||
return merged
|
||
|
||
|
||
def get_registered_model_keys(visible_only: bool = False) -> List[str]:
|
||
keys: List[str] = []
|
||
for key, profile in get_registered_model_profiles().items():
|
||
if visible_only and profile.get("hidden"):
|
||
continue
|
||
keys.append(key)
|
||
return keys
|
||
|
||
|
||
def get_model_capabilities(key: str) -> Dict[str, Any]:
|
||
profile = get_model_profile(key)
|
||
multimodal = _normalize_multimodal(profile.get("multimodal"))
|
||
supports_image = multimodal in {"image", "image,video"}
|
||
supports_video = multimodal in {"video", "image,video"}
|
||
return {
|
||
"supports_image": supports_image,
|
||
"supports_video": supports_video,
|
||
"multimodal": multimodal,
|
||
"supports_thinking": bool(profile.get("supports_thinking", False)),
|
||
"fast_only": bool(profile.get("fast_only", False)),
|
||
"deep_only": bool(profile.get("deep_only", False)),
|
||
}
|
||
|
||
|
||
def model_supports_image(key: str) -> bool:
|
||
try:
|
||
return bool(get_model_capabilities(key).get("supports_image"))
|
||
except Exception:
|
||
return False
|
||
|
||
|
||
def model_supports_video(key: str) -> bool:
|
||
try:
|
||
return bool(get_model_capabilities(key).get("supports_video"))
|
||
except Exception:
|
||
return False
|
||
|
||
|
||
def get_model_prompt_replacements(key: str) -> dict:
|
||
"""获取模型相关的提示词替换字段，若缺失则回退到 Kimi 版本。"""
|
||
fallback = MODEL_PROMPT_OVERRIDES.get("kimi", {})
|
||
overrides = MODEL_PROMPT_OVERRIDES.get(key) or {}
|
||
custom_model_description = ""
|
||
is_custom_model = False
|
||
try:
|
||
custom_profile = get_registered_model_profiles().get(key) or {}
|
||
custom_model_description = str(custom_profile.get("model_description") or "")
|
||
is_custom_model = bool(custom_profile.get("is_custom_model"))
|
||
except Exception:
|
||
custom_model_description = ""
|
||
is_custom_model = False
|
||
if is_custom_model:
|
||
return {
|
||
"model_description": custom_model_description or overrides.get("model_description") or fallback.get("model_description") or "",
|
||
"thinking_model_line": "",
|
||
"deep_thinking_line": "",
|
||
}
|
||
return {
|
||
"model_description": custom_model_description or overrides.get("model_description") or fallback.get("model_description") or "",
|
||
"thinking_model_line": overrides.get("thinking_model_line") or fallback.get("thinking_model_line") or "",
|
||
"deep_thinking_line": overrides.get("deep_thinking_line") or fallback.get("deep_thinking_line") or ""
|
||
}
|
||
|
||
|
||
def get_model_context_window(key: str) -> Optional[int]:
|
||
"""
|
||
获取模型的最大上下文窗口（token 数）。
|
||
优先使用 profile.context_window，其次回退到 fast.context_window。
|
||
"""
|
||
profile = get_model_profile(key)
|
||
ctx = profile.get("context_window")
|
||
if ctx:
|
||
return ctx
|
||
fast = profile.get("fast") or {}
|
||
return fast.get("context_window")
|