from functools import lru_cache from typing import Any from zhenxun.configs.config import Config from zhenxun.configs.utils import parse_as from zhenxun.utils.pydantic_compat import model_dump from .models import DebugLogOptions, LLMConfig, ProviderConfig def get_ai_config(): """获取 AI 配置组""" return Config.get("AI") def get_default_providers() -> list[dict[str, Any]]: """获取默认提供商配置列表。""" return [ { "name": "DeepSeek", "api_key": "YOUR_API_KEY", "api_base": "https://api.deepseek.com", "api_type": "deepseek", "models": [ { "model_name": "deepseek-v4-pro", "reasoning_effort": "high", }, { "model_name": "deepseek-v4-flash", }, ], }, { "name": "Doubao", "api_key": "YOUR_ARK_API_KEY", "api_base": "https://ark.cn-beijing.volces.com/api", "api_type": "doubao", "models": [ {"model_name": "doubao-seed-1-6-250615"}, {"model_name": "doubao-seed-1-6-flash-250615"}, ], }, { "name": "siliconflow", "api_key": "YOUR_ARK_API_KEY", "api_base": "https://api.siliconflow.cn", "api_type": "openai", "models": [ {"model_name": "deepseek-ai/DeepSeek-V4-Flash"}, {"model_name": "BAAI/bge-m3"}, {"model_name": "BAAI/bge-reranker-v2-m3"}, ], }, { "name": "GLM", "api_key": "YOUR_API_KEY", "api_base": "https://open.bigmodel.cn", "api_type": "glm", "models": [ {"model_name": "glm-4.6v-flash"}, {"model_name": "glm-5v-turbo"}, ], }, { "name": "Gemini", "api_key": [ "AIzaSy*****************************", "AIzaSy*****************************", ], "api_base": "https://generativelanguage.googleapis.com", "api_type": "gemini", "models": [ {"model_name": "gemini-3.5-flash"}, {"model_name": "gemini-3.1-flash-lite"}, {"model_name": "gemini-2.5-flash-image"}, {"model_name": "gemini-embedding-2"}, {"model_name": "gemini-3.1-flash-tts-preview"}, ], }, { "name": "OpenRouter", "api_key": "YOUR_OPENROUTER_API_KEY", "api_base": "https://openrouter.ai/api", "api_type": "openrouter", "models": [ {"model_name": "google/gemini-3.1-flash-lite"}, {"model_name": "x-ai/grok-4"}, ], }, { "name": "MiniMax", "api_key": "YOUR_API_KEY", "api_base": "https://api.minimaxi.com", "api_type": "minimax", "models": [ {"model_name": "MiniMax-M3"}, {"model_name": "MiniMax-M2.7"}, {"model_name": "MiniMax-M2.7-highspeed"}, ], }, { "name": "MiMo", "api_key": "YOUR_MIMO_API_KEY", "api_base": "https://api.xiaomimimo.com", "api_type": "mimo", "models": [ {"model_name": "mimo-v2.5-pro"}, {"model_name": "mimo-v2.5"}, {"model_name": "mimo-v2.5-tts"}, ], }, ] def register_llm_configs(): """注册 LLM 服务的配置项""" llm_config = LLMConfig() Config.add_plugin_config( "AI", "default_models", model_dump(llm_config.default_models), help="不同任务类型的全局默认模型配置字典", type=dict, ) Config.add_plugin_config( "AI", "client_settings", model_dump(llm_config.client_settings), help=( "LLM客户端高级设置。\n" "包含: timeout(超时秒数), max_retries(重试次数), " "retry_delay(重试延迟), structured_retries(结构化生成重试)" ), type=dict, ) Config.add_plugin_config( "AI", "debug_log", model_dump(llm_config.debug_log), help=( "LLM日志详情开关。示例: {'show_tools': True, 'show_schema': False, " "'show_safety': False}" ), type=dict, ) Config.add_plugin_config( "AI", "context_settings", model_dump(llm_config.context_settings), help=( "智能上下文管理与压缩配置。\n" "包含:\n" " - llm_summary: 大模型总结策略配置\n" " - enable: 是否开启大模型对话总结以压缩上下文\n" " - trigger_threshold: 触发压缩的 Token 阈值。<=1.0为比例，>1.0为绝对 Token 数\n" # noqa: E501 " - max_history_turns: 触发压缩的最大历史对话轮数\n" " - summarization_model: 指定用于总结的大模型名称\n" " - summarization_prompt: 指导大模型总结的系统提示词\n" " - keep_recent_turns: 总结外强制原样保留的最近对话轮数\n" " - vision_window_size: 多模态滑动窗口大小。0表示无限制，>0表示仅保留最近N轮包含多模态真实数据的消息，超过则自动降级为占位符\n" # noqa: E501 " - tool_pruning: 工具结果过载修剪策略配置\n" " - enable: 是否开启长工具输出结果的自动修剪\n" " - trigger_threshold: 触发修剪的工具纯 Token 阈值。<=1.0为比例，>1.0为绝对 Token 数\n" # noqa: E501 " - max_history_turns: 触发修剪的最大工具消息轮数。设为 0 表示不限制轮数\n" # noqa: E501 " - keep_recent_turns: 修剪时强制原样保留的最新的工具消息轮数" ), type=dict, ) Config.add_plugin_config( "AI", "MODEL_GROUPS", llm_config.model_groups, help=( "虚拟模型路由组配置 (Virtual Router Groups)。\n" "键为组名，值为模型名称或其它组名的列表。\n" "使用 chat(model='cheap_models') 时系统将自动按列表顺序轮询和故障转移。" ), type=dict, ) Config.add_plugin_config( "AI", "agent_settings", model_dump(llm_config.agent_settings), help=( "Agent 执行引擎默认设置。\n" "包含: max_cycles(最大工具循环数), enable_parallel_calls(允许并行), " "reflexion_retries(反思重试次数), " "enable_fallback_summary(达到最大循环时兜底总结), " "enable_hitl(是否允许智能体主动向用户求助), " "mcp_cleanup_timeout(MCP 闲置回收时间)" ), type=dict, ) Config.add_plugin_config( "AI", "sandbox", model_dump(llm_config.sandbox), help=( "沙箱底层环境基础设施配置。\n" "包含: enable_sandbox(全局开关), sandbox_type(驱动类型), docker_image" "(使用的镜像), " "cleanup_timeout(空闲清理超时秒数), enable_vfs_helper(开启VFS防逃逸探针)。" ), type=dict, ) Config.add_plugin_config( "AI", "provider_settings", model_dump(llm_config.provider_settings), help=("厂商专属高级设置。\n包含各厂商全局的特有策略开关"), type=dict, ) Config.add_plugin_config( "AI", "PROVIDERS", get_default_providers(), help=( "配置多个 AI 服务提供商及其模型信息。\n" "注意：可以在特定模型配置下添加 'api_type' 以覆盖提供商的全局设置。\n" "支持的 api_type 包括:\n" "- 'openai': 标准 OpenAI 格式 (DeepSeek, SiliconFlow等)\n" "- 'gemini': Google Gemini API\n" "- 'glm': 智谱 AI (GLM)\n" "- 'doubao': 字节跳动火山引擎 (Doubao)\n" "- 'jina': Jina AI (专精于多模态嵌入与重排)\n" "- 'openrouter': OpenRouter 聚合平台\n" "- 'openai_responses': 支持新版 responses 格式的 OpenAI 兼容接口\n" "- 'smart': 智能路由模式 (主要用于第三方中转场景，自动根据模型名" "分发请求到 openai 或 gemini)" ), default_value=[], type=list[ProviderConfig], ) @lru_cache(maxsize=1) def get_llm_config() -> LLMConfig: """获取 LLM 配置实例""" ai_config = get_ai_config() raw_debug = ai_config.get("debug_log", False) if isinstance(raw_debug, bool): debug_log_val = DebugLogOptions( show_tools=raw_debug, show_schema=raw_debug, show_safety=raw_debug ) else: debug_log_val = raw_debug config_data = { "default_models": ai_config.get("default_models", {}), "client_settings": ai_config.get("client_settings", {}), "debug_log": debug_log_val, "PROVIDERS": ai_config.get("PROVIDERS", []), "context_settings": ai_config.get("context_settings", {}), "model_groups": ai_config.get("MODEL_GROUPS", {}), "agent_settings": ai_config.get("agent_settings", {}), "sandbox": ai_config.get("sandbox", {}), "provider_settings": ai_config.get("provider_settings", {}), } return parse_as(LLMConfig, config_data) def get_gemini_safety_threshold() -> str: """获取 Gemini 安全过滤阈值配置。""" return get_llm_config().provider_settings.gemini.safety_threshold