{"repo": "camel-ai/camel", "files": {"camel/types/enums.py": {"7dfa2a75a7b17f35a91c4841c52877e1272cd559": {"t": 1785674191.834007, "n": 84094, "h": "e675af24e40ff06a", "c": "# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\nimport os\nfrom enum import Enum, EnumMeta\nfrom typing import Union, cast\n\nfrom camel.logger import get_logger\nfrom camel.types.unified_model_type import UnifiedModelType\n\nlogger = get_logger(__name__)\n\n\nclass RoleType(Enum):\n ASSISTANT = \"assistant\"\n USER = \"user\"\n SYSTEM = \"system\"\n CRITIC = \"critic\"\n EMBODIMENT = \"embodiment\"\n DEFAULT = \"default\"\n\n\nclass ModelType(UnifiedModelType, Enum):\n DEFAULT = os.getenv(\"DEFAULT_MODEL_TYPE\", \"gpt-4.1-mini-2025-04-14\")\n\n GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n GPT_4 = \"gpt-4\"\n GPT_4_TURBO = \"gpt-4-turbo\"\n GPT_4O = \"gpt-4o\"\n GPT_4O_MINI = \"gpt-4o-mini\"\n GPT_4_5_PREVIEW = \"gpt-4.5-preview\"\n O1 = \"o1\"\n O1_PREVIEW = \"o1-preview\"\n O1_MINI = \"o1-mini\"\n O3_MINI = \"o3-mini\"\n GPT_4_1 = \"gpt-4.1\"\n GPT_4_1_MINI = \"gpt-4.1-mini-2025-04-14\"\n GPT_4_1_NANO = \"gpt-4.1-nano-2025-04-14\"\n O4_MINI = \"o4-mini\"\n O3 = \"o3\"\n O3_PRO = \"o3-pro\"\n GPT_5_1 = \"gpt-5.1\"\n GPT_5_2 = \"gpt-5.2\"\n GPT_5_4 = \"gpt-5.4\"\n GPT_5_4_MINI = \"gpt-5.4-mini-2026-03-17\"\n GPT_5_4_NANO = \"gpt-5.4-nano-2026-03-17\"\n GPT_5_4_PRO = \"gpt-5.5-pro-2026-04-23\"\n GPT_5_5 = \"gpt-5.5\"\n GPT_5_5_PRO = \"gpt-5.5-pro-2026-04-23\"\n GPT_5 = \"gpt-5\"\n GPT_5_MINI = \"gpt-5-mini\"\n GPT_5_NANO = \"gpt-5-nano\"\n\n AWS_CLAUDE_3_7_SONNET = \"anthropic.claude-3-7-sonnet-20250219-v1:0\"\n AWS_CLAUDE_3_5_SONNET = \"anthropic.claude-3-5-sonnet-20241022-v2:0\"\n AWS_CLAUDE_3_HAIKU = \"anthropic.claude-3-haiku-20240307-v1:0\"\n AWS_CLAUDE_3_SONNET = \"anthropic.claude-3-sonnet-20240229-v1:0\"\n AWS_DEEPSEEK_R1 = \"us.deepseek.r1-v1:0\"\n AWS_LLAMA_3_3_70B_INSTRUCT = \"us.meta.llama3-3-70b-instruct-v1:0\"\n AWS_LLAMA_3_2_90B_INSTRUCT = \"us.meta.llama3-2-90b-instruct-v1:0\"\n AWS_LLAMA_3_2_11B_INSTRUCT = \"us.meta.llama3-2-11b-instruct-v1:0\"\n AWS_CLAUDE_SONNET_4 = \"anthropic.claude-sonnet-4-20250514-v1:0\"\n AWS_CLAUDE_OPUS_4 = \"anthropic.claude-opus-4-20250514-v1:0\"\n AWS_CLAUDE_OPUS_4_1 = \"anthropic.claude-opus-4-1-20250805-v1:0\"\n\n AMD_GPT4 = \"dvue-aoai-001-gpt-4.1\"\n\n # ZhipuAI GLM-5 series (latest)\n GLM_5_2 = \"glm-5.2\"\n GLM_5_1 = \"glm-5.1\"\n GLM_5 = \"glm-5\"\n GLM_4_7 = \"glm-4.7\"\n GLM_4_7_FLASHX = \"glm-4.7-flashx\"\n GLM_4_7_FLASH = \"glm-4.7-flash\"\n GLM_4_6 = \"glm-4.6\"\n GLM_4 = \"glm-4\"\n GLM_4_5_AIR = \"glm-4.5-air\"\n GLM_4_5_AIRX = \"glm-4.5-airx\"\n GLM_4_5_FLASH = \"glm-4.5-flash\"\n GLM_4V = \"glm-4v\"\n GLM_4_1V_THINKING_FLASHX = \"glm-4.1v-thinking-flashx\"\n GLM_4_1V_THINKING_FLASH = \"glm-4.1v-thinking-flash\"\n GLM_4V_FLASH = \"glm-4v-flash\"\n GLM_4V_PLUS_0111 = \"glm-4v-plus-0111\"\n GLM_4_PLUS = \"glm-4-plus\"\n GLM_4_AIR = \"glm-4-air\"\n GLM_4_AIR_0111 = \"glm-4-air-0111\"\n GLM_4_AIRX = \"glm-4-airx\"\n GLM_4_LONG = \"glm-4-long\"\n GLM_4_FLASHX = \"glm-4-flashx\"\n GLM_4_FLASHX_250414 = \"glm-4-flashx-250414\"\n GLM_4_FLASH = \"glm-4-flash\"\n GLM_4_FLASH_250414 = \"glm-4-flash-250414\"\n GLM_ZERO_PREVIEW = \"glm-zero-preview\"\n GLM_3_TURBO = \"glm-3-turbo\"\n\n # ZhipuAI GLM Vision models (VLM)\n GLM_4_6V = \"glm-4.6v\"\n GLM_4_6V_FLASHX = \"glm-4.6v-flashx\"\n GLM_4_5V = \"glm-4.5v\"\n GLM_4_6V_FLASH = \"glm-4.6v-flash\"\n\n # Groq platform models\n GROQ_LLAMA_3_1_8B = \"llama-3.1-8b-instant\"\n GROQ_LLAMA_3_3_70B = \"llama-3.3-70b-versatile\"\n\n # xAI platform models\n XAI_GROK_4_5 = \"grok-4.5\"\n\n # Cerebras platform models\n CEREBRAS_GPT_OSS_120B = \"gpt-oss-120b\"\n CEREBRAS_LLAMA_3_1_8B = \"llama3.1-8b\"\n CEREBRAS_LLAMA_3_3_70B = \"llama3.3-70b\"\n CEREBRAS_QWEN_3_32B = \"qwen-3-32b\"\n\n # Nebius AI Studio platform models\n NEBIUS_GPT_OSS_120B = \"gpt-oss-120b\"\n NEBIUS_GPT_OSS_20B = \"gpt-oss-20b\"\n NEBIUS_GLM_4_5 = \"GLM-4.5\"\n NEBIUS_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n NEBIUS_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n NEBIUS_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n NEBIUS_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.3\"\n\n # CometAPI platform models\n COMETAPI_GPT_5_CHAT_LATEST = \"gpt-5-chat-latest\"\n COMETAPI_CHATGPT_4O_LATEST = \"chatgpt-4o-latest\"\n COMETAPI_GPT_5_MINI = \"gpt-5-mini\"\n COMETAPI_GPT_5_NANO = \"gpt-5-nano\"\n COMETAPI_GPT_5 = \"gpt-5\"\n COMETAPI_GPT_4_1 = \"gpt-4.1\"\n COMETAPI_GPT_4O_MINI = \"gpt-4o-mini\"\n COMETAPI_O4_MINI_2025_04_16 = \"o4-mini-2025-04-16\"\n COMETAPI_O3_PRO_2025_06_10 = \"o3-pro-2025-06-10\"\n COMETAPI_CLAUDE_OPUS_4_1_20250805 = \"claude-opus-4-1-20250805\"\n COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING = (\n \"claude-opus-4-1-20250805-thinking\"\n )\n COMETAPI_CLAUDE_SONNET_4_20250514 = \"claude-sonnet-4-20250514\"\n COMETAPI_CLAUDE_SONNET_4_20250514_THINKING = (\n \"claude-sonnet-4-20250514-thinking\"\n )\n COMETAPI_CLAUDE_3_7_SONNET_LATEST = \"claude-3-7-sonnet-latest\"\n COMETAPI_CLAUDE_3_5_HAIKU_LATEST = \"claude-3-5-haiku-latest\"\n COMETAPI_GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n COMETAPI_GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n COMETAPI_GEMINI_2_5_FLASH_LITE = \"gemini-2.5-flash-lite\"\n COMETAPI_GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n COMETAPI_GROK_4_0709 = \"grok-4-0709\"\n COMETAPI_GROK_3 = \"grok-3\"\n COMETAPI_GROK_3_MINI = \"grok-3-mini\"\n COMETAPI_GROK_2_IMAGE_1212 = \"grok-2-image-1212\"\n COMETAPI_DEEPSEEK_V3_1 = \"deepseek-v3.1\"\n COMETAPI_DEEPSEEK_V3 = \"deepseek-v3\"\n COMETAPI_DEEPSEEK_R1_0528 = \"deepseek-r1-0528\"\n COMETAPI_DEEPSEEK_CHAT = \"deepseek-chat\"\n COMETAPI_DEEPSEEK_REASONER = \"deepseek-reasoner\"\n COMETAPI_QWEN3_30B_A3B = \"qwen3-30b-a3b\"\n COMETAPI_QWEN3_CODER_PLUS_2025_07_22 = \"qwen3-coder-plus-2025-07-22\"\n\n # OpenRouter models\n OPENROUTER_LLAMA_3_1_405B = \"meta-llama/llama-3.1-405b-instruct\"\n OPENROUTER_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n OPENROUTER_LLAMA_4_MAVERICK = \"meta-llama/llama-4-maverick\"\n OPENROUTER_LLAMA_4_MAVERICK_FREE = \"meta-llama/llama-4-maverick:free\"\n OPENROUTER_LLAMA_4_SCOUT = \"meta-llama/llama-4-scout\"\n OPENROUTER_LLAMA_4_SCOUT_FREE = \"meta-llama/llama-4-scout:free\"\n OPENROUTER_OLYMPICODER_7B = \"open-r1/olympiccoder-7b:free\"\n OPENROUTER_HORIZON_ALPHA = \"openrouter/horizon-alpha\"\n\n ORCAROUTER_AUTO = \"orcarouter/auto\"\n ORCAROUTER_GPT_5 = \"openai/gpt-5\"\n ORCAROUTER_CLAUDE_OPUS_4_7 = \"anthropic/claude-opus-4.7\"\n ORCAROUTER_GEMINI_3_FLASH_PREVIEW = \"google/gemini-3-flash-preview\"\n ORCAROUTER_GROK_4_3 = \"grok/grok-4.3\"\n\n # LMStudio models\n LMSTUDIO_GEMMA_3_1B = \"gemma-3-1b\"\n LMSTUDIO_GEMMA_3_4B = \"gemma-3-4b\"\n LMSTUDIO_GEMMA_3_12B = \"gemma-3-12b\"\n LMSTUDIO_GEMMA_3_27B = \"gemma-3-27b\"\n\n # TogetherAI platform models support tool calling\n TOGETHER_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\"\n TOGETHER_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\"\n TOGETHER_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\"\n TOGETHER_LLAMA_3_3_70B = \"meta-llama/Llama-3.3-70B-Instruct-Turbo\"\n TOGETHER_MIXTRAL_8_7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n TOGETHER_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.1\"\n TOGETHER_LLAMA_4_MAVERICK = (\n \"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\"\n )\n TOGETHER_LLAMA_4_SCOUT = \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n\n # PPIO platform models support tool calling\n PPIO_DEEPSEEK_PROVER_V2_671B = \"deepseek/deepseek-prover-v2-671b\"\n PPIO_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n PPIO_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n PPIO_DEEPSEEK_R1_COMMUNITY = \"deepseek/deepseek-r1/community\"\n PPIO_DEEPSEEK_V3_COMMUNITY = \"deepseek/deepseek-v3/community\"\n PPIO_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n PPIO_DEEPSEEK_V3 = \"deepseek/deepseek-v3\"\n PPIO_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n PPIO_BAICHUAN_2_13B_CHAT = \"baichuan/baichuan2-13b-chat\"\n PPIO_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n PPIO_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n PPIO_YI_1_5_34B_CHAT = \"01-ai/yi-1.5-34b-chat\"\n\n # SambaNova Cloud platform models support tool calling\n SAMBA_LLAMA_3_1_8B = \"Meta-Llama-3.1-8B-Instruct\"\n SAMBA_LLAMA_3_1_70B = \"Meta-Llama-3.1-70B-Instruct\"\n SAMBA_LLAMA_3_1_405B = \"Meta-Llama-3.1-405B-Instruct\"\n\n # SGLang models support tool calling\n SGLANG_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n SGLANG_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n SGLANG_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct\"\n SGLANG_LLAMA_3_2_1B = \"meta-llama/Llama-3.2-1B-Instruct\"\n SGLANG_MIXTRAL_NEMO = \"mistralai/Mistral-Nemo-Instruct-2407\"\n SGLANG_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.3\"\n SGLANG_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B-Instruct\"\n SGLANG_QWEN_2_5_32B = \"Qwen/Qwen2.5-32B-Instruct\"\n SGLANG_QWEN_2_5_72B = \"Qwen/Qwen2.5-72B-Instruct\"\n\n STUB = \"stub\"\n\n # Claude models\n CLAUDE_3_7_SONNET = \"claude-3-7-sonnet-latest\"\n CLAUDE_SONNET_4_5 = \"claude-sonnet-4-5\"\n CLAUDE_SONNET_4 = \"claude-sonnet-4-20250514\"\n CLAUDE_SONNET_4_6 = \"claude-sonnet-4-6\"\n CLAUDE_HAIKU_4_5 = \"claude-haiku-4-5\"\n CLAUDE_FABLE_5 = \"claude-fable-5\"\n CLAUDE_OPUS_4 = \"claude-opus-4-20250514\"\n CLAUDE_OPUS_4_1 = \"claude-opus-4-1-20250805\"\n CLAUDE_OPUS_4_5 = \"claude-opus-4-5\"\n CLAUDE_OPUS_4_6 = \"claude-opus-4-6\"\n CLAUDE_OPUS_4_7 = \"claude-opus-4-7\"\n CLAUDE_OPUS_4_8 = \"claude-opus-4-8\"\n\n # Netmind models\n NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT = (\n \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n )\n NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT = (\n \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n )\n NETMIND_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n NETMIND_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3-0324\"\n NETMIND_DOUBAO_1_5_PRO = \"doubao/Doubao-1.5-pro\"\n NETMIND_QWQ_32B = \"Qwen/QwQ-32B\"\n\n # Nvidia models\n NVIDIA_NEMOTRON_340B_INSTRUCT = \"nvidia/nemotron-4-340b-instruct\"\n NVIDIA_NEMOTRON_340B_REWARD = \"nvidia/nemotron-4-340b-reward\"\n NVIDIA_YI_LARGE = \"01-ai/yi-large\"\n NVIDIA_MISTRAL_LARGE = \"mistralai/mistral-large\"\n NVIDIA_MIXTRAL_8X7B = \"mistralai/mixtral-8x7b-instruct\"\n NVIDIA_LLAMA3_70B = \"meta/llama3-70b\"\n NVIDIA_LLAMA3_1_8B_INSTRUCT = \"meta/llama-3.1-8b-instruct\"\n NVIDIA_LLAMA3_1_70B_INSTRUCT = \"meta/llama-3.1-70b-instruct\"\n NVIDIA_LLAMA3_1_405B_INSTRUCT = \"meta/llama-3.1-405b-instruct\"\n NVIDIA_LLAMA3_2_1B_INSTRUCT = \"meta/llama-3.2-1b-instruct\"\n NVIDIA_LLAMA3_2_3B_INSTRUCT = \"meta/llama-3.2-3b-instruct\"\n NVIDIA_LLAMA3_3_70B_INSTRUCT = \"meta/llama-3.3-70b-instruct\"\n\n # Gemini models\n GEMINI_3_1_PRO = \"gemini-3.1-pro-preview\"\n GEMINI_3_1_FLASH_LITE = \"gemini-3.1-flash-lite-preview\"\n GEMINI_3_PRO = \"gemini-3-pro-preview\"\n GEMINI_3_FLASH = \"gemini-3-flash-preview\"\n GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n GEMINI_2_0_FLASH_EXP = \"gemini-2.0-flash-exp\"\n GEMINI_2_0_FLASH_THINKING = \"gemini-2.0-flash-thinking-exp\"\n GEMINI_2_0_PRO_EXP = \"gemini-2.0-pro-exp-02-05\"\n GEMINI_2_0_FLASH_LITE = \"gemini-2.0-flash-lite\"\n GEMINI_2_0_FLASH_LITE_PREVIEW = \"gemini-2.0-flash-lite-preview-02-05\"\n\n # Mistral AI models\n MISTRAL_3B = \"ministral-3b-latest\"\n MISTRAL_7B = \"open-mistral-7b\"\n MISTRAL_8B = \"ministral-8b-latest\"\n MISTRAL_CODESTRAL = \"codestral-latest\"\n MISTRAL_CODESTRAL_MAMBA = \"open-codestral-mamba\"\n MISTRAL_LARGE = \"mistral-large-latest\"\n MISTRAL_MIXTRAL_8x7B = \"open-mixtral-8x7b\"\n MISTRAL_MIXTRAL_8x22B = \"open-mixtral-8x22b\"\n MISTRAL_NEMO = \"open-mistral-nemo\"\n MISTRAL_PIXTRAL_12B = \"pixtral-12b-2409\"\n MISTRAL_MEDIUM_3_1 = \"mistral-medium-2508\"\n MISTRAL_SMALL_3_2 = \"mistral-small-2506\"\n MAGISTRAL_SMALL_1_2 = \"magistral-small-1.2\"\n MAGISTRAL_MEDIUM_1_2 = \"magistral-medium-1.2\"\n\n # Reka models\n REKA_CORE = \"reka-core\"\n REKA_FLASH = \"reka-flash\"\n REKA_EDGE = \"reka-edge\"\n\n # Cohere models\n COHERE_COMMAND_R_PLUS = \"command-r-plus\"\n COHERE_COMMAND_R = \"command-r\"\n COHERE_COMMAND_LIGHT = \"command-light\"\n COHERE_COMMAND = \"command\"\n COHERE_COMMAND_NIGHTLY = \"command-nightly\"\n\n # Qwen models (Aliyun)\n QWEN_MAX = \"qwen-max\"\n QWEN_PLUS = \"qwen-plus\"\n QWEN_TURBO = \"qwen-turbo\"\n QWEN_PLUS_LATEST = \"qwen-plus-latest\"\n QWEN_PLUS_2025_04_28 = \"qwen-plus-2025-04-28\"\n QWEN_TURBO_LATEST = \"qwen-turbo-latest\"\n QWEN_TURBO_2025_04_28 = \"qwen-turbo-2025-04-28\"\n QWEN_LONG = \"qwen-long\"\n QWEN_VL_MAX = \"qwen-vl-max\"\n QWEN_VL_PLUS = \"qwen-vl-plus\"\n QWEN_MATH_PLUS = \"qwen-math-plus\"\n QWEN_MATH_TURBO = \"qwen-math-turbo\"\n QWEN_CODER_TURBO = \"qwen-coder-turbo\"\n QWEN_2_5_CODER_32B = \"qwen2.5-coder-32b-instruct\"\n QWEN_2_5_VL_72B = \"qwen2.5-vl-72b-instruct\"\n QWEN_2_5_72B = \"qwen2.5-72b-instruct\"\n QWEN_2_5_32B = \"qwen2.5-32b-instruct\"\n QWEN_2_5_14B = \"qwen2.5-14b-instruct\"\n QWEN_QWQ_32B = \"qwq-32b-preview\"\n QWEN_QVQ_72B = \"qvq-72b-preview\"\n QWEN_QWQ_PLUS = \"qwq-plus\"\n QWEN_3_CODER_PLUS = \"qwen3-coder-plus\"\n\n # Yi models (01-ai)\n YI_LIGHTNING = \"yi-lightning\"\n YI_LARGE = \"yi-large\"\n YI_MEDIUM = \"yi-medium\"\n YI_LARGE_TURBO = \"yi-large-turbo\"\n YI_VISION = \"yi-vision\"\n YI_MEDIUM_200K = \"yi-medium-200k\"\n YI_SPARK = \"yi-spark\"\n YI_LARGE_RAG = \"yi-large-rag\"\n YI_LARGE_FC = \"yi-large-fc\"\n\n # DeepSeek models\n DEEPSEEK_V4_FLASH = \"deepseek-v4-flash\"\n DEEPSEEK_V4_PRO = \"deepseek-v4-pro\"\n DEEPSEEK_CHAT = \"deepseek-chat\"\n DEEPSEEK_REASONER = \"deepseek-reasoner\"\n # InternLM models\n INTERNLM3_LATEST = \"internlm3-latest\"\n INTERNLM3_8B_INSTRUCT = \"internlm3-8b-instruct\"\n INTERNLM2_5_LATEST = \"internlm2.5-latest\"\n INTERNLM2_PRO_CHAT = \"internlm2-pro-chat\"\n\n # Moonshot models\n MOONSHOT_V1_8K = \"moonshot-v1-8k\"\n MOONSHOT_V1_32K = \"moonshot-v1-32k\"\n MOONSHOT_V1_128K = \"moonshot-v1-128k\"\n MOONSHOT_KIMI_K2 = \"kimi-k2-0905-Preview\"\n MOONSHOT_KIMI_K2_5 = \"kimi-k2.5\"\n MOONSHOT_KIMI_K2_TURBO_PREVIEW = \"kimi-k2-turbo-preview\"\n MOONSHOT_KIMI_K2_THINKING = \"kimi-k2-thinking\"\n MOONSHOT_KIMI_K2_THINKING_TURBO = \"kimi-k2-thinking-turbo\"\n\n # SiliconFlow models support tool calling\n SILICONFLOW_DEEPSEEK_V2_5 = \"deepseek-ai/DeepSeek-V2.5\"\n SILICONFLOW_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n SILICONFLOW_PRO_DEEPSEEK_V3 = \"Pro/deepseek-ai/DeepSeek-V3\"\n SILICONFLOW_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n SILICONFLOW_PRO_DEEPSEEK_R1 = \"Pro/deepseek-ai/DeepSeek-R1\"\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_32B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B\"\n )\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_14B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-14B\"\n )\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n )\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n )\n SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n \"Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n )\n SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n \"Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n )\n SILICONFLOW_INTERN_LM2_5_20B_CHAT = \"internlm/internlm2_5-20b-chat\"\n SILICONFLOW_INTERN_LM2_5_7B_CHAT = \"internlm/internlm2_5-7b-chat\"\n SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT = \"Pro/internlm/internlm2_5-7b-chat\"\n SILICONFLOW_QWQ_32B = \"Qwen/QwQ-32B\"\n SILICONFLOW_QWEN2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n SILICONFLOW_QWEN2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n SILICONFLOW_QWEN2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n SILICONFLOW_QWEN2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT = \"Pro/Qwen/Qwen2.5-7B-Instruct\"\n SILICONFLOW_THUDM_GLM_4_9B_CHAT = \"THUDM/glm-4-9b-chat\"\n SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT = \"Pro/THUDM/glm-4-9b-chat\"\n SILICONFLOW_THUDM_GLM_Z1_32B_0414 = \"THUDM/GLM-Z1-32B-0414\"\n SILICONFLOW_THUDM_GLM_4_32B_0414 = \"THUDM/GLM-4-32B-0414\"\n SILICONFLOW_THUDM_GLM_Z1_RUMINATION_32B_0414 = (\n \"THUDM/GLM-Z1-Rumination-32B-0414\"\n )\n SILICONFLOW_THUDM_GLM_4_9B_0414 = \"THUDM/GLM-4-9B-0414\"\n\n # AIML models support tool calling\n AIML_MIXTRAL_8X7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n AIML_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.1\"\n\n # Novita platform models support tool calling\n NOVITA_LLAMA_4_MAVERICK_17B = (\n \"meta-llama/llama-4-maverick-17b-128e-instruct-fp8\"\n )\n NOVITA_LLAMA_4_SCOUT_17B = \"meta-llama/llama-4-scout-17b-16e-instruct\"\n NOVITA_DEEPSEEK_V3_0324 = \"deepseek/deepseek-v3-0324\"\n NOVITA_QWEN_2_5_V1_72B = \"qwen/qwen2.5-vl-72b-instruct\"\n NOVITA_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n NOVITA_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n NOVITA_GEMMA_3_27B_IT = \"google/gemma-3-27b-it\"\n NOVITA_QWEN_32B = \"qwen/qwq-32b\"\n NOVITA_L3_8B_STHENO_V3_2 = \"Sao10K/L3-8B-Stheno-v3.2\"\n NOVITA_MYTHOMAX_L2_13B = \"gryphe/mythomax-l2-13b\"\n NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n \"deepseek/deepseek-r1-distill-llama-8b\"\n )\n NOVITA_DEEPSEEK_V3 = \"deepseek/deepseek_v3\"\n NOVITA_LLAMA_3_1_8B = \"meta-llama/llama-3.1-8b-instruct\"\n NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B = (\n \"deepseek/deepseek-r1-distill-qwen-14b\"\n )\n NOVITA_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n NOVITA_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n NOVITA_MISTRAL_NEMO = \"mistralai/mistral-nemo\"\n NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B = (\n \"deepseek/deepseek-r1-distill-qwen-32b\"\n )\n NOVITA_LLAMA_3_8B = \"meta-llama/llama-3-8b-instruct\"\n NOVITA_WIZARDLM_2_8X22B = \"microsoft/wizardlm-2-8x22b\"\n NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B = (\n \"deepseek/deepseek-r1-distill-llama-70b\"\n )\n NOVITA_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n NOVITA_GEMMA_2_9B_IT = \"google/gemma-2-9b-it\"\n NOVITA_MISTRAL_7B = \"mistralai/mistral-7b-instruct\"\n NOVITA_LLAMA_3_70B = \"meta-llama/llama-3-70b-instruct\"\n NOVITA_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n NOVITA_HERMES_2_PRO_LLAMA_3_8B = \"nousresearch/hermes-2-pro-llama-3-8b\"\n NOVITA_L3_70B_EURYALE_V2_1 = \"sao10k/l3-70b-euryale-v2.1\"\n NOVITA_DOLPHIN_MIXTRAL_8X22B = (\n \"cognitivecomputations/dolphin-mixtral-8x22b\"\n )\n NOVITA_AIROBOROS_L2_70B = \"jondurbin/airoboros-l2-70b\"\n NOVITA_MIDNIGHT_ROSE_70B = \"sophosympatheia/midnight-rose-70b\"\n NOVITA_L3_8B_LUNARIS = \"sao10k/l3-8b-lunaris\"\n NOVITA_GLM_4_9B_0414 = \"thudm/glm-4-9b-0414\"\n NOVITA_GLM_Z1_9B_0414 = \"thudm/glm-z1-9b-0414\"\n NOVITA_GLM_Z1_32B_0414 = \"thudm/glm-z1-32b-0414\"\n NOVITA_GLM_4_32B_0414 = \"thudm/glm-4-32b-0414\"\n NOVITA_GLM_Z1_RUMINATION_32B_0414 = \"thudm/glm-z1-rumination-32b-0414\"\n NOVITA_QWEN_2_5_7B = \"qwen/qwen2.5-7b-instruct\"\n NOVITA_LLAMA_3_2_1B = \"meta-llama/llama-3.2-1b-instruct\"\n NOVITA_LLAMA_3_2_11B_VISION = \"meta-llama/llama-3.2-11b-vision-instruct\"\n NOVITA_LLAMA_3_2_3B = \"meta-llama/llama-3.2-3b-instruct\"\n NOVITA_LLAMA_3_1_8B_BF16 = \"meta-llama/llama-3.1-8b-instruct-bf16\"\n NOVITA_L31_70B_EURYALE_V2_2 = \"sao10k/l31-70b-euryale-v2.2\"\n\n # ModelScope models support tool calling\n MODELSCOPE_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n MODELSCOPE_QWEN_2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n MODELSCOPE_QWEN_2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n MODELSCOPE_QWEN_2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT = \"Qwen/Qwen2.5-Coder-7B-Instruct\"\n MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT = \"Qwen/Qwen2.5-Coder-14B-Instruct\"\n MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT = \"Qwen/Qwen2.5-Coder-32B-Instruct\"\n MODELSCOPE_QWEN_3_235B_A22B = \"Qwen/Qwen3-235B-A22B\"\n MODELSCOPE_QWEN_3_32B = \"Qwen/Qwen3-32B\"\n MODELSCOPE_QWQ_32B = \"Qwen/QwQ-32B\"\n MODELSCOPE_QWQ_32B_PREVIEW = \"Qwen/QwQ-32B-Preview\"\n MODELSCOPE_LLAMA_3_1_8B_INSTRUCT = (\n \"LLM-Research/Meta-Llama-3.1-8B-Instruct\"\n )\n MODELSCOPE_LLAMA_3_1_70B_INSTRUCT = (\n \"LLM-Research/Meta-Llama-3.1-70B-Instruct\"\n )\n MODELSCOPE_LLAMA_3_1_405B_INSTRUCT = (\n \"LLM-Research/Meta-Llama-3.1-405B-Instruct\"\n )\n MODELSCOPE_LLAMA_3_3_70B_INSTRUCT = \"LLM-Research/Llama-3.3-70B-Instruct\"\n MODELSCOPE_MINISTRAL_8B_INSTRUCT = \"mistralai/Ministral-8B-Instruct-2410\"\n MODELSCOPE_DEEPSEEK_V3_0324 = \"deepseek-ai/DeepSeek-V3-0324\"\n MODELSCOPE_ERNIE_4_5_VL_28B_A3B_THINKING = (\n \"PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Thinking\"\n )\n\n # WatsonX models\n WATSONX_GRANITE_3_8B_INSTRUCT = \"ibm/granite-3-8b-instruct\"\n WATSONX_LLAMA_3_3_70B_INSTRUCT = \"meta-llama/llama-3-3-70b-instruct\"\n WATSONX_LLAMA_3_2_1B_INSTRUCT = \"meta-llama/llama-3-2-1b-instruct\"\n WATSONX_LLAMA_3_2_3B_INSTRUCT = \"meta-llama/llama-3-2-3b-instruct\"\n WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT = (\n \"meta-llama/llama-3-2-11b-vision-instruct\"\n )\n WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT = (\n \"meta-llama/llama-3-2-90b-vision-instruct\"\n )\n WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT = (\n \"meta-llama/llama-guard-3-11b-vision-instruct\"\n )\n WATSONX_MISTRAL_LARGE = \"mistralai/mistral-large\"\n\n # Qianfan models\n ERNIE_X1_TURBO_32K = \"ernie-x1-turbo-32k\"\n ERNIE_X1_32K = \"ernie-x1-32k\"\n ERNIE_X1_32K_PREVIEW = \"ernie-x1-32k-preview\"\n ERNIE_4_5_TURBO_128K = \"ernie-4.5-turbo-128k\"\n ERNIE_4_5_TURBO_32K = \"ernie-4.5-turbo-32k\"\n DEEPSEEK_V3 = \"deepseek-v3\"\n DEEPSEEK_R1 = \"deepseek-r1\"\n QWEN3_235B_A22B = \"qwen3-235b-a22b\"\n ERNIE_5_0_THINKING = \"ernie-5.0-thinking-latest\"\n ERNIE_4_5_TURBO_VL = \"ernie-4.5-turbo-vl-latest\"\n\n # Crynux models\n CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n )\n CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n )\n CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Llama-8B\"\n )\n\n CRYNUX_QWEN_3_4_B = \"Qwen/Qwen3-4B\"\n CRYNUX_QWEN_3_8_B = \"Qwen/Qwen3-8B\"\n CRYNUX_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B\"\n CRYNUX_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n\n CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B = \"NousResearch/Hermes-3-Llama-3.1-8B\"\n CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B = \"NousResearch/Hermes-3-Llama-3.2-3B\"\n\n # Minimax models\n MINIMAX_M3 = \"MiniMax-M3\"\n MINIMAX_M2_7 = \"MiniMax-M2.7\"\n MINIMAX_M2_7_HIGHSPEED = \"MiniMax-M2.7-highspeed\"\n MINIMAX_M2_5 = \"MiniMax-M2.5\"\n MINIMAX_M2_1 = \"MiniMax-M2.1\"\n MINIMAX_M2_1_LIGHTNING = \"MiniMax-M2.1-lightning\"\n MINIMAX_M2 = \"MiniMax-M2\"\n\n # Avian models\n AVIAN_DEEPSEEK_V3_2 = \"deepseek/deepseek-v3.2\"\n AVIAN_KIMI_K2_5 = \"moonshotai/kimi-k2.5\"\n AVIAN_GLM_5 = \"z-ai/glm-5\"\n AVIAN_MINIMAX_M2_5 = \"minimax/minimax-m2.5\"\n\n # AtlasCloud models\n ATLASCLOUD_GPT_OSS_120B = \"openai/gpt-oss-120b\"\n ATLASCLOUD_GLM_4_7 = \"zai-org/glm-4.7\"\n\n def __str__(self):\n return self.value\n\n def __repr__(self):\n return self.value\n\n def __new__(cls, value: Union[\"ModelType\", str]) -> \"ModelType\":\n return cast(\"ModelType\", UnifiedModelType.__new__(cls, value))\n\n @classmethod\n def from_name(cls, name: str) -> \"ModelType\":\n r\"\"\"Returns the ModelType enum value from a string.\"\"\"\n for model_type in cls:\n if model_type.value == name:\n return model_type\n raise ValueError(f\"Unknown ModelType name: {name}\")\n\n @property\n def value_for_tiktoken(self) -> str:\n if self.is_openai:\n return self.value\n return \"gpt-4o-mini\"\n\n @property\n def support_native_structured_output(self) -> bool:\n return any(\n [\n self.is_openai,\n self.is_xai,\n ]\n )\n\n @property\n def support_native_tool_calling(self) -> bool:\n return any(\n [\n self.is_openai,\n self.is_gemini,\n self.is_mistral,\n self.is_qwen,\n self.is_minimax,\n self.is_deepseek,\n self.is_ppio,\n self.is_cohere,\n self.is_internlm,\n self.is_together,\n self.is_sambanova,\n self.is_groq,\n self.is_cerebras,\n self.is_openrouter,\n self.is_orcarouter,\n self.is_lmstudio,\n self.is_sglang,\n self.is_moonshot,\n self.is_siliconflow,\n self.is_modelscope,\n self.is_zhipuai,\n self.is_aiml,\n self.is_azure_openai,\n self.is_novita,\n self.is_avian,\n self.is_xai,\n ]\n )\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n return self in {\n ModelType.GPT_3_5_TURBO,\n ModelType.GPT_4,\n ModelType.GPT_4_TURBO,\n ModelType.GPT_4O,\n ModelType.GPT_4O_MINI,\n ModelType.O1,\n ModelType.O1_PREVIEW,\n ModelType.O1_MINI,\n ModelType.O3_PRO,\n ModelType.O3_MINI,\n ModelType.GPT_4_5_PREVIEW,\n ModelType.GPT_4_1,\n ModelType.GPT_4_1_MINI,\n ModelType.GPT_4_1_NANO,\n ModelType.GPT_5,\n ModelType.GPT_5_MINI,\n ModelType.GPT_5_NANO,\n ModelType.O4_MINI,\n ModelType.O3,\n ModelType.GPT_5_1,\n ModelType.GPT_5_2,\n ModelType.GPT_5_4,\n ModelType.GPT_5_4_MINI,\n ModelType.GPT_5_4_NANO,\n ModelType.GPT_5_4_PRO,\n ModelType.GPT_5_5,\n ModelType.GPT_5_5_PRO,\n }\n\n @property\n def is_amd(self) -> bool:\n r\"\"\"Returns whether this type of models is a AMD model.\"\"\"\n return self in {\n ModelType.AMD_GPT4,\n }\n\n @property\n def is_aws_bedrock(self) -> bool:\n r\"\"\"Returns whether this type of models is an AWS Bedrock model.\"\"\"\n return self in {\n ModelType.AWS_CLAUDE_3_7_SONNET,\n ModelType.AWS_CLAUDE_3_5_SONNET,\n ModelType.AWS_CLAUDE_3_HAIKU,\n ModelType.AWS_CLAUDE_3_SONNET,\n ModelType.AWS_DEEPSEEK_R1,\n ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n ModelType.AWS_CLAUDE_SONNET_4,\n ModelType.AWS_CLAUDE_OPUS_4,\n ModelType.AWS_CLAUDE_OPUS_4_1,\n }\n\n @property\n def is_azure_openai(self) -> bool:\n r\"\"\"Returns whether this type of models is an OpenAI-released model\n from Azure.\n \"\"\"\n return self in {\n ModelType.GPT_3_5_TURBO,\n ModelType.GPT_4,\n ModelType.GPT_4_TURBO,\n ModelType.GPT_4O,\n ModelType.GPT_4O_MINI,\n ModelType.O1,\n ModelType.O1_PREVIEW,\n ModelType.O1_MINI,\n ModelType.O3_MINI,\n ModelType.O3_PRO,\n ModelType.GPT_4_5_PREVIEW,\n ModelType.GPT_4_1,\n ModelType.GPT_4_1_MINI,\n ModelType.GPT_4_1_NANO,\n ModelType.GPT_5,\n ModelType.O4_MINI,\n ModelType.O3,\n }\n\n @property\n def is_zhipuai(self) -> bool:\n r\"\"\"Returns whether this type of models is a ZhipuAI model.\"\"\"\n return self in {\n ModelType.GLM_3_TURBO,\n ModelType.GLM_4,\n ModelType.GLM_4_5_AIR,\n ModelType.GLM_4_5_AIRX,\n ModelType.GLM_4_5_FLASH,\n ModelType.GLM_4V,\n ModelType.GLM_4_1V_THINKING_FLASHX,\n ModelType.GLM_4_1V_THINKING_FLASH,\n ModelType.GLM_4V_FLASH,\n ModelType.GLM_4V_PLUS_0111,\n ModelType.GLM_4_PLUS,\n ModelType.GLM_4_AIR,\n ModelType.GLM_4_AIR_0111,\n ModelType.GLM_4_AIRX,\n ModelType.GLM_4_LONG,\n ModelType.GLM_4_FLASHX,\n ModelType.GLM_4_FLASHX_250414,\n ModelType.GLM_4_FLASH,\n ModelType.GLM_4_FLASH_250414,\n ModelType.GLM_ZERO_PREVIEW,\n ModelType.GLM_5_2,\n ModelType.GLM_5_1,\n ModelType.GLM_5,\n ModelType.GLM_4_7,\n ModelType.GLM_4_7_FLASHX,\n ModelType.GLM_4_7_FLASH,\n ModelType.GLM_4_6,\n ModelType.GLM_4_6V,\n ModelType.GLM_4_6V_FLASHX,\n ModelType.GLM_4_5V,\n ModelType.GLM_4_6V_FLASH,\n }\n\n @property\n def is_anthropic(self) -> bool:\n r\"\"\"Returns whether this type of models is Anthropic-released model.\n\n Returns:\n bool: Whether this type of models is anthropic.\n \"\"\"\n return self in {\n ModelType.CLAUDE_3_7_SONNET,\n ModelType.CLAUDE_SONNET_4_5,\n ModelType.CLAUDE_SONNET_4_6,\n ModelType.CLAUDE_SONNET_4,\n ModelType.CLAUDE_HAIKU_4_5,\n ModelType.CLAUDE_FABLE_5,\n ModelType.CLAUDE_OPUS_4,\n ModelType.CLAUDE_OPUS_4_1,\n ModelType.CLAUDE_OPUS_4_5,\n ModelType.CLAUDE_OPUS_4_6,\n ModelType.CLAUDE_OPUS_4_7,\n ModelType.CLAUDE_OPUS_4_8,\n }\n\n @property\n def is_groq(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Groq.\"\"\"\n return self in {\n ModelType.GROQ_LLAMA_3_1_8B,\n ModelType.GROQ_LLAMA_3_3_70B,\n }\n\n @property\n def is_xai(self) -> bool:\n r\"\"\"Returns whether this type of models is served by xAI.\"\"\"\n return self in {\n ModelType.XAI_GROK_4_5,\n }\n\n @property\n def is_cerebras(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Cerebras.\"\"\"\n return self in {\n ModelType.CEREBRAS_GPT_OSS_120B,\n ModelType.CEREBRAS_LLAMA_3_1_8B,\n ModelType.CEREBRAS_LLAMA_3_3_70B,\n ModelType.CEREBRAS_QWEN_3_32B,\n }\n\n @property\n def is_nebius(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Nebius AI\n Studio.\"\"\"\n return self in {\n ModelType.NEBIUS_GPT_OSS_120B,\n ModelType.NEBIUS_GPT_OSS_20B,\n ModelType.NEBIUS_GLM_4_5,\n ModelType.NEBIUS_DEEPSEEK_V3,\n ModelType.NEBIUS_DEEPSEEK_R1,\n ModelType.NEBIUS_LLAMA_3_1_70B,\n ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n }\n\n @property\n def is_cometapi(self) -> bool:\n r\"\"\"Returns whether this type of models is served by CometAPI.\"\"\"\n return self in {\n ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n ModelType.COMETAPI_CHATGPT_4O_LATEST,\n ModelType.COMETAPI_GPT_5_MINI,\n ModelType.COMETAPI_GPT_5_NANO,\n ModelType.COMETAPI_GPT_5,\n ModelType.COMETAPI_GPT_4_1,\n ModelType.COMETAPI_GPT_4O_MINI,\n ModelType.COMETAPI_O4_MINI_2025_04_16,\n ModelType.COMETAPI_O3_PRO_2025_06_10,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n ModelType.COMETAPI_GEMINI_2_5_PRO,\n ModelType.COMETAPI_GEMINI_2_5_FLASH,\n ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n ModelType.COMETAPI_GEMINI_2_0_FLASH,\n ModelType.COMETAPI_GROK_4_0709,\n ModelType.COMETAPI_GROK_3,\n ModelType.COMETAPI_GROK_3_MINI,\n ModelType.COMETAPI_GROK_2_IMAGE_1212,\n ModelType.COMETAPI_DEEPSEEK_V3_1,\n ModelType.COMETAPI_DEEPSEEK_V3,\n ModelType.COMETAPI_DEEPSEEK_R1_0528,\n ModelType.COMETAPI_DEEPSEEK_CHAT,\n ModelType.COMETAPI_DEEPSEEK_REASONER,\n ModelType.COMETAPI_QWEN3_30B_A3B,\n ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n }\n\n @property\n def is_openrouter(self) -> bool:\n r\"\"\"Returns whether this type of models is served by OpenRouter.\"\"\"\n return self in {\n ModelType.OPENROUTER_LLAMA_3_1_405B,\n ModelType.OPENROUTER_LLAMA_3_1_70B,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n ModelType.OPENROUTER_LLAMA_4_SCOUT,\n ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n ModelType.OPENROUTER_OLYMPICODER_7B,\n ModelType.OPENROUTER_HORIZON_ALPHA,\n }\n\n @property\n def is_orcarouter(self) -> bool:\n r\"\"\"Returns whether this type of models is served by OrcaRouter.\"\"\"\n return self in {\n ModelType.ORCAROUTER_AUTO,\n ModelType.ORCAROUTER_GPT_5,\n ModelType.ORCAROUTER_CLAUDE_OPUS_4_7,\n ModelType.ORCAROUTER_GEMINI_3_FLASH_PREVIEW,\n ModelType.ORCAROUTER_GROK_4_3,\n }\n\n @property\n def is_lmstudio(self) -> bool:\n r\"\"\"Returns whether this type of models is served by LMStudio.\"\"\"\n return self in {\n ModelType.LMSTUDIO_GEMMA_3_1B,\n ModelType.LMSTUDIO_GEMMA_3_4B,\n ModelType.LMSTUDIO_GEMMA_3_12B,\n ModelType.LMSTUDIO_GEMMA_3_27B,\n }\n\n @property\n def is_together(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Together AI.\"\"\"\n return self in {\n ModelType.TOGETHER_LLAMA_3_1_405B,\n ModelType.TOGETHER_LLAMA_3_1_70B,\n ModelType.TOGETHER_LLAMA_3_3_70B,\n ModelType.TOGETHER_LLAMA_3_3_70B,\n ModelType.TOGETHER_MISTRAL_7B,\n ModelType.TOGETHER_MIXTRAL_8_7B,\n }\n\n @property\n def is_sambanova(self) -> bool:\n r\"\"\"Returns whether this type of model is served by SambaNova AI.\"\"\"\n return self in {\n ModelType.SAMBA_LLAMA_3_1_8B,\n ModelType.SAMBA_LLAMA_3_1_70B,\n ModelType.SAMBA_LLAMA_3_1_405B,\n }\n\n @property\n def is_mistral(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Mistral.\"\"\"\n return self in {\n ModelType.MISTRAL_LARGE,\n ModelType.MISTRAL_NEMO,\n ModelType.MISTRAL_CODESTRAL,\n ModelType.MISTRAL_7B,\n ModelType.MISTRAL_MIXTRAL_8x7B,\n ModelType.MISTRAL_MIXTRAL_8x22B,\n ModelType.MISTRAL_CODESTRAL_MAMBA,\n ModelType.MISTRAL_PIXTRAL_12B,\n ModelType.MISTRAL_8B,\n ModelType.MISTRAL_3B,\n ModelType.MISTRAL_MEDIUM_3_1,\n ModelType.MISTRAL_SMALL_3_2,\n ModelType.MAGISTRAL_SMALL_1_2,\n ModelType.MAGISTRAL_MEDIUM_1_2,\n }\n\n @property\n def is_nvidia(self) -> bool:\n r\"\"\"Returns whether this type of models is a NVIDIA model.\"\"\"\n return self in {\n ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n ModelType.NVIDIA_YI_LARGE,\n ModelType.NVIDIA_MISTRAL_LARGE,\n ModelType.NVIDIA_LLAMA3_70B,\n ModelType.NVIDIA_MIXTRAL_8X7B,\n ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n }\n\n @property\n def is_gemini(self) -> bool:\n r\"\"\"Returns whether this type of models is Gemini model.\n\n Returns:\n bool: Whether this type of models is gemini.\n \"\"\"\n return self in {\n ModelType.GEMINI_3_1_PRO,\n ModelType.GEMINI_3_1_FLASH_LITE,\n ModelType.GEMINI_3_PRO,\n ModelType.GEMINI_3_FLASH,\n ModelType.GEMINI_2_5_FLASH,\n ModelType.GEMINI_2_5_PRO,\n ModelType.GEMINI_2_0_FLASH,\n ModelType.GEMINI_2_0_FLASH_EXP,\n ModelType.GEMINI_2_0_FLASH_THINKING,\n ModelType.GEMINI_2_0_PRO_EXP,\n ModelType.GEMINI_2_0_FLASH_LITE,\n ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n }\n\n @property\n def is_reka(self) -> bool:\n r\"\"\"Returns whether this type of models is Reka model.\n\n Returns:\n bool: Whether this type of models is Reka.\n \"\"\"\n return self in {\n ModelType.REKA_CORE,\n ModelType.REKA_EDGE,\n ModelType.REKA_FLASH,\n }\n\n @property\n def is_cohere(self) -> bool:\n r\"\"\"Returns whether this type of models is a Cohere model.\n\n Returns:\n bool: Whether this type of models is Cohere.\n \"\"\"\n return self in {\n ModelType.COHERE_COMMAND_R_PLUS,\n ModelType.COHERE_COMMAND_R,\n ModelType.COHERE_COMMAND_LIGHT,\n ModelType.COHERE_COMMAND,\n ModelType.COHERE_COMMAND_NIGHTLY,\n }\n\n @property\n def is_yi(self) -> bool:\n r\"\"\"Returns whether this type of models is Yi model.\n\n Returns:\n bool: Whether this type of models is Yi.\n \"\"\"\n return self in {\n ModelType.YI_LIGHTNING,\n ModelType.YI_LARGE,\n ModelType.YI_MEDIUM,\n ModelType.YI_LARGE_TURBO,\n ModelType.YI_VISION,\n ModelType.YI_MEDIUM_200K,\n ModelType.YI_SPARK,\n ModelType.YI_LARGE_RAG,\n ModelType.YI_LARGE_FC,\n }\n\n @property\n def is_qwen(self) -> bool:\n return self in {\n ModelType.QWEN_MAX,\n ModelType.QWEN_PLUS,\n ModelType.QWEN_TURBO,\n ModelType.QWEN_LONG,\n ModelType.QWEN_VL_MAX,\n ModelType.QWEN_VL_PLUS,\n ModelType.QWEN_MATH_PLUS,\n ModelType.QWEN_MATH_TURBO,\n ModelType.QWEN_CODER_TURBO,\n ModelType.QWEN_2_5_CODER_32B,\n ModelType.QWEN_2_5_VL_72B,\n ModelType.QWEN_2_5_72B,\n ModelType.QWEN_2_5_32B,\n ModelType.QWEN_2_5_14B,\n ModelType.QWEN_QWQ_32B,\n ModelType.QWEN_QVQ_72B,\n ModelType.QWEN_QWQ_PLUS,\n ModelType.QWEN_PLUS_LATEST,\n ModelType.QWEN_PLUS_2025_04_28,\n ModelType.QWEN_TURBO_LATEST,\n ModelType.QWEN_TURBO_2025_04_28,\n ModelType.QWEN_3_CODER_PLUS,\n }\n\n @property\n def is_minimax(self) -> bool:\n return self in {\n ModelType.MINIMAX_M3,\n ModelType.MINIMAX_M2_7,\n ModelType.MINIMAX_M2_7_HIGHSPEED,\n ModelType.MINIMAX_M2_5,\n ModelType.MINIMAX_M2_1,\n ModelType.MINIMAX_M2_1_LIGHTNING,\n ModelType.MINIMAX_M2,\n }\n\n @property\n def is_deepseek(self) -> bool:\n return self in {\n ModelType.DEEPSEEK_V4_FLASH,\n ModelType.DEEPSEEK_V4_PRO,\n ModelType.DEEPSEEK_CHAT,\n ModelType.DEEPSEEK_REASONER,\n }\n\n @property\n def is_netmind(self) -> bool:\n return self in {\n ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n ModelType.NETMIND_DEEPSEEK_R1,\n ModelType.NETMIND_DEEPSEEK_V3,\n ModelType.NETMIND_DOUBAO_1_5_PRO,\n ModelType.NETMIND_QWQ_32B,\n }\n\n @property\n def is_ppio(self) -> bool:\n return self in {\n ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n ModelType.PPIO_DEEPSEEK_R1_TURBO,\n ModelType.PPIO_DEEPSEEK_V3_TURBO,\n ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_R1,\n ModelType.PPIO_DEEPSEEK_V3,\n ModelType.PPIO_QWEN_2_5_72B,\n ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n ModelType.PPIO_LLAMA_3_3_70B,\n ModelType.PPIO_LLAMA_3_1_70B,\n ModelType.PPIO_YI_1_5_34B_CHAT,\n }\n\n @property\n def is_internlm(self) -> bool:\n return self in {\n ModelType.INTERNLM3_LATEST,\n ModelType.INTERNLM3_8B_INSTRUCT,\n ModelType.INTERNLM2_5_LATEST,\n ModelType.INTERNLM2_PRO_CHAT,\n }\n\n @property\n def is_modelscope(self) -> bool:\n return self in {\n ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n ModelType.MODELSCOPE_QWEN_3_32B,\n ModelType.MODELSCOPE_QWQ_32B,\n ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n ModelType.MODELSCOPE_ERNIE_4_5_VL_28B_A3B_THINKING,\n }\n\n @property\n def is_moonshot(self) -> bool:\n return self in {\n ModelType.MOONSHOT_V1_8K,\n ModelType.MOONSHOT_V1_32K,\n ModelType.MOONSHOT_V1_128K,\n ModelType.MOONSHOT_KIMI_K2,\n ModelType.MOONSHOT_KIMI_K2_5,\n ModelType.MOONSHOT_KIMI_K2_TURBO_PREVIEW,\n ModelType.MOONSHOT_KIMI_K2_THINKING,\n ModelType.MOONSHOT_KIMI_K2_THINKING_TURBO,\n }\n\n @property\n def is_sglang(self) -> bool:\n return self in {\n ModelType.SGLANG_LLAMA_3_1_8B,\n ModelType.SGLANG_LLAMA_3_1_70B,\n ModelType.SGLANG_LLAMA_3_1_405B,\n ModelType.SGLANG_LLAMA_3_2_1B,\n ModelType.SGLANG_MIXTRAL_NEMO,\n ModelType.SGLANG_MISTRAL_7B,\n ModelType.SGLANG_QWEN_2_5_7B,\n ModelType.SGLANG_QWEN_2_5_32B,\n ModelType.SGLANG_QWEN_2_5_72B,\n }\n\n @property\n def is_siliconflow(self) -> bool:\n return self in {\n ModelType.SILICONFLOW_DEEPSEEK_V2_5,\n ModelType.SILICONFLOW_DEEPSEEK_V3,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_V3,\n ModelType.SILICONFLOW_DEEPSEEK_R1,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.SILICONFLOW_INTERN_LM2_5_20B_CHAT,\n ModelType.SILICONFLOW_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_QWQ_32B,\n ModelType.SILICONFLOW_QWEN2_5_72B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_32B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_14B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_7B_INSTRUCT,\n ModelType.SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_RUMINATION_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_0414,\n }\n\n @property\n def is_watsonx(self) -> bool:\n return self in {\n ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n ModelType.WATSONX_MISTRAL_LARGE,\n }\n\n @property\n def is_qianfan(self) -> bool:\n return self in {\n ModelType.ERNIE_X1_TURBO_32K,\n ModelType.ERNIE_X1_32K,\n ModelType.ERNIE_X1_32K_PREVIEW,\n ModelType.ERNIE_4_5_TURBO_128K,\n ModelType.ERNIE_4_5_TURBO_32K,\n ModelType.DEEPSEEK_V3,\n ModelType.DEEPSEEK_R1,\n ModelType.QWEN3_235B_A22B,\n ModelType.ERNIE_5_0_THINKING,\n ModelType.ERNIE_4_5_TURBO_VL,\n }\n\n @property\n def is_novita(self) -> bool:\n return self in {\n ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n ModelType.NOVITA_DEEPSEEK_V3_0324,\n ModelType.NOVITA_QWEN_2_5_V1_72B,\n ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n ModelType.NOVITA_GEMMA_3_27B_IT,\n ModelType.NOVITA_QWEN_32B,\n ModelType.NOVITA_L3_8B_STHENO_V3_2,\n ModelType.NOVITA_MYTHOMAX_L2_13B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.NOVITA_DEEPSEEK_V3,\n ModelType.NOVITA_LLAMA_3_1_8B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.NOVITA_LLAMA_3_3_70B,\n ModelType.NOVITA_QWEN_2_5_72B,\n ModelType.NOVITA_MISTRAL_NEMO,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.NOVITA_LLAMA_3_8B,\n ModelType.NOVITA_WIZARDLM_2_8X22B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n ModelType.NOVITA_LLAMA_3_1_70B,\n ModelType.NOVITA_GEMMA_2_9B_IT,\n ModelType.NOVITA_MISTRAL_7B,\n ModelType.NOVITA_LLAMA_3_70B,\n ModelType.NOVITA_DEEPSEEK_R1,\n ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n ModelType.NOVITA_AIROBOROS_L2_70B,\n ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n ModelType.NOVITA_L3_8B_LUNARIS,\n ModelType.NOVITA_GLM_4_9B_0414,\n ModelType.NOVITA_GLM_Z1_9B_0414,\n ModelType.NOVITA_GLM_Z1_32B_0414,\n ModelType.NOVITA_GLM_4_32B_0414,\n ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n ModelType.NOVITA_QWEN_2_5_7B,\n ModelType.NOVITA_LLAMA_3_2_1B,\n ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n ModelType.NOVITA_LLAMA_3_2_3B,\n ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n }\n\n @property\n def is_crynux(self) -> bool:\n return self in {\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.CRYNUX_QWEN_3_4_B,\n ModelType.CRYNUX_QWEN_3_8_B,\n ModelType.CRYNUX_QWEN_2_5_7B,\n ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n }\n\n @property\n def is_aiml(self) -> bool:\n return self in {\n ModelType.AIML_MIXTRAL_8X7B,\n ModelType.AIML_MISTRAL_7B_INSTRUCT,\n }\n\n @property\n def is_avian(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Avian.\"\"\"\n return self in {\n ModelType.AVIAN_DEEPSEEK_V3_2,\n ModelType.AVIAN_KIMI_K2_5,\n ModelType.AVIAN_GLM_5,\n ModelType.AVIAN_MINIMAX_M2_5,\n }\n\n @property\n def is_atlascloud(self) -> bool:\n r\"\"\"Returns whether this type of models is served by AtlasCloud.\"\"\"\n return self in {\n ModelType.ATLASCLOUD_GPT_OSS_120B,\n ModelType.ATLASCLOUD_GLM_4_7,\n }\n\n @property\n def token_limit(self) -> int:\n r\"\"\"Returns the maximum token limit for a given model.\n\n Returns:\n int: The maximum token limit for the given model.\n \"\"\"\n if self in {\n ModelType.STUB,\n ModelType.REKA_CORE,\n ModelType.REKA_EDGE,\n ModelType.REKA_FLASH,\n ModelType.QWEN_MATH_PLUS,\n ModelType.QWEN_MATH_TURBO,\n ModelType.COHERE_COMMAND,\n ModelType.COHERE_COMMAND_LIGHT,\n ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n ModelType.NOVITA_MYTHOMAX_L2_13B,\n ModelType.NOVITA_AIROBOROS_L2_70B,\n ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n }:\n return 4_096\n elif self in {\n ModelType.CEREBRAS_LLAMA_3_1_8B,\n ModelType.GPT_4,\n ModelType.QWEN_VL_PLUS,\n ModelType.NVIDIA_LLAMA3_70B,\n ModelType.TOGETHER_MISTRAL_7B,\n ModelType.MOONSHOT_V1_8K,\n ModelType.GLM_4_AIRX,\n ModelType.OPENROUTER_OLYMPICODER_7B,\n ModelType.LMSTUDIO_GEMMA_3_1B,\n ModelType.LMSTUDIO_GEMMA_3_4B,\n ModelType.LMSTUDIO_GEMMA_3_12B,\n ModelType.LMSTUDIO_GEMMA_3_27B,\n ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n ModelType.NOVITA_L3_8B_STHENO_V3_2,\n ModelType.NOVITA_LLAMA_3_8B,\n ModelType.NOVITA_GEMMA_2_9B_IT,\n ModelType.NOVITA_LLAMA_3_70B,\n ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n ModelType.NOVITA_L3_8B_LUNARIS,\n ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n }:\n return 8_192\n elif self in {\n ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n }:\n return 14_336\n elif self in {\n ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n }:\n return 16_000\n elif self in {\n ModelType.GPT_3_5_TURBO,\n ModelType.YI_LIGHTNING,\n ModelType.YI_MEDIUM,\n ModelType.YI_LARGE_TURBO,\n ModelType.YI_VISION,\n ModelType.YI_SPARK,\n ModelType.YI_LARGE_RAG,\n ModelType.SAMBA_LLAMA_3_1_8B,\n ModelType.SAMBA_LLAMA_3_1_405B,\n ModelType.GLM_4V_PLUS_0111,\n ModelType.GLM_4V_FLASH,\n ModelType.GLM_ZERO_PREVIEW,\n ModelType.PPIO_YI_1_5_34B_CHAT,\n ModelType.NOVITA_LLAMA_3_1_8B,\n }:\n return 16_384\n elif self in {\n ModelType.NETMIND_DOUBAO_1_5_PRO,\n ModelType.NOVITA_GEMMA_3_27B_IT,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.NOVITA_QWEN_2_5_72B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n ModelType.NOVITA_GLM_4_9B_0414,\n ModelType.NOVITA_GLM_Z1_9B_0414,\n ModelType.NOVITA_GLM_Z1_32B_0414,\n ModelType.NOVITA_GLM_4_32B_0414,\n ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n ModelType.NOVITA_QWEN_2_5_7B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.CRYNUX_QWEN_3_4_B,\n ModelType.CRYNUX_QWEN_3_8_B,\n ModelType.CRYNUX_QWEN_2_5_7B,\n ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n ModelType.ERNIE_X1_TURBO_32K,\n ModelType.ERNIE_X1_32K,\n ModelType.ERNIE_X1_32K_PREVIEW,\n ModelType.ERNIE_4_5_TURBO_32K,\n ModelType.QWEN3_235B_A22B,\n }:\n return 32_000\n elif self in {\n ModelType.MISTRAL_CODESTRAL,\n ModelType.MISTRAL_7B,\n ModelType.MISTRAL_MIXTRAL_8x7B,\n ModelType.YI_LARGE,\n ModelType.YI_LARGE_FC,\n ModelType.QWEN_MAX,\n ModelType.QWEN_VL_MAX,\n ModelType.NVIDIA_YI_LARGE,\n ModelType.NVIDIA_MISTRAL_LARGE,\n ModelType.NVIDIA_MIXTRAL_8X7B,\n ModelType.QWEN_QWQ_32B,\n ModelType.QWEN_QWQ_PLUS,\n ModelType.QWEN_QVQ_72B,\n ModelType.INTERNLM3_8B_INSTRUCT,\n ModelType.INTERNLM3_LATEST,\n ModelType.INTERNLM2_5_LATEST,\n ModelType.INTERNLM2_PRO_CHAT,\n ModelType.TOGETHER_MIXTRAL_8_7B,\n ModelType.SGLANG_MISTRAL_7B,\n ModelType.MOONSHOT_V1_32K,\n ModelType.AIML_MIXTRAL_8X7B,\n ModelType.AIML_MISTRAL_7B_INSTRUCT,\n ModelType.PPIO_QWEN_2_5_72B,\n ModelType.PPIO_LLAMA_3_1_70B,\n ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n ModelType.MODELSCOPE_QWEN_3_32B,\n ModelType.MODELSCOPE_QWQ_32B,\n ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n ModelType.OPENROUTER_LLAMA_3_1_405B,\n ModelType.WATSONX_MISTRAL_LARGE,\n ModelType.NOVITA_QWEN_32B,\n ModelType.NOVITA_LLAMA_3_1_70B,\n ModelType.NOVITA_MISTRAL_7B,\n ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n ModelType.NOVITA_LLAMA_3_2_3B,\n ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n ModelType.SILICONFLOW_INTERN_LM2_5_20B_CHAT,\n ModelType.SILICONFLOW_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_QWQ_32B,\n ModelType.SILICONFLOW_QWEN2_5_72B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_32B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_14B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_7B_INSTRUCT,\n ModelType.SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT,\n }:\n return 32_768\n elif self in {\n ModelType.MISTRAL_MIXTRAL_8x22B,\n ModelType.CEREBRAS_GPT_OSS_120B,\n ModelType.CEREBRAS_LLAMA_3_3_70B,\n ModelType.CEREBRAS_QWEN_3_32B,\n ModelType.PPIO_DEEPSEEK_R1_TURBO,\n ModelType.PPIO_DEEPSEEK_V3_TURBO,\n ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_R1,\n ModelType.PPIO_DEEPSEEK_V3,\n ModelType.AWS_DEEPSEEK_R1,\n ModelType.NETMIND_QWQ_32B,\n ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n ModelType.NOVITA_DEEPSEEK_V3,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.NOVITA_DEEPSEEK_R1,\n ModelType.SILICONFLOW_DEEPSEEK_V2_5,\n ModelType.SILICONFLOW_DEEPSEEK_V3,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_V3,\n ModelType.SILICONFLOW_DEEPSEEK_R1,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.GLM_4_1V_THINKING_FLASHX,\n ModelType.GLM_4_1V_THINKING_FLASH,\n ModelType.GLM_4_5V,\n }:\n return 64_000\n elif self in {\n ModelType.NOVITA_WIZARDLM_2_8X22B,\n }:\n return 65_535\n elif self in {\n ModelType.NOVITA_QWEN_2_5_V1_72B,\n ModelType.DEEPSEEK_R1,\n }:\n return 96_000\n elif self in {\n ModelType.DEEPSEEK_CHAT,\n ModelType.DEEPSEEK_REASONER,\n ModelType.GPT_4O,\n ModelType.GPT_4O_MINI,\n ModelType.GPT_4_TURBO,\n ModelType.O1_PREVIEW,\n ModelType.O1_MINI,\n ModelType.GPT_4_5_PREVIEW,\n ModelType.MISTRAL_LARGE,\n ModelType.MISTRAL_NEMO,\n ModelType.MISTRAL_PIXTRAL_12B,\n ModelType.MISTRAL_8B,\n ModelType.MISTRAL_3B,\n ModelType.MISTRAL_SMALL_3_2,\n ModelType.MAGISTRAL_SMALL_1_2,\n ModelType.QWEN_2_5_CODER_32B,\n ModelType.QWEN_2_5_VL_72B,\n ModelType.QWEN_2_5_72B,\n ModelType.QWEN_2_5_32B,\n ModelType.QWEN_2_5_14B,\n ModelType.COHERE_COMMAND_R,\n ModelType.COHERE_COMMAND_R_PLUS,\n ModelType.COHERE_COMMAND_NIGHTLY,\n ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n ModelType.GROQ_LLAMA_3_3_70B,\n ModelType.SAMBA_LLAMA_3_1_70B,\n ModelType.SGLANG_LLAMA_3_1_8B,\n ModelType.SGLANG_LLAMA_3_1_70B,\n ModelType.SGLANG_LLAMA_3_1_405B,\n ModelType.SGLANG_LLAMA_3_2_1B,\n ModelType.SGLANG_MIXTRAL_NEMO,\n ModelType.MOONSHOT_V1_128K,\n ModelType.GLM_4_PLUS,\n ModelType.GLM_4_AIR,\n ModelType.GLM_4_AIR_0111,\n ModelType.GLM_4_FLASHX,\n ModelType.GLM_4_FLASH,\n ModelType.GLM_4_FLASHX_250414,\n ModelType.GLM_4_FLASH_250414,\n ModelType.GLM_4_5_AIR,\n ModelType.GLM_4_5_AIRX,\n ModelType.GLM_4_5_FLASH,\n ModelType.GLM_3_TURBO,\n ModelType.GLM_4,\n ModelType.GLM_4_6V,\n ModelType.GLM_4_6V_FLASHX,\n ModelType.GLM_4_6V_FLASH,\n ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n ModelType.NETMIND_DEEPSEEK_R1,\n ModelType.NETMIND_DEEPSEEK_V3,\n ModelType.NOVITA_DEEPSEEK_V3_0324,\n ModelType.MISTRAL_MEDIUM_3_1,\n ModelType.MAGISTRAL_MEDIUM_1_2,\n ModelType.ERNIE_4_5_TURBO_128K,\n ModelType.DEEPSEEK_V3,\n ModelType.NEBIUS_GLM_4_5,\n ModelType.NEBIUS_DEEPSEEK_V3,\n ModelType.NEBIUS_DEEPSEEK_R1,\n ModelType.NEBIUS_GPT_OSS_120B,\n ModelType.ATLASCLOUD_GPT_OSS_120B,\n ModelType.NEBIUS_GPT_OSS_20B,\n ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n ModelType.COMETAPI_CHATGPT_4O_LATEST,\n ModelType.COMETAPI_GPT_5_MINI,\n ModelType.COMETAPI_GPT_5_NANO,\n ModelType.COMETAPI_GPT_5,\n ModelType.COMETAPI_GPT_4_1,\n ModelType.COMETAPI_GPT_4O_MINI,\n ModelType.COMETAPI_O4_MINI_2025_04_16,\n ModelType.COMETAPI_O3_PRO_2025_06_10,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n ModelType.COMETAPI_GEMINI_2_5_PRO,\n ModelType.COMETAPI_GEMINI_2_5_FLASH,\n ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n ModelType.COMETAPI_GEMINI_2_0_FLASH,\n ModelType.COMETAPI_GROK_4_0709,\n ModelType.COMETAPI_GROK_3,\n ModelType.COMETAPI_GROK_3_MINI,\n ModelType.COMETAPI_GROK_2_IMAGE_1212,\n ModelType.COMETAPI_DEEPSEEK_V3_1,\n ModelType.COMETAPI_DEEPSEEK_V3,\n ModelType.COMETAPI_DEEPSEEK_R1_0528,\n ModelType.COMETAPI_DEEPSEEK_CHAT,\n ModelType.COMETAPI_DEEPSEEK_REASONER,\n ModelType.COMETAPI_QWEN3_30B_A3B,\n ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_RUMINATION_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_0414,\n ModelType.MODELSCOPE_ERNIE_4_5_VL_28B_A3B_THINKING,\n ModelType.ERNIE_5_0_THINKING,\n ModelType.ERNIE_4_5_TURBO_VL,\n ModelType.GLM_4_5_AIR,\n ModelType.GLM_4_5_AIRX,\n ModelType.ORCAROUTER_AUTO,\n }:\n return 128_000\n elif self in {\n ModelType.NOVITA_LLAMA_3_2_1B,\n }:\n return 131_000\n elif self in {\n ModelType.GROQ_LLAMA_3_1_8B,\n ModelType.QWEN_PLUS,\n ModelType.QWEN_TURBO,\n ModelType.QWEN_CODER_TURBO,\n ModelType.QWEN_PLUS_LATEST,\n ModelType.QWEN_PLUS_2025_04_28,\n ModelType.QWEN_TURBO_LATEST,\n ModelType.QWEN_TURBO_2025_04_28,\n ModelType.TOGETHER_LLAMA_3_1_8B,\n ModelType.TOGETHER_LLAMA_3_1_70B,\n ModelType.TOGETHER_LLAMA_3_1_405B,\n ModelType.TOGETHER_LLAMA_3_3_70B,\n ModelType.SGLANG_QWEN_2_5_7B,\n ModelType.SGLANG_QWEN_2_5_32B,\n ModelType.SGLANG_QWEN_2_5_72B,\n ModelType.OPENROUTER_LLAMA_3_1_70B,\n ModelType.PPIO_LLAMA_3_3_70B,\n ModelType.OPENROUTER_LLAMA_4_SCOUT,\n ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n ModelType.NOVITA_LLAMA_3_3_70B,\n ModelType.NOVITA_MISTRAL_NEMO,\n ModelType.NEBIUS_LLAMA_3_1_70B,\n ModelType.AVIAN_KIMI_K2_5,\n ModelType.AVIAN_GLM_5,\n }:\n return 131_072\n elif self in {\n ModelType.AVIAN_DEEPSEEK_V3_2,\n }:\n return 164_000\n elif self in {\n ModelType.O1,\n ModelType.O3_MINI,\n ModelType.O3_PRO,\n ModelType.CLAUDE_3_7_SONNET,\n ModelType.CLAUDE_SONNET_4_5,\n ModelType.CLAUDE_OPUS_4_5,\n ModelType.CLAUDE_OPUS_4_6,\n ModelType.CLAUDE_SONNET_4,\n ModelType.CLAUDE_HAIKU_4_5,\n ModelType.CLAUDE_OPUS_4,\n ModelType.CLAUDE_OPUS_4_1,\n ModelType.ATLASCLOUD_GLM_4_7,\n ModelType.YI_MEDIUM_200K,\n ModelType.AWS_CLAUDE_3_5_SONNET,\n ModelType.AWS_CLAUDE_3_HAIKU,\n ModelType.AWS_CLAUDE_3_SONNET,\n ModelType.AWS_CLAUDE_3_7_SONNET,\n ModelType.AWS_CLAUDE_SONNET_4,\n ModelType.AWS_CLAUDE_OPUS_4,\n ModelType.AWS_CLAUDE_OPUS_4_1,\n ModelType.O4_MINI,\n ModelType.O3,\n ModelType.GLM_5_1,\n ModelType.GLM_5,\n ModelType.GLM_4_7,\n ModelType.GLM_4_7_FLASHX,\n ModelType.GLM_4_7_FLASH,\n ModelType.GLM_4_6,\n ModelType.GLM_4_5_FLASH,\n }:\n return 200_000\n elif self in {\n ModelType.MISTRAL_CODESTRAL_MAMBA,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n ModelType.OPENROUTER_HORIZON_ALPHA,\n ModelType.MOONSHOT_KIMI_K2,\n ModelType.MOONSHOT_KIMI_K2_5,\n ModelType.MOONSHOT_KIMI_K2_TURBO_PREVIEW,\n ModelType.MOONSHOT_KIMI_K2_THINKING,\n ModelType.MOONSHOT_KIMI_K2_THINKING_TURBO,\n }:\n return 256_000\n\n elif self in {\n ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n }:\n return 320_000\n elif self in {\n ModelType.GPT_5_1,\n ModelType.GPT_5_2,\n ModelType.GPT_5_MINI,\n ModelType.GPT_5_NANO,\n ModelType.GPT_5_4_MINI,\n ModelType.GPT_5_4_NANO,\n ModelType.ORCAROUTER_GPT_5,\n }:\n return 400_000\n elif self in {\n ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n }:\n return 512_000\n elif self in {\n ModelType.XAI_GROK_4_5,\n }:\n return 500_000\n elif self in {\n ModelType.GEMINI_3_1_PRO,\n ModelType.GEMINI_3_1_FLASH_LITE,\n ModelType.GEMINI_3_PRO,\n ModelType.GEMINI_3_FLASH,\n ModelType.GEMINI_2_5_FLASH,\n ModelType.GEMINI_2_5_PRO,\n ModelType.GEMINI_2_0_FLASH,\n ModelType.GEMINI_2_0_FLASH_EXP,\n ModelType.GEMINI_2_0_FLASH_THINKING,\n ModelType.GEMINI_2_0_FLASH_LITE,\n ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n ModelType.GEMINI_2_0_PRO_EXP, # Not given in doc, assume the same\n ModelType.GLM_4_LONG,\n ModelType.TOGETHER_LLAMA_4_MAVERICK,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n ModelType.AMD_GPT4,\n ModelType.GPT_4_1,\n ModelType.GPT_4_1_MINI,\n ModelType.GPT_4_1_NANO,\n ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n ModelType.AVIAN_MINIMAX_M2_5,\n ModelType.ORCAROUTER_GEMINI_3_FLASH_PREVIEW,\n }:\n return 1_048_576\n elif self in {\n ModelType.QWEN_3_CODER_PLUS,\n ModelType.CLAUDE_SONNET_4_6,\n ModelType.CLAUDE_OPUS_4_7,\n ModelType.CLAUDE_OPUS_4_8,\n ModelType.CLAUDE_FABLE_5,\n ModelType.DEEPSEEK_V4_FLASH,\n ModelType.DEEPSEEK_V4_PRO,\n ModelType.GPT_5_4,\n ModelType.GPT_5_4_PRO,\n ModelType.GPT_5_5,\n ModelType.GPT_5_5_PRO,\n ModelType.GLM_5_2,\n ModelType.MINIMAX_M3,\n ModelType.ORCAROUTER_CLAUDE_OPUS_4_7,\n ModelType.ORCAROUTER_GROK_4_3,\n }:\n return 1_000_000\n elif self in {\n ModelType.QWEN_LONG,\n ModelType.TOGETHER_LLAMA_4_SCOUT,\n }:\n return 10_000_000\n elif self in {\n ModelType.MINIMAX_M2_7,\n ModelType.MINIMAX_M2_7_HIGHSPEED,\n ModelType.MINIMAX_M2_5,\n ModelType.MINIMAX_M2_1,\n ModelType.MINIMAX_M2_1_LIGHTNING,\n ModelType.MINIMAX_M2,\n }:\n return 204_800\n\n else:\n logger.warning(\n f\"Unknown model type {self}, set maximum token limit \"\n f\"to 999_999_999\"\n )\n return 999_999_999\n\n\nclass EmbeddingModelType(Enum):\n TEXT_EMBEDDING_ADA_2 = \"text-embedding-ada-002\"\n TEXT_EMBEDDING_3_SMALL = \"text-embedding-3-small\"\n TEXT_EMBEDDING_3_LARGE = \"text-embedding-3-large\"\n\n JINA_EMBEDDINGS_V3 = \"jina-embeddings-v3\"\n JINA_CLIP_V2 = \"jina-clip-v2\"\n JINA_COLBERT_V2 = \"jina-colbert-v2\"\n JINA_EMBEDDINGS_V2_BASE_CODE = \"jina-embeddings-v2-base-code\"\n\n MISTRAL_EMBED = \"mistral-embed\"\n\n GEMINI_EMBEDDING_EXP = \"gemini-embedding-exp-03-07\"\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n return self in {\n EmbeddingModelType.TEXT_EMBEDDING_ADA_2,\n EmbeddingModelType.TEXT_EMBEDDING_3_SMALL,\n EmbeddingModelType.TEXT_EMBEDDING_3_LARGE,\n }\n\n @property\n def is_jina(self) -> bool:\n r\"\"\"Returns whether this type of models is an Jina model.\"\"\"\n return self in {\n EmbeddingModelType.JINA_EMBEDDINGS_V3,\n EmbeddingModelType.JINA_CLIP_V2,\n EmbeddingModelType.JINA_COLBERT_V2,\n EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n }\n\n @property\n def is_mistral(self) -> bool:\n r\"\"\"Returns whether this type of models is an Mistral-released\n model.\n \"\"\"\n return self in {\n EmbeddingModelType.MISTRAL_EMBED,\n }\n\n @property\n def is_gemini(self) -> bool:\n r\"\"\"Returns whether this type of models is an Gemini-released model.\"\"\"\n return self in {\n EmbeddingModelType.GEMINI_EMBEDDING_EXP,\n }\n\n @property\n def output_dim(self) -> int:\n if self in {\n EmbeddingModelType.JINA_COLBERT_V2,\n }:\n return 128\n elif self in {\n EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n }:\n return 768\n elif self in {\n EmbeddingModelType.JINA_EMBEDDINGS_V3,\n EmbeddingModelType.JINA_CLIP_V2,\n }:\n return 1024\n elif self is EmbeddingModelType.TEXT_EMBEDDING_ADA_2:\n return 1536\n elif self is EmbeddingModelType.TEXT_EMBEDDING_3_SMALL:\n return 1536\n elif self is EmbeddingModelType.TEXT_EMBEDDING_3_LARGE:\n return 3072\n elif self is EmbeddingModelType.MISTRAL_EMBED:\n return 1024\n elif self is EmbeddingModelType.GEMINI_EMBEDDING_EXP:\n return 3072\n else:\n raise ValueError(f\"Unknown model type {self}.\")\n\n\nclass GeminiEmbeddingTaskType(str, Enum):\n r\"\"\"Task types for Gemini embedding models.\n\n For more information, please refer to:\n https://ai.google.dev/gemini-api/docs/embeddings#task-types\n \"\"\"\n\n SEMANTIC_SIMILARITY = \"SEMANTIC_SIMILARITY\"\n CLASSIFICATION = \"CLASSIFICATION\"\n CLUSTERING = \"CLUSTERING\"\n RETRIEVAL_DOCUMENT = \"RETRIEVAL_DOCUMENT\"\n RETRIEVAL_QUERY = \"RETRIEVAL_QUERY\"\n QUESTION_ANSWERING = \"QUESTION_ANSWERING\"\n FACT_VERIFICATION = \"FACT_VERIFICATION\"\n CODE_RETRIEVAL_QUERY = \"CODE_RETRIEVAL_QUERY\"\n\n\nclass TaskType(Enum):\n AI_SOCIETY = \"ai_society\"\n CODE = \"code\"\n MISALIGNMENT = \"misalignment\"\n TRANSLATION = \"translation\"\n EVALUATION = \"evaluation\"\n SOLUTION_EXTRACTION = \"solution_extraction\"\n ROLE_DESCRIPTION = \"role_description\"\n GENERATE_TEXT_EMBEDDING_DATA = \"generate_text_embedding_data\"\n OBJECT_RECOGNITION = \"object_recognition\"\n IMAGE_CRAFT = \"image_craft\"\n MULTI_CONDITION_IMAGE_CRAFT = \"multi_condition_image_craft\"\n DEFAULT = \"default\"\n VIDEO_DESCRIPTION = \"video_description\"\n\n\nclass VectorDistance(Enum):\n r\"\"\"Distance metrics used in a vector database.\"\"\"\n\n DOT = \"dot\"\n r\"\"\"Dot product. https://en.wikipedia.org/wiki/Dot_product\"\"\"\n\n COSINE = \"cosine\"\n r\"\"\"Cosine similarity. https://en.wikipedia.org/wiki/Cosine_similarity\"\"\"\n\n EUCLIDEAN = \"euclidean\"\n r\"\"\"Euclidean distance. https://en.wikipedia.org/wiki/Euclidean_distance\"\"\"\n\n\nclass OpenAIBackendRole(Enum):\n ASSISTANT = \"assistant\"\n SYSTEM = \"system\"\n DEVELOPER = \"developer\"\n USER = \"user\"\n FUNCTION = \"function\"\n TOOL = \"tool\"\n\n\nclass TerminationMode(Enum):\n ANY = \"any\"\n ALL = \"all\"\n\n\nclass OpenAIImageTypeMeta(EnumMeta):\n def __contains__(cls, image_type: object) -> bool:\n try:\n cls(image_type)\n except ValueError:\n return False\n return True\n\n\nclass OpenAIImageType(Enum, metaclass=OpenAIImageTypeMeta):\n r\"\"\"Image types supported by OpenAI vision model.\"\"\"\n\n # https://platform.openai.com/docs/guides/vision\n PNG = \"png\"\n JPEG = \"jpeg\"\n JPG = \"jpg\"\n WEBP = \"webp\"\n GIF = \"gif\"\n\n\nclass OpenAIVisionDetailType(Enum):\n AUTO = \"auto\"\n LOW = \"low\"\n HIGH = \"high\"\n\n\nclass StorageType(Enum):\n MILVUS = \"milvus\"\n QDRANT = \"qdrant\"\n TIDB = \"tidb\"\n\n\nclass OpenAPIName(Enum):\n COURSERA = \"coursera\"\n KLARNA = \"klarna\"\n SPEAK = \"speak\"\n NASA_APOD = \"nasa_apod\"\n BIZTOC = \"biztoc\"\n CREATE_QR_CODE = \"create_qr_code\"\n OUTSCHOOL = \"outschool\"\n WEB_SCRAPER = \"web_scraper\"\n\n\nclass ModelPlatformType(Enum):\n DEFAULT = os.getenv(\"DEFAULT_MODEL_PLATFORM_TYPE\", \"openai\")\n\n OPENAI = \"openai\"\n AWS_BEDROCK = \"aws-bedrock\"\n AWS_BEDROCK_CONVERSE = \"aws-bedrock-converse\"\n AZURE = \"azure\"\n ANTHROPIC = \"anthropic\"\n GROQ = \"groq\"\n NEBIUS = \"nebius\"\n COMETAPI = \"cometapi\"\n OPENROUTER = \"openrouter\"\n ORCAROUTER = \"orcarouter\"\n OLLAMA = \"ollama\"\n LITELLM = \"litellm\"\n LMSTUDIO = \"lmstudio\"\n ZHIPU = \"zhipuai\"\n GEMINI = \"gemini\"\n VLLM = \"vllm\"\n MISTRAL = \"mistral\"\n REKA = \"reka\"\n TOGETHER = \"together\"\n STUB = \"stub\"\n OPENAI_COMPATIBLE_MODEL = \"openai-compatible-model\"\n SAMBA = \"samba-nova\"\n COHERE = \"cohere\"\n YI = \"lingyiwanwu\"\n QWEN = \"tongyi-qianwen\"\n AMD = \"amd\"\n NVIDIA = \"nvidia\"\n DEEPSEEK = \"deepseek\"\n PPIO = \"ppio\"\n SGLANG = \"sglang\"\n INTERNLM = \"internlm\"\n MOONSHOT = \"moonshot\"\n MODELSCOPE = \"modelscope\"\n SILICONFLOW = \"siliconflow\"\n AIML = \"aiml\"\n VOLCANO = \"volcano\"\n NETMIND = \"netmind\"\n NOVITA = \"novita\"\n WATSONX = \"watsonx\"\n QIANFAN = \"qianfan\"\n CRYNUX = \"crynux\"\n AIHUBMIX = \"aihubmix\"\n MINIMAX = \"minimax\"\n CEREBRAS = \"cerebras\"\n FUNCTION_GEMMA = \"function-gemma\"\n AVIAN = \"avian\"\n ATLASCLOUD = \"atlascloud\"\n XAI = \"xai\"\n\n @classmethod\n def from_name(cls, name):\n r\"\"\"Returns the ModelPlatformType enum value from a string.\"\"\"\n for model_platfrom_type in cls:\n if model_platfrom_type.value == name:\n return model_platfrom_type\n raise ValueError(f\"Unknown ModelPlatformType name: {name}\")\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this platform is openai.\"\"\"\n return self is ModelPlatformType.OPENAI\n\n @property\n def is_aws_bedrock(self) -> bool:\n r\"\"\"Returns whether this platform is aws-bedrock.\"\"\"\n return self in (\n ModelPlatformType.AWS_BEDROCK,\n ModelPlatformType.AWS_BEDROCK_CONVERSE,\n )\n\n @property\n def is_azure(self) -> bool:\n r\"\"\"Returns whether this platform is azure.\"\"\"\n return self is ModelPlatformType.AZURE\n\n @property\n def is_anthropic(self) -> bool:\n r\"\"\"Returns whether this platform is anthropic.\"\"\"\n return self is ModelPlatformType.ANTHROPIC\n\n @property\n def is_groq(self) -> bool:\n r\"\"\"Returns whether this platform is groq.\"\"\"\n return self is ModelPlatformType.GROQ\n\n @property\n def is_openrouter(self) -> bool:\n r\"\"\"Returns whether this platform is openrouter.\"\"\"\n return self is ModelPlatformType.OPENROUTER\n\n @property\n def is_orcarouter(self) -> bool:\n r\"\"\"Returns whether this platform is orcarouter.\"\"\"\n return self is ModelPlatformType.ORCAROUTER\n\n @property\n def is_lmstudio(self) -> bool:\n r\"\"\"Returns whether this platform is lmstudio.\"\"\"\n return self is ModelPlatformType.LMSTUDIO\n\n @property\n def is_ollama(self) -> bool:\n r\"\"\"Returns whether this platform is ollama.\"\"\"\n return self is ModelPlatformType.OLLAMA\n\n @property\n def is_vllm(self) -> bool:\n r\"\"\"Returns whether this platform is vllm.\"\"\"\n return self is ModelPlatformType.VLLM\n\n @property\n def is_sglang(self) -> bool:\n r\"\"\"Returns whether this platform is sglang.\"\"\"\n return self is ModelPlatformType.SGLANG\n\n @property\n def is_together(self) -> bool:\n r\"\"\"Returns whether this platform is together.\"\"\"\n return self is ModelPlatformType.TOGETHER\n\n @property\n def is_litellm(self) -> bool:\n r\"\"\"Returns whether this platform is litellm.\"\"\"\n return self is ModelPlatformType.LITELLM\n\n @property\n def is_zhipuai(self) -> bool:\n r\"\"\"Returns whether this platform is zhipu.\"\"\"\n return self is ModelPlatformType.ZHIPU\n\n @property\n def is_mistral(self) -> bool:\n r\"\"\"Returns whether this platform is mistral.\"\"\"\n return self is ModelPlatformType.MISTRAL\n\n @property\n def is_openai_compatible_model(self) -> bool:\n r\"\"\"Returns whether this is a platform supporting openai\n compatibility\"\"\"\n return self is ModelPlatformType.OPENAI_COMPATIBLE_MODEL\n\n @property\n def is_gemini(self) -> bool:\n r\"\"\"Returns whether this platform is Gemini.\"\"\"\n return self is ModelPlatformType.GEMINI\n\n @property\n def is_reka(self) -> bool:\n r\"\"\"Returns whether this platform is Reka.\"\"\"\n return self is ModelPlatformType.REKA\n\n @property\n def is_samba(self) -> bool:\n r\"\"\"Returns whether this platform is Samba Nova.\"\"\"\n return self is ModelPlatformType.SAMBA\n\n @property\n def is_cohere(self) -> bool:\n r\"\"\"Returns whether this platform is Cohere.\"\"\"\n return self is ModelPlatformType.COHERE\n\n @property\n def is_yi(self) -> bool:\n r\"\"\"Returns whether this platform is Yi.\"\"\"\n return self is ModelPlatformType.YI\n\n @property\n def is_qwen(self) -> bool:\n r\"\"\"Returns whether this platform is Qwen.\"\"\"\n return self is ModelPlatformType.QWEN\n\n @property\n def is_nvidia(self) -> bool:\n r\"\"\"Returns whether this platform is Nvidia.\"\"\"\n return self is ModelPlatformType.NVIDIA\n\n @property\n def is_deepseek(self) -> bool:\n r\"\"\"Returns whether this platform is DeepSeek.\"\"\"\n return self is ModelPlatformType.DEEPSEEK\n\n @property\n def is_netmind(self) -> bool:\n r\"\"\"Returns whether this platform is Netmind.\"\"\"\n return self is ModelPlatformType.NETMIND\n\n @property\n def is_ppio(self) -> bool:\n r\"\"\"Returns whether this platform is PPIO.\"\"\"\n return self is ModelPlatformType.PPIO\n\n @property\n def is_internlm(self) -> bool:\n r\"\"\"Returns whether this platform is InternLM.\"\"\"\n return self is ModelPlatformType.INTERNLM\n\n @property\n def is_moonshot(self) -> bool:\n r\"\"\"Returns whether this platform is Moonshot model.\"\"\"\n return self is ModelPlatformType.MOONSHOT\n\n @property\n def is_modelscope(self) -> bool:\n r\"\"\"Returns whether this platform is ModelScope model.\"\"\"\n return self is ModelPlatformType.MODELSCOPE\n\n @property\n def is_siliconflow(self) -> bool:\n r\"\"\"Returns whether this platform is SiliconFlow.\"\"\"\n return self is ModelPlatformType.SILICONFLOW\n\n @property\n def is_aiml(self) -> bool:\n r\"\"\"Returns whether this platform is AIML.\"\"\"\n return self is ModelPlatformType.AIML\n\n @property\n def is_volcano(self) -> bool:\n r\"\"\"Returns whether this platform is volcano.\"\"\"\n return self is ModelPlatformType.VOLCANO\n\n @property\n def is_novita(self) -> bool:\n r\"\"\"Returns whether this platform is Novita.\"\"\"\n return self is ModelPlatformType.NOVITA\n\n @property\n def is_watsonx(self) -> bool:\n r\"\"\"Returns whether this platform is WatsonX.\"\"\"\n return self is ModelPlatformType.WATSONX\n\n @property\n def is_crynux(self) -> bool:\n r\"\"\"Returns whether this platform is Crynux.\"\"\"\n return self is ModelPlatformType.CRYNUX\n\n @property\n def is_aihubmix(self) -> bool:\n r\"\"\"Returns whether this platform is AihubMix.\"\"\"\n return self is ModelPlatformType.AIHUBMIX\n\n @property\n def is_minimax(self) -> bool:\n r\"\"\"Returns whether this platform is Minimax M2.\"\"\"\n return self is ModelPlatformType.MINIMAX\n\n @property\n def is_cerebras(self) -> bool:\n r\"\"\"Returns whether this platform is Cerebras.\"\"\"\n return self is ModelPlatformType.CEREBRAS\n\n @property\n def is_avian(self) -> bool:\n r\"\"\"Returns whether this platform is Avian.\"\"\"\n return self is ModelPlatformType.AVIAN\n\n @property\n def is_atlascloud(self) -> bool:\n r\"\"\"Returns whether this platform is AtlasCloud.\"\"\"\n return self is ModelPlatformType.ATLASCLOUD\n\n\nclass AudioModelType(Enum):\n TTS_1 = \"tts-1\"\n TTS_1_HD = \"tts-1-hd\"\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of audio models is an OpenAI-released\n model.\"\"\"\n return self in {\n AudioModelType.TTS_1,\n AudioModelType.TTS_1_HD,\n }\n\n\nclass VoiceType(Enum):\n ALLOY = \"alloy\"\n ECHO = \"echo\"\n FABLE = \"fable\"\n ONYX = \"onyx\"\n NOVA = \"nova\"\n SHIMMER = \"shimmer\"\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of voice is an OpenAI-released voice.\"\"\"\n return self in {\n VoiceType.ALLOY,\n VoiceType.ECHO,\n VoiceType.FABLE,\n VoiceType.ONYX,\n VoiceType.NOVA,\n VoiceType.SHIMMER,\n }\n\n\nclass JinaReturnFormat(Enum):\n DEFAULT = None\n MARKDOWN = \"markdown\"\n HTML = \"html\"\n TEXT = \"text\"\n\n\nclass JinaRerankerModelType(str, Enum):\n r\"\"\"Model types for Jina AI Reranker.\n\n These models are available through the Jina AI Reranker API for\n re-ranking documents based on their relevance to a query.\n\n For more information, please refer to:\n https://jina.ai/reranker/\n \"\"\"\n\n JINA_RERANKER_V2_BASE_MULTILINGUAL = \"jina-reranker-v2-base-multilingual\"\n r\"\"\"Multilingual reranker model supporting 100+ languages. (Default)\"\"\"\n\n JINA_RERANKER_V1_BASE_EN = \"jina-reranker-v1-base-en\"\n r\"\"\"English base reranker model.\"\"\"\n\n JINA_RERANKER_V1_TINY_EN = \"jina-reranker-v1-tiny-en\"\n r\"\"\"Lightweight English reranker model for faster inference.\"\"\"\n\n JINA_RERANKER_V1_TURBO_EN = \"jina-reranker-v1-turbo-en\"\n r\"\"\"High-performance English reranker model.\"\"\"\n\n JINA_COLBERT_V2 = \"jina-colbert-v2\"\n r\"\"\"ColBERT-based reranker for token-level matching.\"\"\"\n\n JINA_RERANKER_V3 = \"jina-reranker-v3\"\n r\"\"\"Latest multilingual reranker with 131K context window (0.6B params).\"\"\"\n\n\nclass HuggingFaceRepoType(str, Enum):\n DATASET = \"dataset\"\n MODEL = \"model\"\n SPACE = \"space\"\n"}, "f56cda207888a682468065792f66c119621d38bc": {"t": 1785846847.5082011, "n": 84094, "h": "e675af24e40ff06a", "c": "# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\nimport os\nfrom enum import Enum, EnumMeta\nfrom typing import Union, cast\n\nfrom camel.logger import get_logger\nfrom camel.types.unified_model_type import UnifiedModelType\n\nlogger = get_logger(__name__)\n\n\nclass RoleType(Enum):\n ASSISTANT = \"assistant\"\n USER = \"user\"\n SYSTEM = \"system\"\n CRITIC = \"critic\"\n EMBODIMENT = \"embodiment\"\n DEFAULT = \"default\"\n\n\nclass ModelType(UnifiedModelType, Enum):\n DEFAULT = os.getenv(\"DEFAULT_MODEL_TYPE\", \"gpt-4.1-mini-2025-04-14\")\n\n GPT_3_5_TURBO = \"gpt-3.5-turbo\"\n GPT_4 = \"gpt-4\"\n GPT_4_TURBO = \"gpt-4-turbo\"\n GPT_4O = \"gpt-4o\"\n GPT_4O_MINI = \"gpt-4o-mini\"\n GPT_4_5_PREVIEW = \"gpt-4.5-preview\"\n O1 = \"o1\"\n O1_PREVIEW = \"o1-preview\"\n O1_MINI = \"o1-mini\"\n O3_MINI = \"o3-mini\"\n GPT_4_1 = \"gpt-4.1\"\n GPT_4_1_MINI = \"gpt-4.1-mini-2025-04-14\"\n GPT_4_1_NANO = \"gpt-4.1-nano-2025-04-14\"\n O4_MINI = \"o4-mini\"\n O3 = \"o3\"\n O3_PRO = \"o3-pro\"\n GPT_5_1 = \"gpt-5.1\"\n GPT_5_2 = \"gpt-5.2\"\n GPT_5_4 = \"gpt-5.4\"\n GPT_5_4_MINI = \"gpt-5.4-mini-2026-03-17\"\n GPT_5_4_NANO = \"gpt-5.4-nano-2026-03-17\"\n GPT_5_4_PRO = \"gpt-5.5-pro-2026-04-23\"\n GPT_5_5 = \"gpt-5.5\"\n GPT_5_5_PRO = \"gpt-5.5-pro-2026-04-23\"\n GPT_5 = \"gpt-5\"\n GPT_5_MINI = \"gpt-5-mini\"\n GPT_5_NANO = \"gpt-5-nano\"\n\n AWS_CLAUDE_3_7_SONNET = \"anthropic.claude-3-7-sonnet-20250219-v1:0\"\n AWS_CLAUDE_3_5_SONNET = \"anthropic.claude-3-5-sonnet-20241022-v2:0\"\n AWS_CLAUDE_3_HAIKU = \"anthropic.claude-3-haiku-20240307-v1:0\"\n AWS_CLAUDE_3_SONNET = \"anthropic.claude-3-sonnet-20240229-v1:0\"\n AWS_DEEPSEEK_R1 = \"us.deepseek.r1-v1:0\"\n AWS_LLAMA_3_3_70B_INSTRUCT = \"us.meta.llama3-3-70b-instruct-v1:0\"\n AWS_LLAMA_3_2_90B_INSTRUCT = \"us.meta.llama3-2-90b-instruct-v1:0\"\n AWS_LLAMA_3_2_11B_INSTRUCT = \"us.meta.llama3-2-11b-instruct-v1:0\"\n AWS_CLAUDE_SONNET_4 = \"anthropic.claude-sonnet-4-20250514-v1:0\"\n AWS_CLAUDE_OPUS_4 = \"anthropic.claude-opus-4-20250514-v1:0\"\n AWS_CLAUDE_OPUS_4_1 = \"anthropic.claude-opus-4-1-20250805-v1:0\"\n\n AMD_GPT4 = \"dvue-aoai-001-gpt-4.1\"\n\n # ZhipuAI GLM-5 series (latest)\n GLM_5_2 = \"glm-5.2\"\n GLM_5_1 = \"glm-5.1\"\n GLM_5 = \"glm-5\"\n GLM_4_7 = \"glm-4.7\"\n GLM_4_7_FLASHX = \"glm-4.7-flashx\"\n GLM_4_7_FLASH = \"glm-4.7-flash\"\n GLM_4_6 = \"glm-4.6\"\n GLM_4 = \"glm-4\"\n GLM_4_5_AIR = \"glm-4.5-air\"\n GLM_4_5_AIRX = \"glm-4.5-airx\"\n GLM_4_5_FLASH = \"glm-4.5-flash\"\n GLM_4V = \"glm-4v\"\n GLM_4_1V_THINKING_FLASHX = \"glm-4.1v-thinking-flashx\"\n GLM_4_1V_THINKING_FLASH = \"glm-4.1v-thinking-flash\"\n GLM_4V_FLASH = \"glm-4v-flash\"\n GLM_4V_PLUS_0111 = \"glm-4v-plus-0111\"\n GLM_4_PLUS = \"glm-4-plus\"\n GLM_4_AIR = \"glm-4-air\"\n GLM_4_AIR_0111 = \"glm-4-air-0111\"\n GLM_4_AIRX = \"glm-4-airx\"\n GLM_4_LONG = \"glm-4-long\"\n GLM_4_FLASHX = \"glm-4-flashx\"\n GLM_4_FLASHX_250414 = \"glm-4-flashx-250414\"\n GLM_4_FLASH = \"glm-4-flash\"\n GLM_4_FLASH_250414 = \"glm-4-flash-250414\"\n GLM_ZERO_PREVIEW = \"glm-zero-preview\"\n GLM_3_TURBO = \"glm-3-turbo\"\n\n # ZhipuAI GLM Vision models (VLM)\n GLM_4_6V = \"glm-4.6v\"\n GLM_4_6V_FLASHX = \"glm-4.6v-flashx\"\n GLM_4_5V = \"glm-4.5v\"\n GLM_4_6V_FLASH = \"glm-4.6v-flash\"\n\n # Groq platform models\n GROQ_LLAMA_3_1_8B = \"llama-3.1-8b-instant\"\n GROQ_LLAMA_3_3_70B = \"llama-3.3-70b-versatile\"\n\n # xAI platform models\n XAI_GROK_4_5 = \"grok-4.5\"\n\n # Cerebras platform models\n CEREBRAS_GPT_OSS_120B = \"gpt-oss-120b\"\n CEREBRAS_LLAMA_3_1_8B = \"llama3.1-8b\"\n CEREBRAS_LLAMA_3_3_70B = \"llama3.3-70b\"\n CEREBRAS_QWEN_3_32B = \"qwen-3-32b\"\n\n # Nebius AI Studio platform models\n NEBIUS_GPT_OSS_120B = \"gpt-oss-120b\"\n NEBIUS_GPT_OSS_20B = \"gpt-oss-20b\"\n NEBIUS_GLM_4_5 = \"GLM-4.5\"\n NEBIUS_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n NEBIUS_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n NEBIUS_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n NEBIUS_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.3\"\n\n # CometAPI platform models\n COMETAPI_GPT_5_CHAT_LATEST = \"gpt-5-chat-latest\"\n COMETAPI_CHATGPT_4O_LATEST = \"chatgpt-4o-latest\"\n COMETAPI_GPT_5_MINI = \"gpt-5-mini\"\n COMETAPI_GPT_5_NANO = \"gpt-5-nano\"\n COMETAPI_GPT_5 = \"gpt-5\"\n COMETAPI_GPT_4_1 = \"gpt-4.1\"\n COMETAPI_GPT_4O_MINI = \"gpt-4o-mini\"\n COMETAPI_O4_MINI_2025_04_16 = \"o4-mini-2025-04-16\"\n COMETAPI_O3_PRO_2025_06_10 = \"o3-pro-2025-06-10\"\n COMETAPI_CLAUDE_OPUS_4_1_20250805 = \"claude-opus-4-1-20250805\"\n COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING = (\n \"claude-opus-4-1-20250805-thinking\"\n )\n COMETAPI_CLAUDE_SONNET_4_20250514 = \"claude-sonnet-4-20250514\"\n COMETAPI_CLAUDE_SONNET_4_20250514_THINKING = (\n \"claude-sonnet-4-20250514-thinking\"\n )\n COMETAPI_CLAUDE_3_7_SONNET_LATEST = \"claude-3-7-sonnet-latest\"\n COMETAPI_CLAUDE_3_5_HAIKU_LATEST = \"claude-3-5-haiku-latest\"\n COMETAPI_GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n COMETAPI_GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n COMETAPI_GEMINI_2_5_FLASH_LITE = \"gemini-2.5-flash-lite\"\n COMETAPI_GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n COMETAPI_GROK_4_0709 = \"grok-4-0709\"\n COMETAPI_GROK_3 = \"grok-3\"\n COMETAPI_GROK_3_MINI = \"grok-3-mini\"\n COMETAPI_GROK_2_IMAGE_1212 = \"grok-2-image-1212\"\n COMETAPI_DEEPSEEK_V3_1 = \"deepseek-v3.1\"\n COMETAPI_DEEPSEEK_V3 = \"deepseek-v3\"\n COMETAPI_DEEPSEEK_R1_0528 = \"deepseek-r1-0528\"\n COMETAPI_DEEPSEEK_CHAT = \"deepseek-chat\"\n COMETAPI_DEEPSEEK_REASONER = \"deepseek-reasoner\"\n COMETAPI_QWEN3_30B_A3B = \"qwen3-30b-a3b\"\n COMETAPI_QWEN3_CODER_PLUS_2025_07_22 = \"qwen3-coder-plus-2025-07-22\"\n\n # OpenRouter models\n OPENROUTER_LLAMA_3_1_405B = \"meta-llama/llama-3.1-405b-instruct\"\n OPENROUTER_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n OPENROUTER_LLAMA_4_MAVERICK = \"meta-llama/llama-4-maverick\"\n OPENROUTER_LLAMA_4_MAVERICK_FREE = \"meta-llama/llama-4-maverick:free\"\n OPENROUTER_LLAMA_4_SCOUT = \"meta-llama/llama-4-scout\"\n OPENROUTER_LLAMA_4_SCOUT_FREE = \"meta-llama/llama-4-scout:free\"\n OPENROUTER_OLYMPICODER_7B = \"open-r1/olympiccoder-7b:free\"\n OPENROUTER_HORIZON_ALPHA = \"openrouter/horizon-alpha\"\n\n ORCAROUTER_AUTO = \"orcarouter/auto\"\n ORCAROUTER_GPT_5 = \"openai/gpt-5\"\n ORCAROUTER_CLAUDE_OPUS_4_7 = \"anthropic/claude-opus-4.7\"\n ORCAROUTER_GEMINI_3_FLASH_PREVIEW = \"google/gemini-3-flash-preview\"\n ORCAROUTER_GROK_4_3 = \"grok/grok-4.3\"\n\n # LMStudio models\n LMSTUDIO_GEMMA_3_1B = \"gemma-3-1b\"\n LMSTUDIO_GEMMA_3_4B = \"gemma-3-4b\"\n LMSTUDIO_GEMMA_3_12B = \"gemma-3-12b\"\n LMSTUDIO_GEMMA_3_27B = \"gemma-3-27b\"\n\n # TogetherAI platform models support tool calling\n TOGETHER_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\"\n TOGETHER_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\"\n TOGETHER_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\"\n TOGETHER_LLAMA_3_3_70B = \"meta-llama/Llama-3.3-70B-Instruct-Turbo\"\n TOGETHER_MIXTRAL_8_7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n TOGETHER_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.1\"\n TOGETHER_LLAMA_4_MAVERICK = (\n \"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\"\n )\n TOGETHER_LLAMA_4_SCOUT = \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n\n # PPIO platform models support tool calling\n PPIO_DEEPSEEK_PROVER_V2_671B = \"deepseek/deepseek-prover-v2-671b\"\n PPIO_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n PPIO_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n PPIO_DEEPSEEK_R1_COMMUNITY = \"deepseek/deepseek-r1/community\"\n PPIO_DEEPSEEK_V3_COMMUNITY = \"deepseek/deepseek-v3/community\"\n PPIO_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n PPIO_DEEPSEEK_V3 = \"deepseek/deepseek-v3\"\n PPIO_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n PPIO_BAICHUAN_2_13B_CHAT = \"baichuan/baichuan2-13b-chat\"\n PPIO_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n PPIO_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n PPIO_YI_1_5_34B_CHAT = \"01-ai/yi-1.5-34b-chat\"\n\n # SambaNova Cloud platform models support tool calling\n SAMBA_LLAMA_3_1_8B = \"Meta-Llama-3.1-8B-Instruct\"\n SAMBA_LLAMA_3_1_70B = \"Meta-Llama-3.1-70B-Instruct\"\n SAMBA_LLAMA_3_1_405B = \"Meta-Llama-3.1-405B-Instruct\"\n\n # SGLang models support tool calling\n SGLANG_LLAMA_3_1_8B = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n SGLANG_LLAMA_3_1_70B = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n SGLANG_LLAMA_3_1_405B = \"meta-llama/Meta-Llama-3.1-405B-Instruct\"\n SGLANG_LLAMA_3_2_1B = \"meta-llama/Llama-3.2-1B-Instruct\"\n SGLANG_MIXTRAL_NEMO = \"mistralai/Mistral-Nemo-Instruct-2407\"\n SGLANG_MISTRAL_7B = \"mistralai/Mistral-7B-Instruct-v0.3\"\n SGLANG_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B-Instruct\"\n SGLANG_QWEN_2_5_32B = \"Qwen/Qwen2.5-32B-Instruct\"\n SGLANG_QWEN_2_5_72B = \"Qwen/Qwen2.5-72B-Instruct\"\n\n STUB = \"stub\"\n\n # Claude models\n CLAUDE_3_7_SONNET = \"claude-3-7-sonnet-latest\"\n CLAUDE_SONNET_4_5 = \"claude-sonnet-4-5\"\n CLAUDE_SONNET_4 = \"claude-sonnet-4-20250514\"\n CLAUDE_SONNET_4_6 = \"claude-sonnet-4-6\"\n CLAUDE_HAIKU_4_5 = \"claude-haiku-4-5\"\n CLAUDE_FABLE_5 = \"claude-fable-5\"\n CLAUDE_OPUS_4 = \"claude-opus-4-20250514\"\n CLAUDE_OPUS_4_1 = \"claude-opus-4-1-20250805\"\n CLAUDE_OPUS_4_5 = \"claude-opus-4-5\"\n CLAUDE_OPUS_4_6 = \"claude-opus-4-6\"\n CLAUDE_OPUS_4_7 = \"claude-opus-4-7\"\n CLAUDE_OPUS_4_8 = \"claude-opus-4-8\"\n\n # Netmind models\n NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT = (\n \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n )\n NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT = (\n \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n )\n NETMIND_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n NETMIND_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3-0324\"\n NETMIND_DOUBAO_1_5_PRO = \"doubao/Doubao-1.5-pro\"\n NETMIND_QWQ_32B = \"Qwen/QwQ-32B\"\n\n # Nvidia models\n NVIDIA_NEMOTRON_340B_INSTRUCT = \"nvidia/nemotron-4-340b-instruct\"\n NVIDIA_NEMOTRON_340B_REWARD = \"nvidia/nemotron-4-340b-reward\"\n NVIDIA_YI_LARGE = \"01-ai/yi-large\"\n NVIDIA_MISTRAL_LARGE = \"mistralai/mistral-large\"\n NVIDIA_MIXTRAL_8X7B = \"mistralai/mixtral-8x7b-instruct\"\n NVIDIA_LLAMA3_70B = \"meta/llama3-70b\"\n NVIDIA_LLAMA3_1_8B_INSTRUCT = \"meta/llama-3.1-8b-instruct\"\n NVIDIA_LLAMA3_1_70B_INSTRUCT = \"meta/llama-3.1-70b-instruct\"\n NVIDIA_LLAMA3_1_405B_INSTRUCT = \"meta/llama-3.1-405b-instruct\"\n NVIDIA_LLAMA3_2_1B_INSTRUCT = \"meta/llama-3.2-1b-instruct\"\n NVIDIA_LLAMA3_2_3B_INSTRUCT = \"meta/llama-3.2-3b-instruct\"\n NVIDIA_LLAMA3_3_70B_INSTRUCT = \"meta/llama-3.3-70b-instruct\"\n\n # Gemini models\n GEMINI_3_1_PRO = \"gemini-3.1-pro-preview\"\n GEMINI_3_1_FLASH_LITE = \"gemini-3.1-flash-lite-preview\"\n GEMINI_3_PRO = \"gemini-3-pro-preview\"\n GEMINI_3_FLASH = \"gemini-3-flash-preview\"\n GEMINI_2_5_FLASH = \"gemini-2.5-flash\"\n GEMINI_2_5_PRO = \"gemini-2.5-pro\"\n GEMINI_2_0_FLASH = \"gemini-2.0-flash\"\n GEMINI_2_0_FLASH_EXP = \"gemini-2.0-flash-exp\"\n GEMINI_2_0_FLASH_THINKING = \"gemini-2.0-flash-thinking-exp\"\n GEMINI_2_0_PRO_EXP = \"gemini-2.0-pro-exp-02-05\"\n GEMINI_2_0_FLASH_LITE = \"gemini-2.0-flash-lite\"\n GEMINI_2_0_FLASH_LITE_PREVIEW = \"gemini-2.0-flash-lite-preview-02-05\"\n\n # Mistral AI models\n MISTRAL_3B = \"ministral-3b-latest\"\n MISTRAL_7B = \"open-mistral-7b\"\n MISTRAL_8B = \"ministral-8b-latest\"\n MISTRAL_CODESTRAL = \"codestral-latest\"\n MISTRAL_CODESTRAL_MAMBA = \"open-codestral-mamba\"\n MISTRAL_LARGE = \"mistral-large-latest\"\n MISTRAL_MIXTRAL_8x7B = \"open-mixtral-8x7b\"\n MISTRAL_MIXTRAL_8x22B = \"open-mixtral-8x22b\"\n MISTRAL_NEMO = \"open-mistral-nemo\"\n MISTRAL_PIXTRAL_12B = \"pixtral-12b-2409\"\n MISTRAL_MEDIUM_3_1 = \"mistral-medium-2508\"\n MISTRAL_SMALL_3_2 = \"mistral-small-2506\"\n MAGISTRAL_SMALL_1_2 = \"magistral-small-1.2\"\n MAGISTRAL_MEDIUM_1_2 = \"magistral-medium-1.2\"\n\n # Reka models\n REKA_CORE = \"reka-core\"\n REKA_FLASH = \"reka-flash\"\n REKA_EDGE = \"reka-edge\"\n\n # Cohere models\n COHERE_COMMAND_R_PLUS = \"command-r-plus\"\n COHERE_COMMAND_R = \"command-r\"\n COHERE_COMMAND_LIGHT = \"command-light\"\n COHERE_COMMAND = \"command\"\n COHERE_COMMAND_NIGHTLY = \"command-nightly\"\n\n # Qwen models (Aliyun)\n QWEN_MAX = \"qwen-max\"\n QWEN_PLUS = \"qwen-plus\"\n QWEN_TURBO = \"qwen-turbo\"\n QWEN_PLUS_LATEST = \"qwen-plus-latest\"\n QWEN_PLUS_2025_04_28 = \"qwen-plus-2025-04-28\"\n QWEN_TURBO_LATEST = \"qwen-turbo-latest\"\n QWEN_TURBO_2025_04_28 = \"qwen-turbo-2025-04-28\"\n QWEN_LONG = \"qwen-long\"\n QWEN_VL_MAX = \"qwen-vl-max\"\n QWEN_VL_PLUS = \"qwen-vl-plus\"\n QWEN_MATH_PLUS = \"qwen-math-plus\"\n QWEN_MATH_TURBO = \"qwen-math-turbo\"\n QWEN_CODER_TURBO = \"qwen-coder-turbo\"\n QWEN_2_5_CODER_32B = \"qwen2.5-coder-32b-instruct\"\n QWEN_2_5_VL_72B = \"qwen2.5-vl-72b-instruct\"\n QWEN_2_5_72B = \"qwen2.5-72b-instruct\"\n QWEN_2_5_32B = \"qwen2.5-32b-instruct\"\n QWEN_2_5_14B = \"qwen2.5-14b-instruct\"\n QWEN_QWQ_32B = \"qwq-32b-preview\"\n QWEN_QVQ_72B = \"qvq-72b-preview\"\n QWEN_QWQ_PLUS = \"qwq-plus\"\n QWEN_3_CODER_PLUS = \"qwen3-coder-plus\"\n\n # Yi models (01-ai)\n YI_LIGHTNING = \"yi-lightning\"\n YI_LARGE = \"yi-large\"\n YI_MEDIUM = \"yi-medium\"\n YI_LARGE_TURBO = \"yi-large-turbo\"\n YI_VISION = \"yi-vision\"\n YI_MEDIUM_200K = \"yi-medium-200k\"\n YI_SPARK = \"yi-spark\"\n YI_LARGE_RAG = \"yi-large-rag\"\n YI_LARGE_FC = \"yi-large-fc\"\n\n # DeepSeek models\n DEEPSEEK_V4_FLASH = \"deepseek-v4-flash\"\n DEEPSEEK_V4_PRO = \"deepseek-v4-pro\"\n DEEPSEEK_CHAT = \"deepseek-chat\"\n DEEPSEEK_REASONER = \"deepseek-reasoner\"\n # InternLM models\n INTERNLM3_LATEST = \"internlm3-latest\"\n INTERNLM3_8B_INSTRUCT = \"internlm3-8b-instruct\"\n INTERNLM2_5_LATEST = \"internlm2.5-latest\"\n INTERNLM2_PRO_CHAT = \"internlm2-pro-chat\"\n\n # Moonshot models\n MOONSHOT_V1_8K = \"moonshot-v1-8k\"\n MOONSHOT_V1_32K = \"moonshot-v1-32k\"\n MOONSHOT_V1_128K = \"moonshot-v1-128k\"\n MOONSHOT_KIMI_K2 = \"kimi-k2-0905-Preview\"\n MOONSHOT_KIMI_K2_5 = \"kimi-k2.5\"\n MOONSHOT_KIMI_K2_TURBO_PREVIEW = \"kimi-k2-turbo-preview\"\n MOONSHOT_KIMI_K2_THINKING = \"kimi-k2-thinking\"\n MOONSHOT_KIMI_K2_THINKING_TURBO = \"kimi-k2-thinking-turbo\"\n\n # SiliconFlow models support tool calling\n SILICONFLOW_DEEPSEEK_V2_5 = \"deepseek-ai/DeepSeek-V2.5\"\n SILICONFLOW_DEEPSEEK_V3 = \"deepseek-ai/DeepSeek-V3\"\n SILICONFLOW_PRO_DEEPSEEK_V3 = \"Pro/deepseek-ai/DeepSeek-V3\"\n SILICONFLOW_DEEPSEEK_R1 = \"deepseek-ai/DeepSeek-R1\"\n SILICONFLOW_PRO_DEEPSEEK_R1 = \"Pro/deepseek-ai/DeepSeek-R1\"\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_32B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B\"\n )\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_14B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-14B\"\n )\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n )\n SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n )\n SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n \"Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n )\n SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n \"Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n )\n SILICONFLOW_INTERN_LM2_5_20B_CHAT = \"internlm/internlm2_5-20b-chat\"\n SILICONFLOW_INTERN_LM2_5_7B_CHAT = \"internlm/internlm2_5-7b-chat\"\n SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT = \"Pro/internlm/internlm2_5-7b-chat\"\n SILICONFLOW_QWQ_32B = \"Qwen/QwQ-32B\"\n SILICONFLOW_QWEN2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n SILICONFLOW_QWEN2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n SILICONFLOW_QWEN2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n SILICONFLOW_QWEN2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT = \"Pro/Qwen/Qwen2.5-7B-Instruct\"\n SILICONFLOW_THUDM_GLM_4_9B_CHAT = \"THUDM/glm-4-9b-chat\"\n SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT = \"Pro/THUDM/glm-4-9b-chat\"\n SILICONFLOW_THUDM_GLM_Z1_32B_0414 = \"THUDM/GLM-Z1-32B-0414\"\n SILICONFLOW_THUDM_GLM_4_32B_0414 = \"THUDM/GLM-4-32B-0414\"\n SILICONFLOW_THUDM_GLM_Z1_RUMINATION_32B_0414 = (\n \"THUDM/GLM-Z1-Rumination-32B-0414\"\n )\n SILICONFLOW_THUDM_GLM_4_9B_0414 = \"THUDM/GLM-4-9B-0414\"\n\n # AIML models support tool calling\n AIML_MIXTRAL_8X7B = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n AIML_MISTRAL_7B_INSTRUCT = \"mistralai/Mistral-7B-Instruct-v0.1\"\n\n # Novita platform models support tool calling\n NOVITA_LLAMA_4_MAVERICK_17B = (\n \"meta-llama/llama-4-maverick-17b-128e-instruct-fp8\"\n )\n NOVITA_LLAMA_4_SCOUT_17B = \"meta-llama/llama-4-scout-17b-16e-instruct\"\n NOVITA_DEEPSEEK_V3_0324 = \"deepseek/deepseek-v3-0324\"\n NOVITA_QWEN_2_5_V1_72B = \"qwen/qwen2.5-vl-72b-instruct\"\n NOVITA_DEEPSEEK_V3_TURBO = \"deepseek/deepseek-v3-turbo\"\n NOVITA_DEEPSEEK_R1_TURBO = \"deepseek/deepseek-r1-turbo\"\n NOVITA_GEMMA_3_27B_IT = \"google/gemma-3-27b-it\"\n NOVITA_QWEN_32B = \"qwen/qwq-32b\"\n NOVITA_L3_8B_STHENO_V3_2 = \"Sao10K/L3-8B-Stheno-v3.2\"\n NOVITA_MYTHOMAX_L2_13B = \"gryphe/mythomax-l2-13b\"\n NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n \"deepseek/deepseek-r1-distill-llama-8b\"\n )\n NOVITA_DEEPSEEK_V3 = \"deepseek/deepseek_v3\"\n NOVITA_LLAMA_3_1_8B = \"meta-llama/llama-3.1-8b-instruct\"\n NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B = (\n \"deepseek/deepseek-r1-distill-qwen-14b\"\n )\n NOVITA_LLAMA_3_3_70B = \"meta-llama/llama-3.3-70b-instruct\"\n NOVITA_QWEN_2_5_72B = \"qwen/qwen-2.5-72b-instruct\"\n NOVITA_MISTRAL_NEMO = \"mistralai/mistral-nemo\"\n NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B = (\n \"deepseek/deepseek-r1-distill-qwen-32b\"\n )\n NOVITA_LLAMA_3_8B = \"meta-llama/llama-3-8b-instruct\"\n NOVITA_WIZARDLM_2_8X22B = \"microsoft/wizardlm-2-8x22b\"\n NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B = (\n \"deepseek/deepseek-r1-distill-llama-70b\"\n )\n NOVITA_LLAMA_3_1_70B = \"meta-llama/llama-3.1-70b-instruct\"\n NOVITA_GEMMA_2_9B_IT = \"google/gemma-2-9b-it\"\n NOVITA_MISTRAL_7B = \"mistralai/mistral-7b-instruct\"\n NOVITA_LLAMA_3_70B = \"meta-llama/llama-3-70b-instruct\"\n NOVITA_DEEPSEEK_R1 = \"deepseek/deepseek-r1\"\n NOVITA_HERMES_2_PRO_LLAMA_3_8B = \"nousresearch/hermes-2-pro-llama-3-8b\"\n NOVITA_L3_70B_EURYALE_V2_1 = \"sao10k/l3-70b-euryale-v2.1\"\n NOVITA_DOLPHIN_MIXTRAL_8X22B = (\n \"cognitivecomputations/dolphin-mixtral-8x22b\"\n )\n NOVITA_AIROBOROS_L2_70B = \"jondurbin/airoboros-l2-70b\"\n NOVITA_MIDNIGHT_ROSE_70B = \"sophosympatheia/midnight-rose-70b\"\n NOVITA_L3_8B_LUNARIS = \"sao10k/l3-8b-lunaris\"\n NOVITA_GLM_4_9B_0414 = \"thudm/glm-4-9b-0414\"\n NOVITA_GLM_Z1_9B_0414 = \"thudm/glm-z1-9b-0414\"\n NOVITA_GLM_Z1_32B_0414 = \"thudm/glm-z1-32b-0414\"\n NOVITA_GLM_4_32B_0414 = \"thudm/glm-4-32b-0414\"\n NOVITA_GLM_Z1_RUMINATION_32B_0414 = \"thudm/glm-z1-rumination-32b-0414\"\n NOVITA_QWEN_2_5_7B = \"qwen/qwen2.5-7b-instruct\"\n NOVITA_LLAMA_3_2_1B = \"meta-llama/llama-3.2-1b-instruct\"\n NOVITA_LLAMA_3_2_11B_VISION = \"meta-llama/llama-3.2-11b-vision-instruct\"\n NOVITA_LLAMA_3_2_3B = \"meta-llama/llama-3.2-3b-instruct\"\n NOVITA_LLAMA_3_1_8B_BF16 = \"meta-llama/llama-3.1-8b-instruct-bf16\"\n NOVITA_L31_70B_EURYALE_V2_2 = \"sao10k/l31-70b-euryale-v2.2\"\n\n # ModelScope models support tool calling\n MODELSCOPE_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n MODELSCOPE_QWEN_2_5_14B_INSTRUCT = \"Qwen/Qwen2.5-14B-Instruct\"\n MODELSCOPE_QWEN_2_5_32B_INSTRUCT = \"Qwen/Qwen2.5-32B-Instruct\"\n MODELSCOPE_QWEN_2_5_72B_INSTRUCT = \"Qwen/Qwen2.5-72B-Instruct\"\n MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT = \"Qwen/Qwen2.5-Coder-7B-Instruct\"\n MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT = \"Qwen/Qwen2.5-Coder-14B-Instruct\"\n MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT = \"Qwen/Qwen2.5-Coder-32B-Instruct\"\n MODELSCOPE_QWEN_3_235B_A22B = \"Qwen/Qwen3-235B-A22B\"\n MODELSCOPE_QWEN_3_32B = \"Qwen/Qwen3-32B\"\n MODELSCOPE_QWQ_32B = \"Qwen/QwQ-32B\"\n MODELSCOPE_QWQ_32B_PREVIEW = \"Qwen/QwQ-32B-Preview\"\n MODELSCOPE_LLAMA_3_1_8B_INSTRUCT = (\n \"LLM-Research/Meta-Llama-3.1-8B-Instruct\"\n )\n MODELSCOPE_LLAMA_3_1_70B_INSTRUCT = (\n \"LLM-Research/Meta-Llama-3.1-70B-Instruct\"\n )\n MODELSCOPE_LLAMA_3_1_405B_INSTRUCT = (\n \"LLM-Research/Meta-Llama-3.1-405B-Instruct\"\n )\n MODELSCOPE_LLAMA_3_3_70B_INSTRUCT = \"LLM-Research/Llama-3.3-70B-Instruct\"\n MODELSCOPE_MINISTRAL_8B_INSTRUCT = \"mistralai/Ministral-8B-Instruct-2410\"\n MODELSCOPE_DEEPSEEK_V3_0324 = \"deepseek-ai/DeepSeek-V3-0324\"\n MODELSCOPE_ERNIE_4_5_VL_28B_A3B_THINKING = (\n \"PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Thinking\"\n )\n\n # WatsonX models\n WATSONX_GRANITE_3_8B_INSTRUCT = \"ibm/granite-3-8b-instruct\"\n WATSONX_LLAMA_3_3_70B_INSTRUCT = \"meta-llama/llama-3-3-70b-instruct\"\n WATSONX_LLAMA_3_2_1B_INSTRUCT = \"meta-llama/llama-3-2-1b-instruct\"\n WATSONX_LLAMA_3_2_3B_INSTRUCT = \"meta-llama/llama-3-2-3b-instruct\"\n WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT = (\n \"meta-llama/llama-3-2-11b-vision-instruct\"\n )\n WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT = (\n \"meta-llama/llama-3-2-90b-vision-instruct\"\n )\n WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT = (\n \"meta-llama/llama-guard-3-11b-vision-instruct\"\n )\n WATSONX_MISTRAL_LARGE = \"mistralai/mistral-large\"\n\n # Qianfan models\n ERNIE_X1_TURBO_32K = \"ernie-x1-turbo-32k\"\n ERNIE_X1_32K = \"ernie-x1-32k\"\n ERNIE_X1_32K_PREVIEW = \"ernie-x1-32k-preview\"\n ERNIE_4_5_TURBO_128K = \"ernie-4.5-turbo-128k\"\n ERNIE_4_5_TURBO_32K = \"ernie-4.5-turbo-32k\"\n DEEPSEEK_V3 = \"deepseek-v3\"\n DEEPSEEK_R1 = \"deepseek-r1\"\n QWEN3_235B_A22B = \"qwen3-235b-a22b\"\n ERNIE_5_0_THINKING = \"ernie-5.0-thinking-latest\"\n ERNIE_4_5_TURBO_VL = \"ernie-4.5-turbo-vl-latest\"\n\n # Crynux models\n CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n )\n CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\"\n )\n CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B = (\n \"deepseek-ai/DeepSeek-R1-Distill-Llama-8B\"\n )\n\n CRYNUX_QWEN_3_4_B = \"Qwen/Qwen3-4B\"\n CRYNUX_QWEN_3_8_B = \"Qwen/Qwen3-8B\"\n CRYNUX_QWEN_2_5_7B = \"Qwen/Qwen2.5-7B\"\n CRYNUX_QWEN_2_5_7B_INSTRUCT = \"Qwen/Qwen2.5-7B-Instruct\"\n\n CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B = \"NousResearch/Hermes-3-Llama-3.1-8B\"\n CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B = \"NousResearch/Hermes-3-Llama-3.2-3B\"\n\n # Minimax models\n MINIMAX_M3 = \"MiniMax-M3\"\n MINIMAX_M2_7 = \"MiniMax-M2.7\"\n MINIMAX_M2_7_HIGHSPEED = \"MiniMax-M2.7-highspeed\"\n MINIMAX_M2_5 = \"MiniMax-M2.5\"\n MINIMAX_M2_1 = \"MiniMax-M2.1\"\n MINIMAX_M2_1_LIGHTNING = \"MiniMax-M2.1-lightning\"\n MINIMAX_M2 = \"MiniMax-M2\"\n\n # Avian models\n AVIAN_DEEPSEEK_V3_2 = \"deepseek/deepseek-v3.2\"\n AVIAN_KIMI_K2_5 = \"moonshotai/kimi-k2.5\"\n AVIAN_GLM_5 = \"z-ai/glm-5\"\n AVIAN_MINIMAX_M2_5 = \"minimax/minimax-m2.5\"\n\n # AtlasCloud models\n ATLASCLOUD_GPT_OSS_120B = \"openai/gpt-oss-120b\"\n ATLASCLOUD_GLM_4_7 = \"zai-org/glm-4.7\"\n\n def __str__(self):\n return self.value\n\n def __repr__(self):\n return self.value\n\n def __new__(cls, value: Union[\"ModelType\", str]) -> \"ModelType\":\n return cast(\"ModelType\", UnifiedModelType.__new__(cls, value))\n\n @classmethod\n def from_name(cls, name: str) -> \"ModelType\":\n r\"\"\"Returns the ModelType enum value from a string.\"\"\"\n for model_type in cls:\n if model_type.value == name:\n return model_type\n raise ValueError(f\"Unknown ModelType name: {name}\")\n\n @property\n def value_for_tiktoken(self) -> str:\n if self.is_openai:\n return self.value\n return \"gpt-4o-mini\"\n\n @property\n def support_native_structured_output(self) -> bool:\n return any(\n [\n self.is_openai,\n self.is_xai,\n ]\n )\n\n @property\n def support_native_tool_calling(self) -> bool:\n return any(\n [\n self.is_openai,\n self.is_gemini,\n self.is_mistral,\n self.is_qwen,\n self.is_minimax,\n self.is_deepseek,\n self.is_ppio,\n self.is_cohere,\n self.is_internlm,\n self.is_together,\n self.is_sambanova,\n self.is_groq,\n self.is_cerebras,\n self.is_openrouter,\n self.is_orcarouter,\n self.is_lmstudio,\n self.is_sglang,\n self.is_moonshot,\n self.is_siliconflow,\n self.is_modelscope,\n self.is_zhipuai,\n self.is_aiml,\n self.is_azure_openai,\n self.is_novita,\n self.is_avian,\n self.is_xai,\n ]\n )\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n return self in {\n ModelType.GPT_3_5_TURBO,\n ModelType.GPT_4,\n ModelType.GPT_4_TURBO,\n ModelType.GPT_4O,\n ModelType.GPT_4O_MINI,\n ModelType.O1,\n ModelType.O1_PREVIEW,\n ModelType.O1_MINI,\n ModelType.O3_PRO,\n ModelType.O3_MINI,\n ModelType.GPT_4_5_PREVIEW,\n ModelType.GPT_4_1,\n ModelType.GPT_4_1_MINI,\n ModelType.GPT_4_1_NANO,\n ModelType.GPT_5,\n ModelType.GPT_5_MINI,\n ModelType.GPT_5_NANO,\n ModelType.O4_MINI,\n ModelType.O3,\n ModelType.GPT_5_1,\n ModelType.GPT_5_2,\n ModelType.GPT_5_4,\n ModelType.GPT_5_4_MINI,\n ModelType.GPT_5_4_NANO,\n ModelType.GPT_5_4_PRO,\n ModelType.GPT_5_5,\n ModelType.GPT_5_5_PRO,\n }\n\n @property\n def is_amd(self) -> bool:\n r\"\"\"Returns whether this type of models is a AMD model.\"\"\"\n return self in {\n ModelType.AMD_GPT4,\n }\n\n @property\n def is_aws_bedrock(self) -> bool:\n r\"\"\"Returns whether this type of models is an AWS Bedrock model.\"\"\"\n return self in {\n ModelType.AWS_CLAUDE_3_7_SONNET,\n ModelType.AWS_CLAUDE_3_5_SONNET,\n ModelType.AWS_CLAUDE_3_HAIKU,\n ModelType.AWS_CLAUDE_3_SONNET,\n ModelType.AWS_DEEPSEEK_R1,\n ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n ModelType.AWS_CLAUDE_SONNET_4,\n ModelType.AWS_CLAUDE_OPUS_4,\n ModelType.AWS_CLAUDE_OPUS_4_1,\n }\n\n @property\n def is_azure_openai(self) -> bool:\n r\"\"\"Returns whether this type of models is an OpenAI-released model\n from Azure.\n \"\"\"\n return self in {\n ModelType.GPT_3_5_TURBO,\n ModelType.GPT_4,\n ModelType.GPT_4_TURBO,\n ModelType.GPT_4O,\n ModelType.GPT_4O_MINI,\n ModelType.O1,\n ModelType.O1_PREVIEW,\n ModelType.O1_MINI,\n ModelType.O3_MINI,\n ModelType.O3_PRO,\n ModelType.GPT_4_5_PREVIEW,\n ModelType.GPT_4_1,\n ModelType.GPT_4_1_MINI,\n ModelType.GPT_4_1_NANO,\n ModelType.GPT_5,\n ModelType.O4_MINI,\n ModelType.O3,\n }\n\n @property\n def is_zhipuai(self) -> bool:\n r\"\"\"Returns whether this type of models is a ZhipuAI model.\"\"\"\n return self in {\n ModelType.GLM_3_TURBO,\n ModelType.GLM_4,\n ModelType.GLM_4_5_AIR,\n ModelType.GLM_4_5_AIRX,\n ModelType.GLM_4_5_FLASH,\n ModelType.GLM_4V,\n ModelType.GLM_4_1V_THINKING_FLASHX,\n ModelType.GLM_4_1V_THINKING_FLASH,\n ModelType.GLM_4V_FLASH,\n ModelType.GLM_4V_PLUS_0111,\n ModelType.GLM_4_PLUS,\n ModelType.GLM_4_AIR,\n ModelType.GLM_4_AIR_0111,\n ModelType.GLM_4_AIRX,\n ModelType.GLM_4_LONG,\n ModelType.GLM_4_FLASHX,\n ModelType.GLM_4_FLASHX_250414,\n ModelType.GLM_4_FLASH,\n ModelType.GLM_4_FLASH_250414,\n ModelType.GLM_ZERO_PREVIEW,\n ModelType.GLM_5_2,\n ModelType.GLM_5_1,\n ModelType.GLM_5,\n ModelType.GLM_4_7,\n ModelType.GLM_4_7_FLASHX,\n ModelType.GLM_4_7_FLASH,\n ModelType.GLM_4_6,\n ModelType.GLM_4_6V,\n ModelType.GLM_4_6V_FLASHX,\n ModelType.GLM_4_5V,\n ModelType.GLM_4_6V_FLASH,\n }\n\n @property\n def is_anthropic(self) -> bool:\n r\"\"\"Returns whether this type of models is Anthropic-released model.\n\n Returns:\n bool: Whether this type of models is anthropic.\n \"\"\"\n return self in {\n ModelType.CLAUDE_3_7_SONNET,\n ModelType.CLAUDE_SONNET_4_5,\n ModelType.CLAUDE_SONNET_4_6,\n ModelType.CLAUDE_SONNET_4,\n ModelType.CLAUDE_HAIKU_4_5,\n ModelType.CLAUDE_FABLE_5,\n ModelType.CLAUDE_OPUS_4,\n ModelType.CLAUDE_OPUS_4_1,\n ModelType.CLAUDE_OPUS_4_5,\n ModelType.CLAUDE_OPUS_4_6,\n ModelType.CLAUDE_OPUS_4_7,\n ModelType.CLAUDE_OPUS_4_8,\n }\n\n @property\n def is_groq(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Groq.\"\"\"\n return self in {\n ModelType.GROQ_LLAMA_3_1_8B,\n ModelType.GROQ_LLAMA_3_3_70B,\n }\n\n @property\n def is_xai(self) -> bool:\n r\"\"\"Returns whether this type of models is served by xAI.\"\"\"\n return self in {\n ModelType.XAI_GROK_4_5,\n }\n\n @property\n def is_cerebras(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Cerebras.\"\"\"\n return self in {\n ModelType.CEREBRAS_GPT_OSS_120B,\n ModelType.CEREBRAS_LLAMA_3_1_8B,\n ModelType.CEREBRAS_LLAMA_3_3_70B,\n ModelType.CEREBRAS_QWEN_3_32B,\n }\n\n @property\n def is_nebius(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Nebius AI\n Studio.\"\"\"\n return self in {\n ModelType.NEBIUS_GPT_OSS_120B,\n ModelType.NEBIUS_GPT_OSS_20B,\n ModelType.NEBIUS_GLM_4_5,\n ModelType.NEBIUS_DEEPSEEK_V3,\n ModelType.NEBIUS_DEEPSEEK_R1,\n ModelType.NEBIUS_LLAMA_3_1_70B,\n ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n }\n\n @property\n def is_cometapi(self) -> bool:\n r\"\"\"Returns whether this type of models is served by CometAPI.\"\"\"\n return self in {\n ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n ModelType.COMETAPI_CHATGPT_4O_LATEST,\n ModelType.COMETAPI_GPT_5_MINI,\n ModelType.COMETAPI_GPT_5_NANO,\n ModelType.COMETAPI_GPT_5,\n ModelType.COMETAPI_GPT_4_1,\n ModelType.COMETAPI_GPT_4O_MINI,\n ModelType.COMETAPI_O4_MINI_2025_04_16,\n ModelType.COMETAPI_O3_PRO_2025_06_10,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n ModelType.COMETAPI_GEMINI_2_5_PRO,\n ModelType.COMETAPI_GEMINI_2_5_FLASH,\n ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n ModelType.COMETAPI_GEMINI_2_0_FLASH,\n ModelType.COMETAPI_GROK_4_0709,\n ModelType.COMETAPI_GROK_3,\n ModelType.COMETAPI_GROK_3_MINI,\n ModelType.COMETAPI_GROK_2_IMAGE_1212,\n ModelType.COMETAPI_DEEPSEEK_V3_1,\n ModelType.COMETAPI_DEEPSEEK_V3,\n ModelType.COMETAPI_DEEPSEEK_R1_0528,\n ModelType.COMETAPI_DEEPSEEK_CHAT,\n ModelType.COMETAPI_DEEPSEEK_REASONER,\n ModelType.COMETAPI_QWEN3_30B_A3B,\n ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n }\n\n @property\n def is_openrouter(self) -> bool:\n r\"\"\"Returns whether this type of models is served by OpenRouter.\"\"\"\n return self in {\n ModelType.OPENROUTER_LLAMA_3_1_405B,\n ModelType.OPENROUTER_LLAMA_3_1_70B,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n ModelType.OPENROUTER_LLAMA_4_SCOUT,\n ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n ModelType.OPENROUTER_OLYMPICODER_7B,\n ModelType.OPENROUTER_HORIZON_ALPHA,\n }\n\n @property\n def is_orcarouter(self) -> bool:\n r\"\"\"Returns whether this type of models is served by OrcaRouter.\"\"\"\n return self in {\n ModelType.ORCAROUTER_AUTO,\n ModelType.ORCAROUTER_GPT_5,\n ModelType.ORCAROUTER_CLAUDE_OPUS_4_7,\n ModelType.ORCAROUTER_GEMINI_3_FLASH_PREVIEW,\n ModelType.ORCAROUTER_GROK_4_3,\n }\n\n @property\n def is_lmstudio(self) -> bool:\n r\"\"\"Returns whether this type of models is served by LMStudio.\"\"\"\n return self in {\n ModelType.LMSTUDIO_GEMMA_3_1B,\n ModelType.LMSTUDIO_GEMMA_3_4B,\n ModelType.LMSTUDIO_GEMMA_3_12B,\n ModelType.LMSTUDIO_GEMMA_3_27B,\n }\n\n @property\n def is_together(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Together AI.\"\"\"\n return self in {\n ModelType.TOGETHER_LLAMA_3_1_405B,\n ModelType.TOGETHER_LLAMA_3_1_70B,\n ModelType.TOGETHER_LLAMA_3_3_70B,\n ModelType.TOGETHER_LLAMA_3_3_70B,\n ModelType.TOGETHER_MISTRAL_7B,\n ModelType.TOGETHER_MIXTRAL_8_7B,\n }\n\n @property\n def is_sambanova(self) -> bool:\n r\"\"\"Returns whether this type of model is served by SambaNova AI.\"\"\"\n return self in {\n ModelType.SAMBA_LLAMA_3_1_8B,\n ModelType.SAMBA_LLAMA_3_1_70B,\n ModelType.SAMBA_LLAMA_3_1_405B,\n }\n\n @property\n def is_mistral(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Mistral.\"\"\"\n return self in {\n ModelType.MISTRAL_LARGE,\n ModelType.MISTRAL_NEMO,\n ModelType.MISTRAL_CODESTRAL,\n ModelType.MISTRAL_7B,\n ModelType.MISTRAL_MIXTRAL_8x7B,\n ModelType.MISTRAL_MIXTRAL_8x22B,\n ModelType.MISTRAL_CODESTRAL_MAMBA,\n ModelType.MISTRAL_PIXTRAL_12B,\n ModelType.MISTRAL_8B,\n ModelType.MISTRAL_3B,\n ModelType.MISTRAL_MEDIUM_3_1,\n ModelType.MISTRAL_SMALL_3_2,\n ModelType.MAGISTRAL_SMALL_1_2,\n ModelType.MAGISTRAL_MEDIUM_1_2,\n }\n\n @property\n def is_nvidia(self) -> bool:\n r\"\"\"Returns whether this type of models is a NVIDIA model.\"\"\"\n return self in {\n ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n ModelType.NVIDIA_YI_LARGE,\n ModelType.NVIDIA_MISTRAL_LARGE,\n ModelType.NVIDIA_LLAMA3_70B,\n ModelType.NVIDIA_MIXTRAL_8X7B,\n ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n }\n\n @property\n def is_gemini(self) -> bool:\n r\"\"\"Returns whether this type of models is Gemini model.\n\n Returns:\n bool: Whether this type of models is gemini.\n \"\"\"\n return self in {\n ModelType.GEMINI_3_1_PRO,\n ModelType.GEMINI_3_1_FLASH_LITE,\n ModelType.GEMINI_3_PRO,\n ModelType.GEMINI_3_FLASH,\n ModelType.GEMINI_2_5_FLASH,\n ModelType.GEMINI_2_5_PRO,\n ModelType.GEMINI_2_0_FLASH,\n ModelType.GEMINI_2_0_FLASH_EXP,\n ModelType.GEMINI_2_0_FLASH_THINKING,\n ModelType.GEMINI_2_0_PRO_EXP,\n ModelType.GEMINI_2_0_FLASH_LITE,\n ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n }\n\n @property\n def is_reka(self) -> bool:\n r\"\"\"Returns whether this type of models is Reka model.\n\n Returns:\n bool: Whether this type of models is Reka.\n \"\"\"\n return self in {\n ModelType.REKA_CORE,\n ModelType.REKA_EDGE,\n ModelType.REKA_FLASH,\n }\n\n @property\n def is_cohere(self) -> bool:\n r\"\"\"Returns whether this type of models is a Cohere model.\n\n Returns:\n bool: Whether this type of models is Cohere.\n \"\"\"\n return self in {\n ModelType.COHERE_COMMAND_R_PLUS,\n ModelType.COHERE_COMMAND_R,\n ModelType.COHERE_COMMAND_LIGHT,\n ModelType.COHERE_COMMAND,\n ModelType.COHERE_COMMAND_NIGHTLY,\n }\n\n @property\n def is_yi(self) -> bool:\n r\"\"\"Returns whether this type of models is Yi model.\n\n Returns:\n bool: Whether this type of models is Yi.\n \"\"\"\n return self in {\n ModelType.YI_LIGHTNING,\n ModelType.YI_LARGE,\n ModelType.YI_MEDIUM,\n ModelType.YI_LARGE_TURBO,\n ModelType.YI_VISION,\n ModelType.YI_MEDIUM_200K,\n ModelType.YI_SPARK,\n ModelType.YI_LARGE_RAG,\n ModelType.YI_LARGE_FC,\n }\n\n @property\n def is_qwen(self) -> bool:\n return self in {\n ModelType.QWEN_MAX,\n ModelType.QWEN_PLUS,\n ModelType.QWEN_TURBO,\n ModelType.QWEN_LONG,\n ModelType.QWEN_VL_MAX,\n ModelType.QWEN_VL_PLUS,\n ModelType.QWEN_MATH_PLUS,\n ModelType.QWEN_MATH_TURBO,\n ModelType.QWEN_CODER_TURBO,\n ModelType.QWEN_2_5_CODER_32B,\n ModelType.QWEN_2_5_VL_72B,\n ModelType.QWEN_2_5_72B,\n ModelType.QWEN_2_5_32B,\n ModelType.QWEN_2_5_14B,\n ModelType.QWEN_QWQ_32B,\n ModelType.QWEN_QVQ_72B,\n ModelType.QWEN_QWQ_PLUS,\n ModelType.QWEN_PLUS_LATEST,\n ModelType.QWEN_PLUS_2025_04_28,\n ModelType.QWEN_TURBO_LATEST,\n ModelType.QWEN_TURBO_2025_04_28,\n ModelType.QWEN_3_CODER_PLUS,\n }\n\n @property\n def is_minimax(self) -> bool:\n return self in {\n ModelType.MINIMAX_M3,\n ModelType.MINIMAX_M2_7,\n ModelType.MINIMAX_M2_7_HIGHSPEED,\n ModelType.MINIMAX_M2_5,\n ModelType.MINIMAX_M2_1,\n ModelType.MINIMAX_M2_1_LIGHTNING,\n ModelType.MINIMAX_M2,\n }\n\n @property\n def is_deepseek(self) -> bool:\n return self in {\n ModelType.DEEPSEEK_V4_FLASH,\n ModelType.DEEPSEEK_V4_PRO,\n ModelType.DEEPSEEK_CHAT,\n ModelType.DEEPSEEK_REASONER,\n }\n\n @property\n def is_netmind(self) -> bool:\n return self in {\n ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n ModelType.NETMIND_DEEPSEEK_R1,\n ModelType.NETMIND_DEEPSEEK_V3,\n ModelType.NETMIND_DOUBAO_1_5_PRO,\n ModelType.NETMIND_QWQ_32B,\n }\n\n @property\n def is_ppio(self) -> bool:\n return self in {\n ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n ModelType.PPIO_DEEPSEEK_R1_TURBO,\n ModelType.PPIO_DEEPSEEK_V3_TURBO,\n ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_R1,\n ModelType.PPIO_DEEPSEEK_V3,\n ModelType.PPIO_QWEN_2_5_72B,\n ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n ModelType.PPIO_LLAMA_3_3_70B,\n ModelType.PPIO_LLAMA_3_1_70B,\n ModelType.PPIO_YI_1_5_34B_CHAT,\n }\n\n @property\n def is_internlm(self) -> bool:\n return self in {\n ModelType.INTERNLM3_LATEST,\n ModelType.INTERNLM3_8B_INSTRUCT,\n ModelType.INTERNLM2_5_LATEST,\n ModelType.INTERNLM2_PRO_CHAT,\n }\n\n @property\n def is_modelscope(self) -> bool:\n return self in {\n ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n ModelType.MODELSCOPE_QWEN_3_32B,\n ModelType.MODELSCOPE_QWQ_32B,\n ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n ModelType.MODELSCOPE_ERNIE_4_5_VL_28B_A3B_THINKING,\n }\n\n @property\n def is_moonshot(self) -> bool:\n return self in {\n ModelType.MOONSHOT_V1_8K,\n ModelType.MOONSHOT_V1_32K,\n ModelType.MOONSHOT_V1_128K,\n ModelType.MOONSHOT_KIMI_K2,\n ModelType.MOONSHOT_KIMI_K2_5,\n ModelType.MOONSHOT_KIMI_K2_TURBO_PREVIEW,\n ModelType.MOONSHOT_KIMI_K2_THINKING,\n ModelType.MOONSHOT_KIMI_K2_THINKING_TURBO,\n }\n\n @property\n def is_sglang(self) -> bool:\n return self in {\n ModelType.SGLANG_LLAMA_3_1_8B,\n ModelType.SGLANG_LLAMA_3_1_70B,\n ModelType.SGLANG_LLAMA_3_1_405B,\n ModelType.SGLANG_LLAMA_3_2_1B,\n ModelType.SGLANG_MIXTRAL_NEMO,\n ModelType.SGLANG_MISTRAL_7B,\n ModelType.SGLANG_QWEN_2_5_7B,\n ModelType.SGLANG_QWEN_2_5_32B,\n ModelType.SGLANG_QWEN_2_5_72B,\n }\n\n @property\n def is_siliconflow(self) -> bool:\n return self in {\n ModelType.SILICONFLOW_DEEPSEEK_V2_5,\n ModelType.SILICONFLOW_DEEPSEEK_V3,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_V3,\n ModelType.SILICONFLOW_DEEPSEEK_R1,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.SILICONFLOW_INTERN_LM2_5_20B_CHAT,\n ModelType.SILICONFLOW_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_QWQ_32B,\n ModelType.SILICONFLOW_QWEN2_5_72B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_32B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_14B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_7B_INSTRUCT,\n ModelType.SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_RUMINATION_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_0414,\n }\n\n @property\n def is_watsonx(self) -> bool:\n return self in {\n ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n ModelType.WATSONX_MISTRAL_LARGE,\n }\n\n @property\n def is_qianfan(self) -> bool:\n return self in {\n ModelType.ERNIE_X1_TURBO_32K,\n ModelType.ERNIE_X1_32K,\n ModelType.ERNIE_X1_32K_PREVIEW,\n ModelType.ERNIE_4_5_TURBO_128K,\n ModelType.ERNIE_4_5_TURBO_32K,\n ModelType.DEEPSEEK_V3,\n ModelType.DEEPSEEK_R1,\n ModelType.QWEN3_235B_A22B,\n ModelType.ERNIE_5_0_THINKING,\n ModelType.ERNIE_4_5_TURBO_VL,\n }\n\n @property\n def is_novita(self) -> bool:\n return self in {\n ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n ModelType.NOVITA_DEEPSEEK_V3_0324,\n ModelType.NOVITA_QWEN_2_5_V1_72B,\n ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n ModelType.NOVITA_GEMMA_3_27B_IT,\n ModelType.NOVITA_QWEN_32B,\n ModelType.NOVITA_L3_8B_STHENO_V3_2,\n ModelType.NOVITA_MYTHOMAX_L2_13B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.NOVITA_DEEPSEEK_V3,\n ModelType.NOVITA_LLAMA_3_1_8B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.NOVITA_LLAMA_3_3_70B,\n ModelType.NOVITA_QWEN_2_5_72B,\n ModelType.NOVITA_MISTRAL_NEMO,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.NOVITA_LLAMA_3_8B,\n ModelType.NOVITA_WIZARDLM_2_8X22B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n ModelType.NOVITA_LLAMA_3_1_70B,\n ModelType.NOVITA_GEMMA_2_9B_IT,\n ModelType.NOVITA_MISTRAL_7B,\n ModelType.NOVITA_LLAMA_3_70B,\n ModelType.NOVITA_DEEPSEEK_R1,\n ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n ModelType.NOVITA_AIROBOROS_L2_70B,\n ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n ModelType.NOVITA_L3_8B_LUNARIS,\n ModelType.NOVITA_GLM_4_9B_0414,\n ModelType.NOVITA_GLM_Z1_9B_0414,\n ModelType.NOVITA_GLM_Z1_32B_0414,\n ModelType.NOVITA_GLM_4_32B_0414,\n ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n ModelType.NOVITA_QWEN_2_5_7B,\n ModelType.NOVITA_LLAMA_3_2_1B,\n ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n ModelType.NOVITA_LLAMA_3_2_3B,\n ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n }\n\n @property\n def is_crynux(self) -> bool:\n return self in {\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.CRYNUX_QWEN_3_4_B,\n ModelType.CRYNUX_QWEN_3_8_B,\n ModelType.CRYNUX_QWEN_2_5_7B,\n ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n }\n\n @property\n def is_aiml(self) -> bool:\n return self in {\n ModelType.AIML_MIXTRAL_8X7B,\n ModelType.AIML_MISTRAL_7B_INSTRUCT,\n }\n\n @property\n def is_avian(self) -> bool:\n r\"\"\"Returns whether this type of models is served by Avian.\"\"\"\n return self in {\n ModelType.AVIAN_DEEPSEEK_V3_2,\n ModelType.AVIAN_KIMI_K2_5,\n ModelType.AVIAN_GLM_5,\n ModelType.AVIAN_MINIMAX_M2_5,\n }\n\n @property\n def is_atlascloud(self) -> bool:\n r\"\"\"Returns whether this type of models is served by AtlasCloud.\"\"\"\n return self in {\n ModelType.ATLASCLOUD_GPT_OSS_120B,\n ModelType.ATLASCLOUD_GLM_4_7,\n }\n\n @property\n def token_limit(self) -> int:\n r\"\"\"Returns the maximum token limit for a given model.\n\n Returns:\n int: The maximum token limit for the given model.\n \"\"\"\n if self in {\n ModelType.STUB,\n ModelType.REKA_CORE,\n ModelType.REKA_EDGE,\n ModelType.REKA_FLASH,\n ModelType.QWEN_MATH_PLUS,\n ModelType.QWEN_MATH_TURBO,\n ModelType.COHERE_COMMAND,\n ModelType.COHERE_COMMAND_LIGHT,\n ModelType.NVIDIA_NEMOTRON_340B_INSTRUCT,\n ModelType.NVIDIA_NEMOTRON_340B_REWARD,\n ModelType.NOVITA_MYTHOMAX_L2_13B,\n ModelType.NOVITA_AIROBOROS_L2_70B,\n ModelType.NOVITA_MIDNIGHT_ROSE_70B,\n }:\n return 4_096\n elif self in {\n ModelType.CEREBRAS_LLAMA_3_1_8B,\n ModelType.GPT_4,\n ModelType.QWEN_VL_PLUS,\n ModelType.NVIDIA_LLAMA3_70B,\n ModelType.TOGETHER_MISTRAL_7B,\n ModelType.MOONSHOT_V1_8K,\n ModelType.GLM_4_AIRX,\n ModelType.OPENROUTER_OLYMPICODER_7B,\n ModelType.LMSTUDIO_GEMMA_3_1B,\n ModelType.LMSTUDIO_GEMMA_3_4B,\n ModelType.LMSTUDIO_GEMMA_3_12B,\n ModelType.LMSTUDIO_GEMMA_3_27B,\n ModelType.WATSONX_GRANITE_3_8B_INSTRUCT,\n ModelType.NOVITA_L3_8B_STHENO_V3_2,\n ModelType.NOVITA_LLAMA_3_8B,\n ModelType.NOVITA_GEMMA_2_9B_IT,\n ModelType.NOVITA_LLAMA_3_70B,\n ModelType.NOVITA_HERMES_2_PRO_LLAMA_3_8B,\n ModelType.NOVITA_L3_70B_EURYALE_V2_1,\n ModelType.NOVITA_L3_8B_LUNARIS,\n ModelType.NOVITA_LLAMA_3_1_8B_BF16,\n ModelType.NOVITA_L31_70B_EURYALE_V2_2,\n }:\n return 8_192\n elif self in {\n ModelType.PPIO_BAICHUAN_2_13B_CHAT,\n }:\n return 14_336\n elif self in {\n ModelType.PPIO_DEEPSEEK_PROVER_V2_671B,\n ModelType.NOVITA_DOLPHIN_MIXTRAL_8X22B,\n }:\n return 16_000\n elif self in {\n ModelType.GPT_3_5_TURBO,\n ModelType.YI_LIGHTNING,\n ModelType.YI_MEDIUM,\n ModelType.YI_LARGE_TURBO,\n ModelType.YI_VISION,\n ModelType.YI_SPARK,\n ModelType.YI_LARGE_RAG,\n ModelType.SAMBA_LLAMA_3_1_8B,\n ModelType.SAMBA_LLAMA_3_1_405B,\n ModelType.GLM_4V_PLUS_0111,\n ModelType.GLM_4V_FLASH,\n ModelType.GLM_ZERO_PREVIEW,\n ModelType.PPIO_YI_1_5_34B_CHAT,\n ModelType.NOVITA_LLAMA_3_1_8B,\n }:\n return 16_384\n elif self in {\n ModelType.NETMIND_DOUBAO_1_5_PRO,\n ModelType.NOVITA_GEMMA_3_27B_IT,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.NOVITA_QWEN_2_5_72B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_LLAMA_70B,\n ModelType.NOVITA_GLM_4_9B_0414,\n ModelType.NOVITA_GLM_Z1_9B_0414,\n ModelType.NOVITA_GLM_Z1_32B_0414,\n ModelType.NOVITA_GLM_4_32B_0414,\n ModelType.NOVITA_GLM_Z1_RUMINATION_32B_0414,\n ModelType.NOVITA_QWEN_2_5_7B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.CRYNUX_DEEPSEEK_R1_DISTILL_LLAMA_8B,\n ModelType.CRYNUX_QWEN_3_4_B,\n ModelType.CRYNUX_QWEN_3_8_B,\n ModelType.CRYNUX_QWEN_2_5_7B,\n ModelType.CRYNUX_QWEN_2_5_7B_INSTRUCT,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_1_8B,\n ModelType.CRYNUX_NOUS_HERMES_3_LLAMA_3_2_3B,\n ModelType.ERNIE_X1_TURBO_32K,\n ModelType.ERNIE_X1_32K,\n ModelType.ERNIE_X1_32K_PREVIEW,\n ModelType.ERNIE_4_5_TURBO_32K,\n ModelType.QWEN3_235B_A22B,\n }:\n return 32_000\n elif self in {\n ModelType.MISTRAL_CODESTRAL,\n ModelType.MISTRAL_7B,\n ModelType.MISTRAL_MIXTRAL_8x7B,\n ModelType.YI_LARGE,\n ModelType.YI_LARGE_FC,\n ModelType.QWEN_MAX,\n ModelType.QWEN_VL_MAX,\n ModelType.NVIDIA_YI_LARGE,\n ModelType.NVIDIA_MISTRAL_LARGE,\n ModelType.NVIDIA_MIXTRAL_8X7B,\n ModelType.QWEN_QWQ_32B,\n ModelType.QWEN_QWQ_PLUS,\n ModelType.QWEN_QVQ_72B,\n ModelType.INTERNLM3_8B_INSTRUCT,\n ModelType.INTERNLM3_LATEST,\n ModelType.INTERNLM2_5_LATEST,\n ModelType.INTERNLM2_PRO_CHAT,\n ModelType.TOGETHER_MIXTRAL_8_7B,\n ModelType.SGLANG_MISTRAL_7B,\n ModelType.MOONSHOT_V1_32K,\n ModelType.AIML_MIXTRAL_8X7B,\n ModelType.AIML_MISTRAL_7B_INSTRUCT,\n ModelType.PPIO_QWEN_2_5_72B,\n ModelType.PPIO_LLAMA_3_1_70B,\n ModelType.MODELSCOPE_QWEN_2_5_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_72B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_7B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_14B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_2_5_CODER_32B_INSTRUCT,\n ModelType.MODELSCOPE_QWEN_3_235B_A22B,\n ModelType.MODELSCOPE_QWEN_3_32B,\n ModelType.MODELSCOPE_QWQ_32B,\n ModelType.MODELSCOPE_QWQ_32B_PREVIEW,\n ModelType.MODELSCOPE_LLAMA_3_1_8B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_70B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_1_405B_INSTRUCT,\n ModelType.MODELSCOPE_LLAMA_3_3_70B_INSTRUCT,\n ModelType.MODELSCOPE_MINISTRAL_8B_INSTRUCT,\n ModelType.MODELSCOPE_DEEPSEEK_V3_0324,\n ModelType.OPENROUTER_LLAMA_3_1_405B,\n ModelType.WATSONX_MISTRAL_LARGE,\n ModelType.NOVITA_QWEN_32B,\n ModelType.NOVITA_LLAMA_3_1_70B,\n ModelType.NOVITA_MISTRAL_7B,\n ModelType.NOVITA_LLAMA_3_2_11B_VISION,\n ModelType.NOVITA_LLAMA_3_2_3B,\n ModelType.NEBIUS_MISTRAL_7B_INSTRUCT,\n ModelType.SILICONFLOW_INTERN_LM2_5_20B_CHAT,\n ModelType.SILICONFLOW_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_PRO_INTERN_LM2_5_7B_CHAT,\n ModelType.SILICONFLOW_QWQ_32B,\n ModelType.SILICONFLOW_QWEN2_5_72B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_32B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_14B_INSTRUCT,\n ModelType.SILICONFLOW_QWEN2_5_7B_INSTRUCT,\n ModelType.SILICONFLOW_PRO_QWEN2_5_7B_INSTRUCT,\n }:\n return 32_768\n elif self in {\n ModelType.MISTRAL_MIXTRAL_8x22B,\n ModelType.CEREBRAS_GPT_OSS_120B,\n ModelType.CEREBRAS_LLAMA_3_3_70B,\n ModelType.CEREBRAS_QWEN_3_32B,\n ModelType.PPIO_DEEPSEEK_R1_TURBO,\n ModelType.PPIO_DEEPSEEK_V3_TURBO,\n ModelType.PPIO_DEEPSEEK_R1_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,\n ModelType.PPIO_DEEPSEEK_R1,\n ModelType.PPIO_DEEPSEEK_V3,\n ModelType.AWS_DEEPSEEK_R1,\n ModelType.NETMIND_QWQ_32B,\n ModelType.NOVITA_DEEPSEEK_V3_TURBO,\n ModelType.NOVITA_DEEPSEEK_R1_TURBO,\n ModelType.NOVITA_DEEPSEEK_V3,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.NOVITA_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.NOVITA_DEEPSEEK_R1,\n ModelType.SILICONFLOW_DEEPSEEK_V2_5,\n ModelType.SILICONFLOW_DEEPSEEK_V3,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_V3,\n ModelType.SILICONFLOW_DEEPSEEK_R1,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_32B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_14B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_7B,\n ModelType.SILICONFLOW_PRO_DEEPSEEK_R1_DISTILL_QWEN_1_5B,\n ModelType.GLM_4_1V_THINKING_FLASHX,\n ModelType.GLM_4_1V_THINKING_FLASH,\n ModelType.GLM_4_5V,\n }:\n return 64_000\n elif self in {\n ModelType.NOVITA_WIZARDLM_2_8X22B,\n }:\n return 65_535\n elif self in {\n ModelType.NOVITA_QWEN_2_5_V1_72B,\n ModelType.DEEPSEEK_R1,\n }:\n return 96_000\n elif self in {\n ModelType.DEEPSEEK_CHAT,\n ModelType.DEEPSEEK_REASONER,\n ModelType.GPT_4O,\n ModelType.GPT_4O_MINI,\n ModelType.GPT_4_TURBO,\n ModelType.O1_PREVIEW,\n ModelType.O1_MINI,\n ModelType.GPT_4_5_PREVIEW,\n ModelType.MISTRAL_LARGE,\n ModelType.MISTRAL_NEMO,\n ModelType.MISTRAL_PIXTRAL_12B,\n ModelType.MISTRAL_8B,\n ModelType.MISTRAL_3B,\n ModelType.MISTRAL_SMALL_3_2,\n ModelType.MAGISTRAL_SMALL_1_2,\n ModelType.QWEN_2_5_CODER_32B,\n ModelType.QWEN_2_5_VL_72B,\n ModelType.QWEN_2_5_72B,\n ModelType.QWEN_2_5_32B,\n ModelType.QWEN_2_5_14B,\n ModelType.COHERE_COMMAND_R,\n ModelType.COHERE_COMMAND_R_PLUS,\n ModelType.COHERE_COMMAND_NIGHTLY,\n ModelType.NVIDIA_LLAMA3_1_8B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_70B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_1_405B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_1B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_2_3B_INSTRUCT,\n ModelType.NVIDIA_LLAMA3_3_70B_INSTRUCT,\n ModelType.GROQ_LLAMA_3_3_70B,\n ModelType.SAMBA_LLAMA_3_1_70B,\n ModelType.SGLANG_LLAMA_3_1_8B,\n ModelType.SGLANG_LLAMA_3_1_70B,\n ModelType.SGLANG_LLAMA_3_1_405B,\n ModelType.SGLANG_LLAMA_3_2_1B,\n ModelType.SGLANG_MIXTRAL_NEMO,\n ModelType.MOONSHOT_V1_128K,\n ModelType.GLM_4_PLUS,\n ModelType.GLM_4_AIR,\n ModelType.GLM_4_AIR_0111,\n ModelType.GLM_4_FLASHX,\n ModelType.GLM_4_FLASH,\n ModelType.GLM_4_FLASHX_250414,\n ModelType.GLM_4_FLASH_250414,\n ModelType.GLM_4_5_AIR,\n ModelType.GLM_4_5_AIRX,\n ModelType.GLM_4_5_FLASH,\n ModelType.GLM_3_TURBO,\n ModelType.GLM_4,\n ModelType.GLM_4_6V,\n ModelType.GLM_4_6V_FLASHX,\n ModelType.GLM_4_6V_FLASH,\n ModelType.AWS_LLAMA_3_3_70B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_90B_INSTRUCT,\n ModelType.AWS_LLAMA_3_2_11B_INSTRUCT,\n ModelType.NETMIND_DEEPSEEK_R1,\n ModelType.NETMIND_DEEPSEEK_V3,\n ModelType.NOVITA_DEEPSEEK_V3_0324,\n ModelType.MISTRAL_MEDIUM_3_1,\n ModelType.MAGISTRAL_MEDIUM_1_2,\n ModelType.ERNIE_4_5_TURBO_128K,\n ModelType.DEEPSEEK_V3,\n ModelType.NEBIUS_GLM_4_5,\n ModelType.NEBIUS_DEEPSEEK_V3,\n ModelType.NEBIUS_DEEPSEEK_R1,\n ModelType.NEBIUS_GPT_OSS_120B,\n ModelType.ATLASCLOUD_GPT_OSS_120B,\n ModelType.NEBIUS_GPT_OSS_20B,\n ModelType.COMETAPI_GPT_5_CHAT_LATEST,\n ModelType.COMETAPI_CHATGPT_4O_LATEST,\n ModelType.COMETAPI_GPT_5_MINI,\n ModelType.COMETAPI_GPT_5_NANO,\n ModelType.COMETAPI_GPT_5,\n ModelType.COMETAPI_GPT_4_1,\n ModelType.COMETAPI_GPT_4O_MINI,\n ModelType.COMETAPI_O4_MINI_2025_04_16,\n ModelType.COMETAPI_O3_PRO_2025_06_10,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805,\n ModelType.COMETAPI_CLAUDE_OPUS_4_1_20250805_THINKING,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514,\n ModelType.COMETAPI_CLAUDE_SONNET_4_20250514_THINKING,\n ModelType.COMETAPI_CLAUDE_3_7_SONNET_LATEST,\n ModelType.COMETAPI_CLAUDE_3_5_HAIKU_LATEST,\n ModelType.COMETAPI_GEMINI_2_5_PRO,\n ModelType.COMETAPI_GEMINI_2_5_FLASH,\n ModelType.COMETAPI_GEMINI_2_5_FLASH_LITE,\n ModelType.COMETAPI_GEMINI_2_0_FLASH,\n ModelType.COMETAPI_GROK_4_0709,\n ModelType.COMETAPI_GROK_3,\n ModelType.COMETAPI_GROK_3_MINI,\n ModelType.COMETAPI_GROK_2_IMAGE_1212,\n ModelType.COMETAPI_DEEPSEEK_V3_1,\n ModelType.COMETAPI_DEEPSEEK_V3,\n ModelType.COMETAPI_DEEPSEEK_R1_0528,\n ModelType.COMETAPI_DEEPSEEK_CHAT,\n ModelType.COMETAPI_DEEPSEEK_REASONER,\n ModelType.COMETAPI_QWEN3_30B_A3B,\n ModelType.COMETAPI_QWEN3_CODER_PLUS_2025_07_22,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_PRO_THUDM_GLM_4_9B_CHAT,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_Z1_RUMINATION_32B_0414,\n ModelType.SILICONFLOW_THUDM_GLM_4_9B_0414,\n ModelType.MODELSCOPE_ERNIE_4_5_VL_28B_A3B_THINKING,\n ModelType.ERNIE_5_0_THINKING,\n ModelType.ERNIE_4_5_TURBO_VL,\n ModelType.GLM_4_5_AIR,\n ModelType.GLM_4_5_AIRX,\n ModelType.ORCAROUTER_AUTO,\n }:\n return 128_000\n elif self in {\n ModelType.NOVITA_LLAMA_3_2_1B,\n }:\n return 131_000\n elif self in {\n ModelType.GROQ_LLAMA_3_1_8B,\n ModelType.QWEN_PLUS,\n ModelType.QWEN_TURBO,\n ModelType.QWEN_CODER_TURBO,\n ModelType.QWEN_PLUS_LATEST,\n ModelType.QWEN_PLUS_2025_04_28,\n ModelType.QWEN_TURBO_LATEST,\n ModelType.QWEN_TURBO_2025_04_28,\n ModelType.TOGETHER_LLAMA_3_1_8B,\n ModelType.TOGETHER_LLAMA_3_1_70B,\n ModelType.TOGETHER_LLAMA_3_1_405B,\n ModelType.TOGETHER_LLAMA_3_3_70B,\n ModelType.SGLANG_QWEN_2_5_7B,\n ModelType.SGLANG_QWEN_2_5_32B,\n ModelType.SGLANG_QWEN_2_5_72B,\n ModelType.OPENROUTER_LLAMA_3_1_70B,\n ModelType.PPIO_LLAMA_3_3_70B,\n ModelType.OPENROUTER_LLAMA_4_SCOUT,\n ModelType.WATSONX_LLAMA_3_3_70B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_1B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_3B_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_11B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_3_2_90B_VISION_INSTRUCT,\n ModelType.WATSONX_LLAMA_GUARD_3_11B_VISION_INSTRUCT,\n ModelType.NOVITA_LLAMA_4_SCOUT_17B,\n ModelType.NOVITA_LLAMA_3_3_70B,\n ModelType.NOVITA_MISTRAL_NEMO,\n ModelType.NEBIUS_LLAMA_3_1_70B,\n ModelType.AVIAN_KIMI_K2_5,\n ModelType.AVIAN_GLM_5,\n }:\n return 131_072\n elif self in {\n ModelType.AVIAN_DEEPSEEK_V3_2,\n }:\n return 164_000\n elif self in {\n ModelType.O1,\n ModelType.O3_MINI,\n ModelType.O3_PRO,\n ModelType.CLAUDE_3_7_SONNET,\n ModelType.CLAUDE_SONNET_4_5,\n ModelType.CLAUDE_OPUS_4_5,\n ModelType.CLAUDE_OPUS_4_6,\n ModelType.CLAUDE_SONNET_4,\n ModelType.CLAUDE_HAIKU_4_5,\n ModelType.CLAUDE_OPUS_4,\n ModelType.CLAUDE_OPUS_4_1,\n ModelType.ATLASCLOUD_GLM_4_7,\n ModelType.YI_MEDIUM_200K,\n ModelType.AWS_CLAUDE_3_5_SONNET,\n ModelType.AWS_CLAUDE_3_HAIKU,\n ModelType.AWS_CLAUDE_3_SONNET,\n ModelType.AWS_CLAUDE_3_7_SONNET,\n ModelType.AWS_CLAUDE_SONNET_4,\n ModelType.AWS_CLAUDE_OPUS_4,\n ModelType.AWS_CLAUDE_OPUS_4_1,\n ModelType.O4_MINI,\n ModelType.O3,\n ModelType.GLM_5_1,\n ModelType.GLM_5,\n ModelType.GLM_4_7,\n ModelType.GLM_4_7_FLASHX,\n ModelType.GLM_4_7_FLASH,\n ModelType.GLM_4_6,\n ModelType.GLM_4_5_FLASH,\n }:\n return 200_000\n elif self in {\n ModelType.MISTRAL_CODESTRAL_MAMBA,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK_FREE,\n ModelType.OPENROUTER_HORIZON_ALPHA,\n ModelType.MOONSHOT_KIMI_K2,\n ModelType.MOONSHOT_KIMI_K2_5,\n ModelType.MOONSHOT_KIMI_K2_TURBO_PREVIEW,\n ModelType.MOONSHOT_KIMI_K2_THINKING,\n ModelType.MOONSHOT_KIMI_K2_THINKING_TURBO,\n }:\n return 256_000\n\n elif self in {\n ModelType.NETMIND_LLAMA_4_SCOUT_17B_16E_INSTRUCT,\n }:\n return 320_000\n elif self in {\n ModelType.GPT_5_1,\n ModelType.GPT_5_2,\n ModelType.GPT_5_MINI,\n ModelType.GPT_5_NANO,\n ModelType.GPT_5_4_MINI,\n ModelType.GPT_5_4_NANO,\n ModelType.ORCAROUTER_GPT_5,\n }:\n return 400_000\n elif self in {\n ModelType.OPENROUTER_LLAMA_4_SCOUT_FREE,\n ModelType.NETMIND_LLAMA_4_MAVERICK_17B_128E_INSTRUCT,\n }:\n return 512_000\n elif self in {\n ModelType.XAI_GROK_4_5,\n }:\n return 500_000\n elif self in {\n ModelType.GEMINI_3_1_PRO,\n ModelType.GEMINI_3_1_FLASH_LITE,\n ModelType.GEMINI_3_PRO,\n ModelType.GEMINI_3_FLASH,\n ModelType.GEMINI_2_5_FLASH,\n ModelType.GEMINI_2_5_PRO,\n ModelType.GEMINI_2_0_FLASH,\n ModelType.GEMINI_2_0_FLASH_EXP,\n ModelType.GEMINI_2_0_FLASH_THINKING,\n ModelType.GEMINI_2_0_FLASH_LITE,\n ModelType.GEMINI_2_0_FLASH_LITE_PREVIEW,\n ModelType.GEMINI_2_0_PRO_EXP, # Not given in doc, assume the same\n ModelType.GLM_4_LONG,\n ModelType.TOGETHER_LLAMA_4_MAVERICK,\n ModelType.OPENROUTER_LLAMA_4_MAVERICK,\n ModelType.AMD_GPT4,\n ModelType.GPT_4_1,\n ModelType.GPT_4_1_MINI,\n ModelType.GPT_4_1_NANO,\n ModelType.NOVITA_LLAMA_4_MAVERICK_17B,\n ModelType.AVIAN_MINIMAX_M2_5,\n ModelType.ORCAROUTER_GEMINI_3_FLASH_PREVIEW,\n }:\n return 1_048_576\n elif self in {\n ModelType.QWEN_3_CODER_PLUS,\n ModelType.CLAUDE_SONNET_4_6,\n ModelType.CLAUDE_OPUS_4_7,\n ModelType.CLAUDE_OPUS_4_8,\n ModelType.CLAUDE_FABLE_5,\n ModelType.DEEPSEEK_V4_FLASH,\n ModelType.DEEPSEEK_V4_PRO,\n ModelType.GPT_5_4,\n ModelType.GPT_5_4_PRO,\n ModelType.GPT_5_5,\n ModelType.GPT_5_5_PRO,\n ModelType.GLM_5_2,\n ModelType.MINIMAX_M3,\n ModelType.ORCAROUTER_CLAUDE_OPUS_4_7,\n ModelType.ORCAROUTER_GROK_4_3,\n }:\n return 1_000_000\n elif self in {\n ModelType.QWEN_LONG,\n ModelType.TOGETHER_LLAMA_4_SCOUT,\n }:\n return 10_000_000\n elif self in {\n ModelType.MINIMAX_M2_7,\n ModelType.MINIMAX_M2_7_HIGHSPEED,\n ModelType.MINIMAX_M2_5,\n ModelType.MINIMAX_M2_1,\n ModelType.MINIMAX_M2_1_LIGHTNING,\n ModelType.MINIMAX_M2,\n }:\n return 204_800\n\n else:\n logger.warning(\n f\"Unknown model type {self}, set maximum token limit \"\n f\"to 999_999_999\"\n )\n return 999_999_999\n\n\nclass EmbeddingModelType(Enum):\n TEXT_EMBEDDING_ADA_2 = \"text-embedding-ada-002\"\n TEXT_EMBEDDING_3_SMALL = \"text-embedding-3-small\"\n TEXT_EMBEDDING_3_LARGE = \"text-embedding-3-large\"\n\n JINA_EMBEDDINGS_V3 = \"jina-embeddings-v3\"\n JINA_CLIP_V2 = \"jina-clip-v2\"\n JINA_COLBERT_V2 = \"jina-colbert-v2\"\n JINA_EMBEDDINGS_V2_BASE_CODE = \"jina-embeddings-v2-base-code\"\n\n MISTRAL_EMBED = \"mistral-embed\"\n\n GEMINI_EMBEDDING_EXP = \"gemini-embedding-exp-03-07\"\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of models is an OpenAI-released model.\"\"\"\n return self in {\n EmbeddingModelType.TEXT_EMBEDDING_ADA_2,\n EmbeddingModelType.TEXT_EMBEDDING_3_SMALL,\n EmbeddingModelType.TEXT_EMBEDDING_3_LARGE,\n }\n\n @property\n def is_jina(self) -> bool:\n r\"\"\"Returns whether this type of models is an Jina model.\"\"\"\n return self in {\n EmbeddingModelType.JINA_EMBEDDINGS_V3,\n EmbeddingModelType.JINA_CLIP_V2,\n EmbeddingModelType.JINA_COLBERT_V2,\n EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n }\n\n @property\n def is_mistral(self) -> bool:\n r\"\"\"Returns whether this type of models is an Mistral-released\n model.\n \"\"\"\n return self in {\n EmbeddingModelType.MISTRAL_EMBED,\n }\n\n @property\n def is_gemini(self) -> bool:\n r\"\"\"Returns whether this type of models is an Gemini-released model.\"\"\"\n return self in {\n EmbeddingModelType.GEMINI_EMBEDDING_EXP,\n }\n\n @property\n def output_dim(self) -> int:\n if self in {\n EmbeddingModelType.JINA_COLBERT_V2,\n }:\n return 128\n elif self in {\n EmbeddingModelType.JINA_EMBEDDINGS_V2_BASE_CODE,\n }:\n return 768\n elif self in {\n EmbeddingModelType.JINA_EMBEDDINGS_V3,\n EmbeddingModelType.JINA_CLIP_V2,\n }:\n return 1024\n elif self is EmbeddingModelType.TEXT_EMBEDDING_ADA_2:\n return 1536\n elif self is EmbeddingModelType.TEXT_EMBEDDING_3_SMALL:\n return 1536\n elif self is EmbeddingModelType.TEXT_EMBEDDING_3_LARGE:\n return 3072\n elif self is EmbeddingModelType.MISTRAL_EMBED:\n return 1024\n elif self is EmbeddingModelType.GEMINI_EMBEDDING_EXP:\n return 3072\n else:\n raise ValueError(f\"Unknown model type {self}.\")\n\n\nclass GeminiEmbeddingTaskType(str, Enum):\n r\"\"\"Task types for Gemini embedding models.\n\n For more information, please refer to:\n https://ai.google.dev/gemini-api/docs/embeddings#task-types\n \"\"\"\n\n SEMANTIC_SIMILARITY = \"SEMANTIC_SIMILARITY\"\n CLASSIFICATION = \"CLASSIFICATION\"\n CLUSTERING = \"CLUSTERING\"\n RETRIEVAL_DOCUMENT = \"RETRIEVAL_DOCUMENT\"\n RETRIEVAL_QUERY = \"RETRIEVAL_QUERY\"\n QUESTION_ANSWERING = \"QUESTION_ANSWERING\"\n FACT_VERIFICATION = \"FACT_VERIFICATION\"\n CODE_RETRIEVAL_QUERY = \"CODE_RETRIEVAL_QUERY\"\n\n\nclass TaskType(Enum):\n AI_SOCIETY = \"ai_society\"\n CODE = \"code\"\n MISALIGNMENT = \"misalignment\"\n TRANSLATION = \"translation\"\n EVALUATION = \"evaluation\"\n SOLUTION_EXTRACTION = \"solution_extraction\"\n ROLE_DESCRIPTION = \"role_description\"\n GENERATE_TEXT_EMBEDDING_DATA = \"generate_text_embedding_data\"\n OBJECT_RECOGNITION = \"object_recognition\"\n IMAGE_CRAFT = \"image_craft\"\n MULTI_CONDITION_IMAGE_CRAFT = \"multi_condition_image_craft\"\n DEFAULT = \"default\"\n VIDEO_DESCRIPTION = \"video_description\"\n\n\nclass VectorDistance(Enum):\n r\"\"\"Distance metrics used in a vector database.\"\"\"\n\n DOT = \"dot\"\n r\"\"\"Dot product. https://en.wikipedia.org/wiki/Dot_product\"\"\"\n\n COSINE = \"cosine\"\n r\"\"\"Cosine similarity. https://en.wikipedia.org/wiki/Cosine_similarity\"\"\"\n\n EUCLIDEAN = \"euclidean\"\n r\"\"\"Euclidean distance. https://en.wikipedia.org/wiki/Euclidean_distance\"\"\"\n\n\nclass OpenAIBackendRole(Enum):\n ASSISTANT = \"assistant\"\n SYSTEM = \"system\"\n DEVELOPER = \"developer\"\n USER = \"user\"\n FUNCTION = \"function\"\n TOOL = \"tool\"\n\n\nclass TerminationMode(Enum):\n ANY = \"any\"\n ALL = \"all\"\n\n\nclass OpenAIImageTypeMeta(EnumMeta):\n def __contains__(cls, image_type: object) -> bool:\n try:\n cls(image_type)\n except ValueError:\n return False\n return True\n\n\nclass OpenAIImageType(Enum, metaclass=OpenAIImageTypeMeta):\n r\"\"\"Image types supported by OpenAI vision model.\"\"\"\n\n # https://platform.openai.com/docs/guides/vision\n PNG = \"png\"\n JPEG = \"jpeg\"\n JPG = \"jpg\"\n WEBP = \"webp\"\n GIF = \"gif\"\n\n\nclass OpenAIVisionDetailType(Enum):\n AUTO = \"auto\"\n LOW = \"low\"\n HIGH = \"high\"\n\n\nclass StorageType(Enum):\n MILVUS = \"milvus\"\n QDRANT = \"qdrant\"\n TIDB = \"tidb\"\n\n\nclass OpenAPIName(Enum):\n COURSERA = \"coursera\"\n KLARNA = \"klarna\"\n SPEAK = \"speak\"\n NASA_APOD = \"nasa_apod\"\n BIZTOC = \"biztoc\"\n CREATE_QR_CODE = \"create_qr_code\"\n OUTSCHOOL = \"outschool\"\n WEB_SCRAPER = \"web_scraper\"\n\n\nclass ModelPlatformType(Enum):\n DEFAULT = os.getenv(\"DEFAULT_MODEL_PLATFORM_TYPE\", \"openai\")\n\n OPENAI = \"openai\"\n AWS_BEDROCK = \"aws-bedrock\"\n AWS_BEDROCK_CONVERSE = \"aws-bedrock-converse\"\n AZURE = \"azure\"\n ANTHROPIC = \"anthropic\"\n GROQ = \"groq\"\n NEBIUS = \"nebius\"\n COMETAPI = \"cometapi\"\n OPENROUTER = \"openrouter\"\n ORCAROUTER = \"orcarouter\"\n OLLAMA = \"ollama\"\n LITELLM = \"litellm\"\n LMSTUDIO = \"lmstudio\"\n ZHIPU = \"zhipuai\"\n GEMINI = \"gemini\"\n VLLM = \"vllm\"\n MISTRAL = \"mistral\"\n REKA = \"reka\"\n TOGETHER = \"together\"\n STUB = \"stub\"\n OPENAI_COMPATIBLE_MODEL = \"openai-compatible-model\"\n SAMBA = \"samba-nova\"\n COHERE = \"cohere\"\n YI = \"lingyiwanwu\"\n QWEN = \"tongyi-qianwen\"\n AMD = \"amd\"\n NVIDIA = \"nvidia\"\n DEEPSEEK = \"deepseek\"\n PPIO = \"ppio\"\n SGLANG = \"sglang\"\n INTERNLM = \"internlm\"\n MOONSHOT = \"moonshot\"\n MODELSCOPE = \"modelscope\"\n SILICONFLOW = \"siliconflow\"\n AIML = \"aiml\"\n VOLCANO = \"volcano\"\n NETMIND = \"netmind\"\n NOVITA = \"novita\"\n WATSONX = \"watsonx\"\n QIANFAN = \"qianfan\"\n CRYNUX = \"crynux\"\n AIHUBMIX = \"aihubmix\"\n MINIMAX = \"minimax\"\n CEREBRAS = \"cerebras\"\n FUNCTION_GEMMA = \"function-gemma\"\n AVIAN = \"avian\"\n ATLASCLOUD = \"atlascloud\"\n XAI = \"xai\"\n\n @classmethod\n def from_name(cls, name):\n r\"\"\"Returns the ModelPlatformType enum value from a string.\"\"\"\n for model_platfrom_type in cls:\n if model_platfrom_type.value == name:\n return model_platfrom_type\n raise ValueError(f\"Unknown ModelPlatformType name: {name}\")\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this platform is openai.\"\"\"\n return self is ModelPlatformType.OPENAI\n\n @property\n def is_aws_bedrock(self) -> bool:\n r\"\"\"Returns whether this platform is aws-bedrock.\"\"\"\n return self in (\n ModelPlatformType.AWS_BEDROCK,\n ModelPlatformType.AWS_BEDROCK_CONVERSE,\n )\n\n @property\n def is_azure(self) -> bool:\n r\"\"\"Returns whether this platform is azure.\"\"\"\n return self is ModelPlatformType.AZURE\n\n @property\n def is_anthropic(self) -> bool:\n r\"\"\"Returns whether this platform is anthropic.\"\"\"\n return self is ModelPlatformType.ANTHROPIC\n\n @property\n def is_groq(self) -> bool:\n r\"\"\"Returns whether this platform is groq.\"\"\"\n return self is ModelPlatformType.GROQ\n\n @property\n def is_openrouter(self) -> bool:\n r\"\"\"Returns whether this platform is openrouter.\"\"\"\n return self is ModelPlatformType.OPENROUTER\n\n @property\n def is_orcarouter(self) -> bool:\n r\"\"\"Returns whether this platform is orcarouter.\"\"\"\n return self is ModelPlatformType.ORCAROUTER\n\n @property\n def is_lmstudio(self) -> bool:\n r\"\"\"Returns whether this platform is lmstudio.\"\"\"\n return self is ModelPlatformType.LMSTUDIO\n\n @property\n def is_ollama(self) -> bool:\n r\"\"\"Returns whether this platform is ollama.\"\"\"\n return self is ModelPlatformType.OLLAMA\n\n @property\n def is_vllm(self) -> bool:\n r\"\"\"Returns whether this platform is vllm.\"\"\"\n return self is ModelPlatformType.VLLM\n\n @property\n def is_sglang(self) -> bool:\n r\"\"\"Returns whether this platform is sglang.\"\"\"\n return self is ModelPlatformType.SGLANG\n\n @property\n def is_together(self) -> bool:\n r\"\"\"Returns whether this platform is together.\"\"\"\n return self is ModelPlatformType.TOGETHER\n\n @property\n def is_litellm(self) -> bool:\n r\"\"\"Returns whether this platform is litellm.\"\"\"\n return self is ModelPlatformType.LITELLM\n\n @property\n def is_zhipuai(self) -> bool:\n r\"\"\"Returns whether this platform is zhipu.\"\"\"\n return self is ModelPlatformType.ZHIPU\n\n @property\n def is_mistral(self) -> bool:\n r\"\"\"Returns whether this platform is mistral.\"\"\"\n return self is ModelPlatformType.MISTRAL\n\n @property\n def is_openai_compatible_model(self) -> bool:\n r\"\"\"Returns whether this is a platform supporting openai\n compatibility\"\"\"\n return self is ModelPlatformType.OPENAI_COMPATIBLE_MODEL\n\n @property\n def is_gemini(self) -> bool:\n r\"\"\"Returns whether this platform is Gemini.\"\"\"\n return self is ModelPlatformType.GEMINI\n\n @property\n def is_reka(self) -> bool:\n r\"\"\"Returns whether this platform is Reka.\"\"\"\n return self is ModelPlatformType.REKA\n\n @property\n def is_samba(self) -> bool:\n r\"\"\"Returns whether this platform is Samba Nova.\"\"\"\n return self is ModelPlatformType.SAMBA\n\n @property\n def is_cohere(self) -> bool:\n r\"\"\"Returns whether this platform is Cohere.\"\"\"\n return self is ModelPlatformType.COHERE\n\n @property\n def is_yi(self) -> bool:\n r\"\"\"Returns whether this platform is Yi.\"\"\"\n return self is ModelPlatformType.YI\n\n @property\n def is_qwen(self) -> bool:\n r\"\"\"Returns whether this platform is Qwen.\"\"\"\n return self is ModelPlatformType.QWEN\n\n @property\n def is_nvidia(self) -> bool:\n r\"\"\"Returns whether this platform is Nvidia.\"\"\"\n return self is ModelPlatformType.NVIDIA\n\n @property\n def is_deepseek(self) -> bool:\n r\"\"\"Returns whether this platform is DeepSeek.\"\"\"\n return self is ModelPlatformType.DEEPSEEK\n\n @property\n def is_netmind(self) -> bool:\n r\"\"\"Returns whether this platform is Netmind.\"\"\"\n return self is ModelPlatformType.NETMIND\n\n @property\n def is_ppio(self) -> bool:\n r\"\"\"Returns whether this platform is PPIO.\"\"\"\n return self is ModelPlatformType.PPIO\n\n @property\n def is_internlm(self) -> bool:\n r\"\"\"Returns whether this platform is InternLM.\"\"\"\n return self is ModelPlatformType.INTERNLM\n\n @property\n def is_moonshot(self) -> bool:\n r\"\"\"Returns whether this platform is Moonshot model.\"\"\"\n return self is ModelPlatformType.MOONSHOT\n\n @property\n def is_modelscope(self) -> bool:\n r\"\"\"Returns whether this platform is ModelScope model.\"\"\"\n return self is ModelPlatformType.MODELSCOPE\n\n @property\n def is_siliconflow(self) -> bool:\n r\"\"\"Returns whether this platform is SiliconFlow.\"\"\"\n return self is ModelPlatformType.SILICONFLOW\n\n @property\n def is_aiml(self) -> bool:\n r\"\"\"Returns whether this platform is AIML.\"\"\"\n return self is ModelPlatformType.AIML\n\n @property\n def is_volcano(self) -> bool:\n r\"\"\"Returns whether this platform is volcano.\"\"\"\n return self is ModelPlatformType.VOLCANO\n\n @property\n def is_novita(self) -> bool:\n r\"\"\"Returns whether this platform is Novita.\"\"\"\n return self is ModelPlatformType.NOVITA\n\n @property\n def is_watsonx(self) -> bool:\n r\"\"\"Returns whether this platform is WatsonX.\"\"\"\n return self is ModelPlatformType.WATSONX\n\n @property\n def is_crynux(self) -> bool:\n r\"\"\"Returns whether this platform is Crynux.\"\"\"\n return self is ModelPlatformType.CRYNUX\n\n @property\n def is_aihubmix(self) -> bool:\n r\"\"\"Returns whether this platform is AihubMix.\"\"\"\n return self is ModelPlatformType.AIHUBMIX\n\n @property\n def is_minimax(self) -> bool:\n r\"\"\"Returns whether this platform is Minimax M2.\"\"\"\n return self is ModelPlatformType.MINIMAX\n\n @property\n def is_cerebras(self) -> bool:\n r\"\"\"Returns whether this platform is Cerebras.\"\"\"\n return self is ModelPlatformType.CEREBRAS\n\n @property\n def is_avian(self) -> bool:\n r\"\"\"Returns whether this platform is Avian.\"\"\"\n return self is ModelPlatformType.AVIAN\n\n @property\n def is_atlascloud(self) -> bool:\n r\"\"\"Returns whether this platform is AtlasCloud.\"\"\"\n return self is ModelPlatformType.ATLASCLOUD\n\n\nclass AudioModelType(Enum):\n TTS_1 = \"tts-1\"\n TTS_1_HD = \"tts-1-hd\"\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of audio models is an OpenAI-released\n model.\"\"\"\n return self in {\n AudioModelType.TTS_1,\n AudioModelType.TTS_1_HD,\n }\n\n\nclass VoiceType(Enum):\n ALLOY = \"alloy\"\n ECHO = \"echo\"\n FABLE = \"fable\"\n ONYX = \"onyx\"\n NOVA = \"nova\"\n SHIMMER = \"shimmer\"\n\n @property\n def is_openai(self) -> bool:\n r\"\"\"Returns whether this type of voice is an OpenAI-released voice.\"\"\"\n return self in {\n VoiceType.ALLOY,\n VoiceType.ECHO,\n VoiceType.FABLE,\n VoiceType.ONYX,\n VoiceType.NOVA,\n VoiceType.SHIMMER,\n }\n\n\nclass JinaReturnFormat(Enum):\n DEFAULT = None\n MARKDOWN = \"markdown\"\n HTML = \"html\"\n TEXT = \"text\"\n\n\nclass JinaRerankerModelType(str, Enum):\n r\"\"\"Model types for Jina AI Reranker.\n\n These models are available through the Jina AI Reranker API for\n re-ranking documents based on their relevance to a query.\n\n For more information, please refer to:\n https://jina.ai/reranker/\n \"\"\"\n\n JINA_RERANKER_V2_BASE_MULTILINGUAL = \"jina-reranker-v2-base-multilingual\"\n r\"\"\"Multilingual reranker model supporting 100+ languages. (Default)\"\"\"\n\n JINA_RERANKER_V1_BASE_EN = \"jina-reranker-v1-base-en\"\n r\"\"\"English base reranker model.\"\"\"\n\n JINA_RERANKER_V1_TINY_EN = \"jina-reranker-v1-tiny-en\"\n r\"\"\"Lightweight English reranker model for faster inference.\"\"\"\n\n JINA_RERANKER_V1_TURBO_EN = \"jina-reranker-v1-turbo-en\"\n r\"\"\"High-performance English reranker model.\"\"\"\n\n JINA_COLBERT_V2 = \"jina-colbert-v2\"\n r\"\"\"ColBERT-based reranker for token-level matching.\"\"\"\n\n JINA_RERANKER_V3 = \"jina-reranker-v3\"\n r\"\"\"Latest multilingual reranker with 131K context window (0.6B params).\"\"\"\n\n\nclass HuggingFaceRepoType(str, Enum):\n DATASET = \"dataset\"\n MODEL = \"model\"\n SPACE = \"space\"\n"}}, "camel/utils/commons.py": {"7dfa2a75a7b17f35a91c4841c52877e1272cd559": {"t": 1785674219.7051804, "n": 40439, "h": "ac4403de1bafbd33", "c": "# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\nimport asyncio\nimport functools\nimport importlib\nimport inspect\nimport logging\nimport os\nimport platform\nimport re\nimport socket\nimport subprocess\nimport threading\nimport time\nimport zipfile\nfrom functools import wraps\nfrom http import HTTPStatus\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Dict,\n List,\n Mapping,\n Optional,\n Set,\n Tuple,\n Type,\n TypeVar,\n cast,\n)\n\nif TYPE_CHECKING:\n from camel.responses.agent_responses import ChatAgentResponse\nfrom urllib.parse import urlparse\n\nimport pydantic\nimport requests\nfrom pydantic import BaseModel\n\nfrom camel.types import TaskType\n\nfrom .constants import Constants\n\nF = TypeVar('F', bound=Callable[..., Any])\nT = TypeVar('T', bound=BaseModel)\n\nlogger = logging.getLogger(__name__)\n\n\ndef print_text_animated(text, delay: float = 0.02, end: str = \"\"):\n r\"\"\"Prints the given text with an animated effect.\n\n Args:\n text (str): The text to print.\n delay (float, optional): The delay between each character printed.\n (default: :obj:`0.02`)\n end (str, optional): The end character to print after each\n character of text. (default: :obj:`\"\"`)\n \"\"\"\n for char in text:\n print(char, end=end, flush=True)\n time.sleep(delay)\n\n\ndef get_prompt_template_key_words(template: str) -> Set[str]:\n r\"\"\"Given a string template containing curly braces {}, return a set of\n the words inside the braces.\n\n Args:\n template (str): A string containing curly braces.\n\n Returns:\n List[str]: A list of the words inside the curly braces.\n\n Example:\n >>> get_prompt_template_key_words('Hi, {name}! How are you {status}?')\n {'name', 'status'}\n \"\"\"\n return set(re.findall(r'{([^}]*)}', template))\n\n\ndef get_first_int(string: str) -> Optional[int]:\n r\"\"\"Returns the first integer number found in the given string.\n\n If no integer number is found, returns None.\n\n Args:\n string (str): The input string.\n\n Returns:\n int or None: The first integer number found in the string, or None if\n no integer number is found.\n \"\"\"\n match = re.search(r'\\d+', string)\n if match:\n return int(match.group())\n else:\n return None\n\n\ndef download_tasks(task: TaskType, folder_path: str) -> None:\n r\"\"\"Downloads task-related files from a specified URL and extracts them.\n\n This function downloads a zip file containing tasks based on the specified\n `task` type from a predefined URL, saves it to `folder_path`, and then\n extracts the contents of the zip file into the same folder. After\n extraction, the zip file is deleted.\n\n Args:\n task (TaskType): An enum representing the type of task to download.\n folder_path (str): The path of the folder where the zip file will be\n downloaded and extracted.\n \"\"\"\n # Define the path to save the zip file\n zip_file_path = os.path.join(folder_path, \"tasks.zip\")\n\n # Download the zip file from the Google Drive link\n response = requests.get(\n \"https://huggingface.co/datasets/camel-ai/\"\n f\"metadata/resolve/main/{task.value}_tasks.zip\"\n )\n\n # Save the zip file\n with open(zip_file_path, \"wb\") as f:\n f.write(response.content)\n\n with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n zip_ref.extractall(folder_path)\n\n # Delete the zip file\n os.remove(zip_file_path)\n\n\ndef get_task_list(task_response: str) -> List[str]:\n r\"\"\"Parse the response of the Agent and return task list.\n\n Args:\n task_response (str): The string response of the Agent.\n\n Returns:\n List[str]: A list of the string tasks.\n \"\"\"\n\n new_tasks_list = []\n task_string_list = task_response.strip().split('\\n')\n # each task starts with #.\n for task_string in task_string_list:\n task_parts = task_string.strip().split(\".\", 1)\n if len(task_parts) == 2:\n task_id = ''.join(s for s in task_parts[0] if s.isnumeric())\n task_name = re.sub(r'[^\\w\\s_]+', '', task_parts[1]).strip()\n if task_name.strip() and task_id.isnumeric():\n new_tasks_list.append(task_name)\n return new_tasks_list\n\n\ndef check_server_running(server_url: str) -> bool:\n r\"\"\"Check whether the port referred by the URL to the server\n is open.\n\n Args:\n server_url (str): The URL to the server running LLM inference\n service.\n\n Returns:\n bool: Whether the port is open for packets (server is running).\n \"\"\"\n parsed_url = urlparse(server_url)\n url_tuple = (parsed_url.hostname, parsed_url.port)\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n result = sock.connect_ex(url_tuple)\n sock.close()\n\n # if the port is open, the result should be 0.\n return result == 0\n\n\ndef dependencies_required(*required_modules: str) -> Callable[[F], F]:\n r\"\"\"A decorator to ensure that specified Python modules\n are available before a function executes.\n\n Args:\n required_modules (str): The required modules to be checked for\n availability.\n\n Returns:\n Callable[[F], F]: The original function with the added check for\n required module dependencies.\n\n Raises:\n ImportError: If any of the required modules are not available.\n\n Example:\n ::\n\n @dependencies_required('numpy', 'pandas')\n def data_processing_function():\n # Function implementation...\n \"\"\"\n\n def decorator(func: F) -> F:\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> Any:\n missing_modules = [\n m for m in required_modules if not is_module_available(m)\n ]\n if missing_modules:\n raise ImportError(\n f\"Missing required modules: {', '.join(missing_modules)}\"\n )\n return func(*args, **kwargs)\n\n return cast(F, wrapper)\n\n return decorator\n\n\ndef is_module_available(module_name: str) -> bool:\n r\"\"\"Check if a module is available for import.\n\n Args:\n module_name (str): The name of the module to check for availability.\n\n Returns:\n bool: True if the module can be imported, False otherwise.\n \"\"\"\n try:\n importlib.import_module(module_name)\n return True\n except ImportError:\n return False\n\n\ndef api_keys_required(\n param_env_list: List[Tuple[Optional[str], str]],\n) -> Callable[[F], F]:\n r\"\"\"A decorator to check if the required API keys are provided in the\n environment variables or as function arguments.\n\n Args:\n param_env_list (List[Tuple[Optional[str], str]]): A list of tuples\n where each tuple contains a function argument name (as the first\n element, or None) and the corresponding environment variable name\n (as the second element) that holds the API key.\n\n Returns:\n Callable[[F], F]: The original function wrapped with the added check\n for the required API keys.\n\n Raises:\n ValueError: If any of the required API keys are missing, either\n from the function arguments or environment variables.\n\n Example:\n ::\n\n @api_keys_required([\n ('api_key_arg', 'API_KEY_1'),\n ('another_key_arg', 'API_KEY_2'),\n (None, 'API_KEY_3'),\n ])\n def some_api_function(api_key_arg=None, another_key_arg=None):\n # Function implementation that requires API keys\n \"\"\"\n import inspect\n\n def decorator(func: F) -> F:\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> Any:\n signature = inspect.signature(func)\n bound_arguments = signature.bind(*args, **kwargs)\n bound_arguments.apply_defaults()\n arguments = bound_arguments.arguments\n\n missing_keys = []\n for param_name, env_var_name in param_env_list:\n if not isinstance(env_var_name, str):\n raise TypeError(\n f\"Environment variable name must be a string, got\"\n f\" {type(env_var_name)}\"\n )\n\n value = None\n if (\n param_name\n ): # If param_name is provided, check function argument first\n if not isinstance(param_name, str):\n raise TypeError(\n f\"Parameter name must be a string, \"\n f\"got {type(param_name)}\"\n )\n value = arguments.get(param_name)\n # If we found a valid value in arguments, continue to next\n # item\n if value:\n continue\n\n # Check environment variable if no valid value found yet\n value = os.environ.get(env_var_name)\n if not value or value.strip() == \"\":\n missing_keys.append(env_var_name)\n\n if missing_keys:\n env_key_urls = {\n 'ANTHROPIC_API_KEY': \"https://platform.claude.com/docs/en/api/overview\",\n 'AIML_API_KEY': \"https://aimlapi.com/\",\n 'COHERE_API_KEY': \"https://cohere.com/\",\n 'COMETAPI_KEY': \"https://api.cometapi.com/console/token\",\n 'DEEPSEEK_API_KEY': \"https://www.deepseek.com/\",\n 'AZURE_OPENAI_API_KEY': \"https://portal.azure.com/\",\n 'OPENAI_API_KEY': \"https://platform.openai.com/docs/overview\",\n 'FISHAUDIO_API_KEY': \"https://fish.audio/\",\n 'GEMINI_API_KEY': \"https://gemini.google.com/\",\n 'INTERNLM_API_KEY': \"https://internlm.intern-ai.org.cn/api/tokens\",\n 'GROQ_API_KEY': \"https://console.groq.com/keys\",\n 'MISTRAL_API_KEY': \"https://mistral.ai/\",\n 'MOONSHOT_API_KEY': \"platform.moonshot.ai/console\",\n 'NVIDIA_API_KEY': \"https://build.nvidia.com/settings/api-keys\",\n 'OPENAI_COMPATIBILITY_API_KEY': \"https://platform.openai.com/docs/overview\",\n 'QWEN_API_KEY': \"https://tongyi.aliyun.com/\",\n 'REKA_API_KEY': \"https://docs.reka.ai/quick-start\",\n 'SAMBA_API_KEY': \"cloud.sambanova.ai/apis\",\n 'TOGETHER_API_KEY': \"https://docs.together.ai/docs/quickstart\",\n 'YI_API_KEY': \"https://platform.lingyiwanwu.com/docs\",\n 'ZHIPUAI_API_KEY': \"https://www.zhipuai.cn/\",\n 'KLAVIS_API_KEY': \"https://www.klavis.ai/docs\",\n 'XAI_API_KEY': \"https://docs.x.ai/docs/overview\",\n 'AVIAN_API_KEY': \"https://avian.io\",\n 'ORCAROUTER_API_KEY': \"https://www.orcarouter.ai/\",\n 'QUERIT_API_KEY': \"https://www.querit.ai/en/dashboard/home\",\n 'PERPLEXITY_API_KEY': \"https://www.perplexity.ai/account/api\",\n 'TELEGRAM_TOKEN': 'https://t.me/BotFather',\n 'SLACK_TOKEN': \"https://api.slack.com/apps\",\n 'SLACK_SCOPES': \"https://api.slack.com/apps\",\n 'SLACK_SIGNING_SECRET': \"https://api.slack.com/apps\",\n }\n key_way = env_key_urls.get(\n missing_keys[0], \"the official website\"\n )\n raise ValueError(\n \"Missing or empty required API keys in \"\n f\"environment variables: {', '.join(missing_keys)}.\\n\"\n f\"You can obtain the API key from {key_way}\"\n )\n return func(*args, **kwargs)\n\n return cast(F, wrapper)\n\n return decorator\n\n\ndef get_system_information():\n r\"\"\"Gathers information about the operating system.\n\n Returns:\n dict: A dictionary containing various pieces of OS information.\n \"\"\"\n sys_info = {\n \"OS Name\": os.name,\n \"System\": platform.system(),\n \"Release\": platform.release(),\n \"Version\": platform.version(),\n \"Machine\": platform.machine(),\n \"Processor\": platform.processor(),\n \"Platform\": platform.platform(),\n }\n\n return sys_info\n\n\ndef to_pascal(snake: str) -> str:\n \"\"\"Convert a snake_case string to PascalCase.\n\n Args:\n snake (str): The snake_case string to be converted.\n\n Returns:\n str: The converted PascalCase string.\n \"\"\"\n # Check if the string is already in PascalCase\n if re.match(r'^[A-Z][a-zA-Z0-9]*([A-Z][a-zA-Z0-9]*)*$', snake):\n return snake\n # Remove leading and trailing underscores\n snake = snake.strip('_')\n # Replace multiple underscores with a single one\n snake = re.sub('_+', '_', snake)\n # Convert to PascalCase\n return re.sub(\n '_([0-9A-Za-z])',\n lambda m: m.group(1).upper(),\n snake.title(),\n )\n\n\ndef get_pydantic_major_version() -> int:\n r\"\"\"Get the major version of Pydantic.\n\n Returns:\n int: The major version number of Pydantic if installed, otherwise 0.\n \"\"\"\n try:\n return int(pydantic.__version__.split(\".\")[0])\n except ImportError:\n return 0\n\n\ndef get_pydantic_object_schema(pydantic_params: Type[BaseModel]) -> Dict:\n r\"\"\"Get the JSON schema of a Pydantic model.\n\n Args:\n pydantic_params (Type[BaseModel]): The Pydantic model class to retrieve\n the schema for.\n\n Returns:\n dict: The JSON schema of the Pydantic model.\n \"\"\"\n return pydantic_params.model_json_schema()\n\n\ndef func_string_to_callable(code: str):\n r\"\"\"Convert a function code string to a callable function object.\n\n Args:\n code (str): The function code as a string.\n\n Returns:\n Callable[..., Any]: The callable function object extracted from the\n code string.\n \"\"\"\n local_vars: Mapping[str, object] = {}\n exec(code, globals(), local_vars)\n func = local_vars.get(Constants.FUNC_NAME_FOR_STRUCTURED_OUTPUT)\n return func\n\n\ndef json_to_function_code(json_obj: Dict) -> str:\n r\"\"\"Generate a Python function code from a JSON schema.\n\n Args:\n json_obj (dict): The JSON schema object containing properties and\n required fields, and json format is follow openai tools schema\n\n Returns:\n str: The generated Python function code as a string.\n \"\"\"\n properties = json_obj.get('properties', {})\n required = json_obj.get('required', [])\n\n if not properties or not required:\n raise ValueError(\n \"JSON schema must contain 'properties' and 'required' fields\"\n )\n\n args = []\n docstring_args = []\n return_keys = []\n\n prop_to_python = {\n 'string': 'str',\n 'number': 'float',\n 'integer': 'int',\n 'boolean': 'bool',\n }\n\n for prop in required:\n # if no description, return empty string\n description = properties[prop].get('description', \"\")\n prop_type = properties[prop]['type']\n python_type = prop_to_python.get(prop_type, prop_type)\n args.append(f\"{prop}: {python_type}\")\n docstring_args.append(\n f\" {prop} ({python_type}): {description}.\"\n )\n return_keys.append(prop)\n\n # extract entity of schema\n args_str = \", \".join(args)\n docstring_args_str = \"\\n\".join(docstring_args)\n return_keys_str = \", \".join(return_keys)\n\n # function template\n function_code = f'''\ndef {Constants.FUNC_NAME_FOR_STRUCTURED_OUTPUT}({args_str}):\n r\"\"\"Return response with a specified json format.\n Args:\n{docstring_args_str}\n Returns:\n Dict: A dictionary containing {return_keys_str}.\n \"\"\"\n return {{{\", \".join([f'\"{prop}\": {prop}' for prop in required])}}}\n '''\n\n return function_code\n\n\ndef text_extract_from_web(url: str) -> str:\n r\"\"\"Get the text information from given url.\n\n Args:\n url (str): The website you want to search.\n\n Returns:\n str: All texts extract from the web.\n \"\"\"\n try:\n import requests\n from newspaper import Article\n\n # Request the target page\n article = Article(url)\n article.download()\n article.parse()\n text = article.text\n\n except requests.RequestException as e:\n text = f\"Can't access {url}, error: {e}\"\n\n except Exception as e:\n text = f\"Can't extract text from {url}, error: {e}\"\n\n return text\n\n\ndef create_chunks(text: str, n: int) -> List[str]:\n r\"\"\"Returns successive n-sized chunks from provided text. Split a text\n into smaller chunks of size n\".\n\n Args:\n text (str): The text to be split.\n n (int): The max length of a single chunk.\n\n Returns:\n List[str]: A list of split texts.\n \"\"\"\n\n chunks = []\n i = 0\n while i < len(text):\n # Find the nearest end of sentence within a range of 0.5 * n\n # and 1.5 * n tokens\n j = min(i + int(1.2 * n), len(text))\n while j > i + int(0.8 * n):\n # Decode the tokens and check for full stop or newline\n chunk = text[i:j]\n if chunk.endswith(\".\") or chunk.endswith(\"\\n\"):\n break\n j -= 1\n # If no end of sentence found, use n tokens as the chunk size\n if j == i + int(0.8 * n):\n j = min(i + n, len(text))\n chunks.append(text[i:j])\n i = j\n return chunks\n\n\ndef is_docker_running() -> bool:\n r\"\"\"Check if the Docker daemon is running.\n\n Returns:\n bool: True if the Docker daemon is running, False otherwise.\n \"\"\"\n try:\n result = subprocess.run(\n [\"docker\", \"info\"],\n check=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n return result.returncode == 0\n except (subprocess.CalledProcessError, FileNotFoundError):\n return False\n\n\ntry:\n if os.getenv(\"AGENTOPS_API_KEY\") is not None:\n from agentops import (\n ToolEvent,\n record,\n )\n else:\n raise ImportError\nexcept (ImportError, AttributeError):\n ToolEvent = None\n\n\ndef agentops_decorator(func):\n r\"\"\"Decorator that records the execution of a function if ToolEvent is\n available.\n\n Parameters:\n func (callable): The function to be decorated.\n\n Returns:\n callable: The wrapped function which records its execution details.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n if ToolEvent:\n tool_event = ToolEvent(name=func.__name__, params=kwargs)\n result = func(*args, **kwargs)\n tool_event.returns = result\n record(tool_event)\n return result\n return func(*args, **kwargs)\n\n return wrapper\n\n\nclass AgentOpsMeta(type):\n r\"\"\"Metaclass that automatically decorates all callable attributes with\n the agentops_decorator,\n except for the 'get_tools' method.\n\n Methods:\n __new__(cls, name, bases, dct):\n Creates a new class with decorated methods.\n \"\"\"\n\n def __new__(cls, name, bases, dct):\n if ToolEvent:\n for attr, value in dct.items():\n if callable(value) and attr != 'get_tools':\n dct[attr] = agentops_decorator(value)\n return super().__new__(cls, name, bases, dct)\n\n\ndef track_agent(*args, **kwargs):\n r\"\"\"Mock track agent decorator for AgentOps.\"\"\"\n\n def noop(f):\n return f\n\n return noop\n\n\ndef handle_http_error(response: requests.Response) -> str:\n r\"\"\"Handles the HTTP errors based on the status code of the response.\n\n Args:\n response (requests.Response): The HTTP response from the API call.\n\n Returns:\n str: The error type, based on the status code.\n \"\"\"\n if response.status_code == HTTPStatus.UNAUTHORIZED:\n return \"Unauthorized. Check your access token.\"\n elif response.status_code == HTTPStatus.FORBIDDEN:\n return \"Forbidden. You do not have permission to perform this action.\"\n elif response.status_code == HTTPStatus.NOT_FOUND:\n return \"Not Found. The resource could not be located.\"\n elif response.status_code == HTTPStatus.TOO_MANY_REQUESTS:\n return \"Too Many Requests. You have hit the rate limit.\"\n else:\n return \"HTTP Error\"\n\n\ndef retry_on_error(\n max_retries: int = 3, initial_delay: float = 1.0\n) -> Callable:\n r\"\"\"Decorator to retry function calls on exception with exponential\n backoff.\n\n Args:\n max_retries (int): Maximum number of retry attempts\n initial_delay (float): Initial delay between retries in seconds\n\n Returns:\n Callable: Decorated function with retry logic\n \"\"\"\n\n def decorator(func: Callable) -> Callable:\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n delay = initial_delay\n last_exception = None\n\n for attempt in range(max_retries + 1):\n try:\n return func(*args, **kwargs)\n except Exception as e:\n last_exception = e\n if attempt == max_retries:\n logger.warning(\n f\"Failed after {max_retries} retries: {e!s}\"\n )\n raise\n\n logger.warning(\n f\"Attempt {attempt + 1} failed: {e!s}. \"\n f\"Retrying in {delay:.1f}s...\"\n )\n time.sleep(delay)\n delay *= 2 # Exponential backoff\n\n raise last_exception\n\n return wrapper\n\n return decorator\n\n\nclass BatchProcessor:\n r\"\"\"Handles batch processing with dynamic sizing and error handling based\n on system load.\n \"\"\"\n\n def __init__(\n self,\n max_workers: Optional[int] = None,\n initial_batch_size: Optional[int] = None,\n monitoring_interval: float = 5.0,\n cpu_threshold: float = 80.0,\n memory_threshold: float = 85.0,\n ):\n r\"\"\"Initialize the BatchProcessor with dynamic worker allocation.\n\n Args:\n max_workers: Maximum number of workers. If None, will be\n determined dynamically based on system resources.\n (default: :obj:`None`)\n initial_batch_size: Initial size of each batch. If `None`,\n defaults to `10`. (default: :obj:`None`)\n monitoring_interval: Interval in seconds between resource checks.\n (default: :obj:`5.0`)\n cpu_threshold: CPU usage percentage threshold for scaling down.\n (default: :obj:`80.0`)\n memory_threshold: Memory usage percentage threshold for scaling\n down. (default: :obj:`85.0`)\n \"\"\"\n import psutil\n\n self.monitoring_interval = monitoring_interval\n self.cpu_threshold = cpu_threshold\n self.memory_threshold = memory_threshold\n self.last_check_time = time.time()\n self.psutil = psutil\n\n # Initialize performance metrics\n self.total_processed = 0\n self.total_errors = 0\n self.processing_times: List = []\n\n if max_workers is None:\n self.max_workers = self._calculate_optimal_workers()\n else:\n self.max_workers = max_workers\n\n self.batch_size = (\n 10 if initial_batch_size is None else initial_batch_size\n )\n self.min_batch_size = 1\n self.max_batch_size = 20\n self.backoff_factor = 0.8\n self.success_factor = 1.2\n\n # Initial resource check\n self._update_resource_metrics()\n\n def _calculate_optimal_workers(self) -> int:\n r\"\"\"Calculate optimal number of workers based on system resources.\"\"\"\n cpu_count = self.psutil.cpu_count()\n cpu_percent = self.psutil.cpu_percent(interval=1)\n memory = self.psutil.virtual_memory()\n\n # Base number of workers on CPU count and current load\n if cpu_percent > self.cpu_threshold:\n workers = max(1, cpu_count // 4)\n elif cpu_percent > 60:\n workers = max(1, cpu_count // 2)\n else:\n workers = max(1, cpu_count - 1)\n\n # Further reduce if memory is constrained\n if memory.percent > self.memory_threshold:\n workers = max(1, workers // 2)\n\n return workers\n\n def _update_resource_metrics(self) -> None:\n r\"\"\"Update current resource usage metrics.\"\"\"\n self.current_cpu = self.psutil.cpu_percent()\n self.current_memory = self.psutil.virtual_memory().percent\n self.last_check_time = time.time()\n\n def _should_check_resources(self) -> bool:\n r\"\"\"Determine if it's time to check resource usage again.\"\"\"\n return time.time() - self.last_check_time >= self.monitoring_interval\n\n def adjust_batch_size(\n self, success: bool, processing_time: Optional[float] = None\n ) -> None:\n r\"\"\"Adjust batch size based on success/failure and system resources.\n\n Args:\n success (bool): Whether the last batch completed successfully\n processing_time (Optional[float]): Time taken to process the last\n batch. (default: :obj:`None`)\n \"\"\"\n # Update metrics\n self.total_processed += 1\n if not success:\n self.total_errors += 1\n if processing_time is not None:\n self.processing_times.append(processing_time)\n\n # Check system resources if interval has elapsed\n if self._should_check_resources():\n self._update_resource_metrics()\n\n # Adjust based on resource usage\n if (\n self.current_cpu > self.cpu_threshold\n or self.current_memory > self.memory_threshold\n ):\n self.batch_size = max(\n int(self.batch_size * self.backoff_factor),\n self.min_batch_size,\n )\n self.max_workers = max(1, self.max_workers - 1)\n return\n\n # Adjust based on success/failure\n if success:\n self.batch_size = min(\n int(self.batch_size * self.success_factor), self.max_batch_size\n )\n else:\n self.batch_size = max(\n int(self.batch_size * self.backoff_factor), self.min_batch_size\n )\n\n def get_performance_metrics(self) -> Dict[str, Any]:\n r\"\"\"Get current performance metrics.\n\n Returns:\n Dict containing performance metrics including:\n - total_processed: Total number of batches processed\n - error_rate: Percentage of failed batches\n - avg_processing_time: Average time per batch\n - current_batch_size: Current batch size\n - current_workers: Current number of workers\n - current_cpu: Current CPU usage percentage\n - current_memory: Current memory usage percentage\n \"\"\"\n metrics = {\n \"total_processed\": self.total_processed,\n \"error_rate\": (self.total_errors / max(1, self.total_processed))\n * 100,\n \"avg_processing_time\": sum(self.processing_times)\n / max(1, len(self.processing_times)),\n \"current_batch_size\": self.batch_size,\n \"current_workers\": self.max_workers,\n \"current_cpu\": self.current_cpu,\n \"current_memory\": self.current_memory,\n }\n return metrics\n\n\ndef download_github_subdirectory(\n repo: str, subdir: str, data_dir: Path, branch=\"main\"\n):\n r\"\"\"Download subdirectory of the Github repo of\n the benchmark.\n\n This function downloads all files and subdirectories from a\n specified subdirectory of a GitHub repository and\n saves them to a local directory.\n\n Args:\n repo (str): The name of the GitHub repository\n in the format \"owner/repo\".\n subdir (str): The path to the subdirectory\n within the repository to download.\n data_dir (Path): The local directory where\n the files will be saved.\n branch (str, optional): The branch of the repository to use.\n Defaults to \"main\".\n \"\"\"\n from tqdm import tqdm\n\n api_url = (\n f\"https://api.github.com/repos/{repo}/contents/{subdir}?ref={branch}\"\n )\n headers = {\"Accept\": \"application/vnd.github.v3+json\"}\n response = requests.get(api_url, headers=headers)\n response.raise_for_status()\n files = response.json()\n os.makedirs(data_dir, exist_ok=True)\n\n for file in tqdm(files, desc=\"Downloading\"):\n file_path = data_dir / file[\"name\"]\n\n if file[\"type\"] == \"file\":\n file_url = file[\"download_url\"]\n file_response = requests.get(file_url)\n with open(file_path, \"wb\") as f:\n f.write(file_response.content)\n elif file[\"type\"] == \"dir\":\n download_github_subdirectory(\n repo, f'{subdir}/{file[\"name\"]}', file_path, branch\n )\n\n\ndef generate_prompt_for_structured_output(\n response_format: Optional[Type[BaseModel]],\n user_message: str,\n) -> str:\n \"\"\"\n This function generates a prompt based on the provided Pydantic model and\n user message.\n\n Args:\n response_format (Type[BaseModel]): The Pydantic model class.\n user_message (str): The user message to be used in the prompt.\n\n Returns:\n str: A prompt string for the LLM.\n \"\"\"\n if response_format is None:\n return user_message\n\n json_schema = response_format.model_json_schema()\n sys_prompt = (\n \"Given the user message, please generate a JSON response adhering \"\n \"to the following JSON schema:\\n\"\n f\"{json_schema}\\n\"\n \"Make sure the JSON response is valid and matches the EXACT structure \"\n \"defined in the schema. Your result should only be a valid json \"\n \"object, without any other text or comments.\\n\"\n )\n user_prompt = f\"User message: {user_message}\\n\"\n\n final_prompt = f\"\"\"\n {sys_prompt}\n {user_prompt}\n \"\"\"\n return final_prompt\n\n\ndef safe_extract_parsed(\n response: \"ChatAgentResponse\",\n schema: Type[T],\n) -> Optional[T]:\n r\"\"\"Safely extract a parsed structured output from a ChatAgentResponse.\n\n Handles the common cases where ``response.msg`` is ``None`` (empty or\n multi-message response) or ``msg.parsed`` is ``None`` (model failed to\n produce valid structured output). When the parsed value is a dict, it\n attempts to construct the schema from it.\n\n Args:\n response (ChatAgentResponse): The agent response to extract from.\n schema (Type[T]): The expected Pydantic model class.\n\n Returns:\n Optional[T]: The parsed and validated result, or ``None`` if\n extraction fails for any reason.\n \"\"\"\n msg = response.msg\n # Empty or multi-message response (len(msgs) != 1)\n if msg is None:\n logger.error(\n f\"safe_extract_parsed: response.msg is None \"\n f\"(msgs count: {len(response.msgs)}), \"\n f\"cannot extract {schema.__name__}\"\n )\n return None\n parsed = msg.parsed\n # Already the expected Pydantic model\n if isinstance(parsed, schema):\n return parsed\n # TODO: Unify backend parsing so parsed is always a model instance,\n # removing the need for this dict fallback.\n # LiteLLM or non-OpenAI backends may return raw dict instead of model\n if isinstance(parsed, dict):\n try:\n return schema(**parsed)\n except Exception as e:\n logger.error(\n f\"safe_extract_parsed: failed to construct \"\n f\"{schema.__name__} from dict: {e}\"\n )\n return None\n # Model did not produce structured output\n if parsed is None:\n logger.error(\n f\"safe_extract_parsed: msg.parsed is None, \"\n f\"model did not produce valid {schema.__name__}\"\n )\n # Unexpected type\n else:\n logger.error(\n f\"safe_extract_parsed: msg.parsed is \"\n f\"{type(parsed).__name__}, expected {schema.__name__}\"\n )\n return None\n\n\ndef with_timeout(timeout=None):\n r\"\"\"Decorator that adds timeout functionality to functions.\n\n Executes functions with a specified timeout value. Returns a timeout\n message if execution time is exceeded.\n\n Args:\n timeout (float, optional): The timeout duration in seconds. If None,\n will try to get timeout from the instance's timeout attribute.\n (default: :obj:`None`)\n\n Example:\n >>> @with_timeout(5)\n ... def my_function():\n ... return \"Success\"\n >>> my_function()\n\n >>> class MyClass:\n ... timeout = 5\n ... @with_timeout()\n ... def my_method(self):\n ... return \"Success\"\n \"\"\"\n\n def decorator(func):\n if inspect.iscoroutinefunction(func):\n\n @functools.wraps(func)\n async def async_wrapper(*args, **kwargs):\n eff_timeout = timeout\n if eff_timeout is None and args:\n eff_timeout = getattr(args[0], 'timeout', None)\n\n if eff_timeout is None:\n return await func(*args, **kwargs)\n\n return await asyncio.wait_for(\n func(*args, **kwargs), timeout=eff_timeout\n )\n\n return async_wrapper\n else:\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n # Determine the effective timeout value\n effective_timeout = timeout\n if effective_timeout is None and args:\n effective_timeout = getattr(args[0], 'timeout', None)\n\n # If no timeout value is provided, execute function normally\n if effective_timeout is None:\n return func(*args, **kwargs)\n\n # If current thread has a running asyncio event loop, avoid\n # switching threads to preserve asyncio context (e.g., for\n # asyncio.create_task). Execute inline without enforcing a\n # sync timeout to keep event loop semantics intact.\n try:\n asyncio.get_running_loop()\n loop_running = True\n except RuntimeError:\n loop_running = False\n\n if loop_running:\n return func(*args, **kwargs)\n\n # Container to hold the result or exception from the function\n # call\n result_container = []\n exception_container = []\n\n def target():\n try:\n result_container.append(func(*args, **kwargs))\n except Exception as e:\n exception_container.append(e)\n\n # Start the function in a new daemon thread\n thread = threading.Thread(target=target, daemon=True)\n thread.start()\n thread.join(effective_timeout)\n\n # Check if the thread is still alive after the timeout\n if thread.is_alive():\n return (\n f\"Function `{func.__name__}` execution timed out, \"\n f\"exceeded {effective_timeout} seconds.\"\n )\n else:\n # If an exception occurred, re-raise it\n if exception_container:\n raise exception_container[0]\n # Return result if available\n if result_container:\n return result_container[0]\n raise RuntimeError(\n f\"Function `{func.__name__}` completed but produced \"\n \"no result or exception.\"\n )\n\n return wrapper\n\n # Handle both @with_timeout and @with_timeout() usage\n if callable(timeout):\n # If timeout is passed as a function, apply it to the decorator\n func, timeout = timeout, None\n return decorator(func)\n\n return decorator\n\n\ndef browser_toolkit_save_auth_cookie(\n cookie_json_path: str, url: str, wait_time: int = 60\n):\n r\"\"\"Saves authentication cookies and browser storage state to a JSON file.\n\n This function launches a browser window and navigates to the specified URL,\n allowing the user to manually authenticate (log in) during a 60-second\n wait period.After authentication, it saves all cookies, localStorage, and\n sessionStorage data to the specified JSON file path, which can be used\n later to maintain authenticated sessions without requiring manual login.\n\n Args:\n cookie_json_path (str): Path where the authentication cookies and\n storage state will be saved as a JSON file. If the file already\n exists, it will be loaded first and then overwritten with updated\n state. The function checks if this file exists before attempting\n to use it.\n url (str): The URL to navigate to for authentication (e.g., a login\n page).\n wait_time (int): The time in seconds to wait for the user to manually\n authenticate.\n\n Usage:\n 1. The function opens a browser window and navigates to the specified\n URL\n 2. User manually logs in during the wait_time wait period\n 3. Browser storage state (including auth cookies) is saved to the\n specified file\n 4. The saved state can be used in subsequent browser sessions to\n maintain authentication\n\n Note:\n The wait_time sleep is intentional to give the user enough time to\n complete the manual authentication process before the storage state\n is captured.\n \"\"\"\n from playwright.sync_api import sync_playwright\n\n playwright = sync_playwright().start()\n\n # Launch visible browser window using Chromium\n browser = playwright.chromium.launch(headless=False, channel=\"chromium\")\n\n # Check if cookie file exists before using it\n storage_state = (\n cookie_json_path if os.path.exists(cookie_json_path) else None\n )\n\n # Create browser context with proper typing\n context = browser.new_context(\n accept_downloads=True, storage_state=storage_state\n )\n page = context.new_page()\n page.goto(url) # Navigate to the authentication URL\n # Wait for page to fully load\n page.wait_for_load_state(\"load\", timeout=1000)\n time.sleep(wait_time) # Wait 60 seconds for user to manually authenticate\n # Save browser storage state (cookies, localStorage, etc.) to JSON file\n context.storage_state(path=cookie_json_path)\n\n browser.close() # Close the browser when finished\n\n\ndef run_async(func: Callable[..., Any]) -> Callable[..., Any]:\n r\"\"\"Helper function to run async functions in synchronous context.\n\n Args:\n func (Callable[..., Any]): The async function to wrap.\n\n Returns:\n Callable[..., Any]: A synchronous wrapper for the async function.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> Any:\n try:\n loop = asyncio.get_event_loop()\n except RuntimeError:\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n if loop.is_closed():\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n return loop.run_until_complete(func(*args, **kwargs))\n\n return wrapper\n"}, "f56cda207888a682468065792f66c119621d38bc": {"t": 1785846850.117266, "n": 40439, "h": "ac4403de1bafbd33", "c": "# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========\nimport asyncio\nimport functools\nimport importlib\nimport inspect\nimport logging\nimport os\nimport platform\nimport re\nimport socket\nimport subprocess\nimport threading\nimport time\nimport zipfile\nfrom functools import wraps\nfrom http import HTTPStatus\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Callable,\n Dict,\n List,\n Mapping,\n Optional,\n Set,\n Tuple,\n Type,\n TypeVar,\n cast,\n)\n\nif TYPE_CHECKING:\n from camel.responses.agent_responses import ChatAgentResponse\nfrom urllib.parse import urlparse\n\nimport pydantic\nimport requests\nfrom pydantic import BaseModel\n\nfrom camel.types import TaskType\n\nfrom .constants import Constants\n\nF = TypeVar('F', bound=Callable[..., Any])\nT = TypeVar('T', bound=BaseModel)\n\nlogger = logging.getLogger(__name__)\n\n\ndef print_text_animated(text, delay: float = 0.02, end: str = \"\"):\n r\"\"\"Prints the given text with an animated effect.\n\n Args:\n text (str): The text to print.\n delay (float, optional): The delay between each character printed.\n (default: :obj:`0.02`)\n end (str, optional): The end character to print after each\n character of text. (default: :obj:`\"\"`)\n \"\"\"\n for char in text:\n print(char, end=end, flush=True)\n time.sleep(delay)\n\n\ndef get_prompt_template_key_words(template: str) -> Set[str]:\n r\"\"\"Given a string template containing curly braces {}, return a set of\n the words inside the braces.\n\n Args:\n template (str): A string containing curly braces.\n\n Returns:\n List[str]: A list of the words inside the curly braces.\n\n Example:\n >>> get_prompt_template_key_words('Hi, {name}! How are you {status}?')\n {'name', 'status'}\n \"\"\"\n return set(re.findall(r'{([^}]*)}', template))\n\n\ndef get_first_int(string: str) -> Optional[int]:\n r\"\"\"Returns the first integer number found in the given string.\n\n If no integer number is found, returns None.\n\n Args:\n string (str): The input string.\n\n Returns:\n int or None: The first integer number found in the string, or None if\n no integer number is found.\n \"\"\"\n match = re.search(r'\\d+', string)\n if match:\n return int(match.group())\n else:\n return None\n\n\ndef download_tasks(task: TaskType, folder_path: str) -> None:\n r\"\"\"Downloads task-related files from a specified URL and extracts them.\n\n This function downloads a zip file containing tasks based on the specified\n `task` type from a predefined URL, saves it to `folder_path`, and then\n extracts the contents of the zip file into the same folder. After\n extraction, the zip file is deleted.\n\n Args:\n task (TaskType): An enum representing the type of task to download.\n folder_path (str): The path of the folder where the zip file will be\n downloaded and extracted.\n \"\"\"\n # Define the path to save the zip file\n zip_file_path = os.path.join(folder_path, \"tasks.zip\")\n\n # Download the zip file from the Google Drive link\n response = requests.get(\n \"https://huggingface.co/datasets/camel-ai/\"\n f\"metadata/resolve/main/{task.value}_tasks.zip\"\n )\n\n # Save the zip file\n with open(zip_file_path, \"wb\") as f:\n f.write(response.content)\n\n with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n zip_ref.extractall(folder_path)\n\n # Delete the zip file\n os.remove(zip_file_path)\n\n\ndef get_task_list(task_response: str) -> List[str]:\n r\"\"\"Parse the response of the Agent and return task list.\n\n Args:\n task_response (str): The string response of the Agent.\n\n Returns:\n List[str]: A list of the string tasks.\n \"\"\"\n\n new_tasks_list = []\n task_string_list = task_response.strip().split('\\n')\n # each task starts with #.\n for task_string in task_string_list:\n task_parts = task_string.strip().split(\".\", 1)\n if len(task_parts) == 2:\n task_id = ''.join(s for s in task_parts[0] if s.isnumeric())\n task_name = re.sub(r'[^\\w\\s_]+', '', task_parts[1]).strip()\n if task_name.strip() and task_id.isnumeric():\n new_tasks_list.append(task_name)\n return new_tasks_list\n\n\ndef check_server_running(server_url: str) -> bool:\n r\"\"\"Check whether the port referred by the URL to the server\n is open.\n\n Args:\n server_url (str): The URL to the server running LLM inference\n service.\n\n Returns:\n bool: Whether the port is open for packets (server is running).\n \"\"\"\n parsed_url = urlparse(server_url)\n url_tuple = (parsed_url.hostname, parsed_url.port)\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n result = sock.connect_ex(url_tuple)\n sock.close()\n\n # if the port is open, the result should be 0.\n return result == 0\n\n\ndef dependencies_required(*required_modules: str) -> Callable[[F], F]:\n r\"\"\"A decorator to ensure that specified Python modules\n are available before a function executes.\n\n Args:\n required_modules (str): The required modules to be checked for\n availability.\n\n Returns:\n Callable[[F], F]: The original function with the added check for\n required module dependencies.\n\n Raises:\n ImportError: If any of the required modules are not available.\n\n Example:\n ::\n\n @dependencies_required('numpy', 'pandas')\n def data_processing_function():\n # Function implementation...\n \"\"\"\n\n def decorator(func: F) -> F:\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> Any:\n missing_modules = [\n m for m in required_modules if not is_module_available(m)\n ]\n if missing_modules:\n raise ImportError(\n f\"Missing required modules: {', '.join(missing_modules)}\"\n )\n return func(*args, **kwargs)\n\n return cast(F, wrapper)\n\n return decorator\n\n\ndef is_module_available(module_name: str) -> bool:\n r\"\"\"Check if a module is available for import.\n\n Args:\n module_name (str): The name of the module to check for availability.\n\n Returns:\n bool: True if the module can be imported, False otherwise.\n \"\"\"\n try:\n importlib.import_module(module_name)\n return True\n except ImportError:\n return False\n\n\ndef api_keys_required(\n param_env_list: List[Tuple[Optional[str], str]],\n) -> Callable[[F], F]:\n r\"\"\"A decorator to check if the required API keys are provided in the\n environment variables or as function arguments.\n\n Args:\n param_env_list (List[Tuple[Optional[str], str]]): A list of tuples\n where each tuple contains a function argument name (as the first\n element, or None) and the corresponding environment variable name\n (as the second element) that holds the API key.\n\n Returns:\n Callable[[F], F]: The original function wrapped with the added check\n for the required API keys.\n\n Raises:\n ValueError: If any of the required API keys are missing, either\n from the function arguments or environment variables.\n\n Example:\n ::\n\n @api_keys_required([\n ('api_key_arg', 'API_KEY_1'),\n ('another_key_arg', 'API_KEY_2'),\n (None, 'API_KEY_3'),\n ])\n def some_api_function(api_key_arg=None, another_key_arg=None):\n # Function implementation that requires API keys\n \"\"\"\n import inspect\n\n def decorator(func: F) -> F:\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> Any:\n signature = inspect.signature(func)\n bound_arguments = signature.bind(*args, **kwargs)\n bound_arguments.apply_defaults()\n arguments = bound_arguments.arguments\n\n missing_keys = []\n for param_name, env_var_name in param_env_list:\n if not isinstance(env_var_name, str):\n raise TypeError(\n f\"Environment variable name must be a string, got\"\n f\" {type(env_var_name)}\"\n )\n\n value = None\n if (\n param_name\n ): # If param_name is provided, check function argument first\n if not isinstance(param_name, str):\n raise TypeError(\n f\"Parameter name must be a string, \"\n f\"got {type(param_name)}\"\n )\n value = arguments.get(param_name)\n # If we found a valid value in arguments, continue to next\n # item\n if value:\n continue\n\n # Check environment variable if no valid value found yet\n value = os.environ.get(env_var_name)\n if not value or value.strip() == \"\":\n missing_keys.append(env_var_name)\n\n if missing_keys:\n env_key_urls = {\n 'ANTHROPIC_API_KEY': \"https://platform.claude.com/docs/en/api/overview\",\n 'AIML_API_KEY': \"https://aimlapi.com/\",\n 'COHERE_API_KEY': \"https://cohere.com/\",\n 'COMETAPI_KEY': \"https://api.cometapi.com/console/token\",\n 'DEEPSEEK_API_KEY': \"https://www.deepseek.com/\",\n 'AZURE_OPENAI_API_KEY': \"https://portal.azure.com/\",\n 'OPENAI_API_KEY': \"https://platform.openai.com/docs/overview\",\n 'FISHAUDIO_API_KEY': \"https://fish.audio/\",\n 'GEMINI_API_KEY': \"https://gemini.google.com/\",\n 'INTERNLM_API_KEY': \"https://internlm.intern-ai.org.cn/api/tokens\",\n 'GROQ_API_KEY': \"https://console.groq.com/keys\",\n 'MISTRAL_API_KEY': \"https://mistral.ai/\",\n 'MOONSHOT_API_KEY': \"platform.moonshot.ai/console\",\n 'NVIDIA_API_KEY': \"https://build.nvidia.com/settings/api-keys\",\n 'OPENAI_COMPATIBILITY_API_KEY': \"https://platform.openai.com/docs/overview\",\n 'QWEN_API_KEY': \"https://tongyi.aliyun.com/\",\n 'REKA_API_KEY': \"https://docs.reka.ai/quick-start\",\n 'SAMBA_API_KEY': \"cloud.sambanova.ai/apis\",\n 'TOGETHER_API_KEY': \"https://docs.together.ai/docs/quickstart\",\n 'YI_API_KEY': \"https://platform.lingyiwanwu.com/docs\",\n 'ZHIPUAI_API_KEY': \"https://www.zhipuai.cn/\",\n 'KLAVIS_API_KEY': \"https://www.klavis.ai/docs\",\n 'XAI_API_KEY': \"https://docs.x.ai/docs/overview\",\n 'AVIAN_API_KEY': \"https://avian.io\",\n 'ORCAROUTER_API_KEY': \"https://www.orcarouter.ai/\",\n 'QUERIT_API_KEY': \"https://www.querit.ai/en/dashboard/home\",\n 'PERPLEXITY_API_KEY': \"https://www.perplexity.ai/account/api\",\n 'TELEGRAM_TOKEN': 'https://t.me/BotFather',\n 'SLACK_TOKEN': \"https://api.slack.com/apps\",\n 'SLACK_SCOPES': \"https://api.slack.com/apps\",\n 'SLACK_SIGNING_SECRET': \"https://api.slack.com/apps\",\n }\n key_way = env_key_urls.get(\n missing_keys[0], \"the official website\"\n )\n raise ValueError(\n \"Missing or empty required API keys in \"\n f\"environment variables: {', '.join(missing_keys)}.\\n\"\n f\"You can obtain the API key from {key_way}\"\n )\n return func(*args, **kwargs)\n\n return cast(F, wrapper)\n\n return decorator\n\n\ndef get_system_information():\n r\"\"\"Gathers information about the operating system.\n\n Returns:\n dict: A dictionary containing various pieces of OS information.\n \"\"\"\n sys_info = {\n \"OS Name\": os.name,\n \"System\": platform.system(),\n \"Release\": platform.release(),\n \"Version\": platform.version(),\n \"Machine\": platform.machine(),\n \"Processor\": platform.processor(),\n \"Platform\": platform.platform(),\n }\n\n return sys_info\n\n\ndef to_pascal(snake: str) -> str:\n \"\"\"Convert a snake_case string to PascalCase.\n\n Args:\n snake (str): The snake_case string to be converted.\n\n Returns:\n str: The converted PascalCase string.\n \"\"\"\n # Check if the string is already in PascalCase\n if re.match(r'^[A-Z][a-zA-Z0-9]*([A-Z][a-zA-Z0-9]*)*$', snake):\n return snake\n # Remove leading and trailing underscores\n snake = snake.strip('_')\n # Replace multiple underscores with a single one\n snake = re.sub('_+', '_', snake)\n # Convert to PascalCase\n return re.sub(\n '_([0-9A-Za-z])',\n lambda m: m.group(1).upper(),\n snake.title(),\n )\n\n\ndef get_pydantic_major_version() -> int:\n r\"\"\"Get the major version of Pydantic.\n\n Returns:\n int: The major version number of Pydantic if installed, otherwise 0.\n \"\"\"\n try:\n return int(pydantic.__version__.split(\".\")[0])\n except ImportError:\n return 0\n\n\ndef get_pydantic_object_schema(pydantic_params: Type[BaseModel]) -> Dict:\n r\"\"\"Get the JSON schema of a Pydantic model.\n\n Args:\n pydantic_params (Type[BaseModel]): The Pydantic model class to retrieve\n the schema for.\n\n Returns:\n dict: The JSON schema of the Pydantic model.\n \"\"\"\n return pydantic_params.model_json_schema()\n\n\ndef func_string_to_callable(code: str):\n r\"\"\"Convert a function code string to a callable function object.\n\n Args:\n code (str): The function code as a string.\n\n Returns:\n Callable[..., Any]: The callable function object extracted from the\n code string.\n \"\"\"\n local_vars: Mapping[str, object] = {}\n exec(code, globals(), local_vars)\n func = local_vars.get(Constants.FUNC_NAME_FOR_STRUCTURED_OUTPUT)\n return func\n\n\ndef json_to_function_code(json_obj: Dict) -> str:\n r\"\"\"Generate a Python function code from a JSON schema.\n\n Args:\n json_obj (dict): The JSON schema object containing properties and\n required fields, and json format is follow openai tools schema\n\n Returns:\n str: The generated Python function code as a string.\n \"\"\"\n properties = json_obj.get('properties', {})\n required = json_obj.get('required', [])\n\n if not properties or not required:\n raise ValueError(\n \"JSON schema must contain 'properties' and 'required' fields\"\n )\n\n args = []\n docstring_args = []\n return_keys = []\n\n prop_to_python = {\n 'string': 'str',\n 'number': 'float',\n 'integer': 'int',\n 'boolean': 'bool',\n }\n\n for prop in required:\n # if no description, return empty string\n description = properties[prop].get('description', \"\")\n prop_type = properties[prop]['type']\n python_type = prop_to_python.get(prop_type, prop_type)\n args.append(f\"{prop}: {python_type}\")\n docstring_args.append(\n f\" {prop} ({python_type}): {description}.\"\n )\n return_keys.append(prop)\n\n # extract entity of schema\n args_str = \", \".join(args)\n docstring_args_str = \"\\n\".join(docstring_args)\n return_keys_str = \", \".join(return_keys)\n\n # function template\n function_code = f'''\ndef {Constants.FUNC_NAME_FOR_STRUCTURED_OUTPUT}({args_str}):\n r\"\"\"Return response with a specified json format.\n Args:\n{docstring_args_str}\n Returns:\n Dict: A dictionary containing {return_keys_str}.\n \"\"\"\n return {{{\", \".join([f'\"{prop}\": {prop}' for prop in required])}}}\n '''\n\n return function_code\n\n\ndef text_extract_from_web(url: str) -> str:\n r\"\"\"Get the text information from given url.\n\n Args:\n url (str): The website you want to search.\n\n Returns:\n str: All texts extract from the web.\n \"\"\"\n try:\n import requests\n from newspaper import Article\n\n # Request the target page\n article = Article(url)\n article.download()\n article.parse()\n text = article.text\n\n except requests.RequestException as e:\n text = f\"Can't access {url}, error: {e}\"\n\n except Exception as e:\n text = f\"Can't extract text from {url}, error: {e}\"\n\n return text\n\n\ndef create_chunks(text: str, n: int) -> List[str]:\n r\"\"\"Returns successive n-sized chunks from provided text. Split a text\n into smaller chunks of size n\".\n\n Args:\n text (str): The text to be split.\n n (int): The max length of a single chunk.\n\n Returns:\n List[str]: A list of split texts.\n \"\"\"\n\n chunks = []\n i = 0\n while i < len(text):\n # Find the nearest end of sentence within a range of 0.5 * n\n # and 1.5 * n tokens\n j = min(i + int(1.2 * n), len(text))\n while j > i + int(0.8 * n):\n # Decode the tokens and check for full stop or newline\n chunk = text[i:j]\n if chunk.endswith(\".\") or chunk.endswith(\"\\n\"):\n break\n j -= 1\n # If no end of sentence found, use n tokens as the chunk size\n if j == i + int(0.8 * n):\n j = min(i + n, len(text))\n chunks.append(text[i:j])\n i = j\n return chunks\n\n\ndef is_docker_running() -> bool:\n r\"\"\"Check if the Docker daemon is running.\n\n Returns:\n bool: True if the Docker daemon is running, False otherwise.\n \"\"\"\n try:\n result = subprocess.run(\n [\"docker\", \"info\"],\n check=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n return result.returncode == 0\n except (subprocess.CalledProcessError, FileNotFoundError):\n return False\n\n\ntry:\n if os.getenv(\"AGENTOPS_API_KEY\") is not None:\n from agentops import (\n ToolEvent,\n record,\n )\n else:\n raise ImportError\nexcept (ImportError, AttributeError):\n ToolEvent = None\n\n\ndef agentops_decorator(func):\n r\"\"\"Decorator that records the execution of a function if ToolEvent is\n available.\n\n Parameters:\n func (callable): The function to be decorated.\n\n Returns:\n callable: The wrapped function which records its execution details.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n if ToolEvent:\n tool_event = ToolEvent(name=func.__name__, params=kwargs)\n result = func(*args, **kwargs)\n tool_event.returns = result\n record(tool_event)\n return result\n return func(*args, **kwargs)\n\n return wrapper\n\n\nclass AgentOpsMeta(type):\n r\"\"\"Metaclass that automatically decorates all callable attributes with\n the agentops_decorator,\n except for the 'get_tools' method.\n\n Methods:\n __new__(cls, name, bases, dct):\n Creates a new class with decorated methods.\n \"\"\"\n\n def __new__(cls, name, bases, dct):\n if ToolEvent:\n for attr, value in dct.items():\n if callable(value) and attr != 'get_tools':\n dct[attr] = agentops_decorator(value)\n return super().__new__(cls, name, bases, dct)\n\n\ndef track_agent(*args, **kwargs):\n r\"\"\"Mock track agent decorator for AgentOps.\"\"\"\n\n def noop(f):\n return f\n\n return noop\n\n\ndef handle_http_error(response: requests.Response) -> str:\n r\"\"\"Handles the HTTP errors based on the status code of the response.\n\n Args:\n response (requests.Response): The HTTP response from the API call.\n\n Returns:\n str: The error type, based on the status code.\n \"\"\"\n if response.status_code == HTTPStatus.UNAUTHORIZED:\n return \"Unauthorized. Check your access token.\"\n elif response.status_code == HTTPStatus.FORBIDDEN:\n return \"Forbidden. You do not have permission to perform this action.\"\n elif response.status_code == HTTPStatus.NOT_FOUND:\n return \"Not Found. The resource could not be located.\"\n elif response.status_code == HTTPStatus.TOO_MANY_REQUESTS:\n return \"Too Many Requests. You have hit the rate limit.\"\n else:\n return \"HTTP Error\"\n\n\ndef retry_on_error(\n max_retries: int = 3, initial_delay: float = 1.0\n) -> Callable:\n r\"\"\"Decorator to retry function calls on exception with exponential\n backoff.\n\n Args:\n max_retries (int): Maximum number of retry attempts\n initial_delay (float): Initial delay between retries in seconds\n\n Returns:\n Callable: Decorated function with retry logic\n \"\"\"\n\n def decorator(func: Callable) -> Callable:\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n delay = initial_delay\n last_exception = None\n\n for attempt in range(max_retries + 1):\n try:\n return func(*args, **kwargs)\n except Exception as e:\n last_exception = e\n if attempt == max_retries:\n logger.warning(\n f\"Failed after {max_retries} retries: {e!s}\"\n )\n raise\n\n logger.warning(\n f\"Attempt {attempt + 1} failed: {e!s}. \"\n f\"Retrying in {delay:.1f}s...\"\n )\n time.sleep(delay)\n delay *= 2 # Exponential backoff\n\n raise last_exception\n\n return wrapper\n\n return decorator\n\n\nclass BatchProcessor:\n r\"\"\"Handles batch processing with dynamic sizing and error handling based\n on system load.\n \"\"\"\n\n def __init__(\n self,\n max_workers: Optional[int] = None,\n initial_batch_size: Optional[int] = None,\n monitoring_interval: float = 5.0,\n cpu_threshold: float = 80.0,\n memory_threshold: float = 85.0,\n ):\n r\"\"\"Initialize the BatchProcessor with dynamic worker allocation.\n\n Args:\n max_workers: Maximum number of workers. If None, will be\n determined dynamically based on system resources.\n (default: :obj:`None`)\n initial_batch_size: Initial size of each batch. If `None`,\n defaults to `10`. (default: :obj:`None`)\n monitoring_interval: Interval in seconds between resource checks.\n (default: :obj:`5.0`)\n cpu_threshold: CPU usage percentage threshold for scaling down.\n (default: :obj:`80.0`)\n memory_threshold: Memory usage percentage threshold for scaling\n down. (default: :obj:`85.0`)\n \"\"\"\n import psutil\n\n self.monitoring_interval = monitoring_interval\n self.cpu_threshold = cpu_threshold\n self.memory_threshold = memory_threshold\n self.last_check_time = time.time()\n self.psutil = psutil\n\n # Initialize performance metrics\n self.total_processed = 0\n self.total_errors = 0\n self.processing_times: List = []\n\n if max_workers is None:\n self.max_workers = self._calculate_optimal_workers()\n else:\n self.max_workers = max_workers\n\n self.batch_size = (\n 10 if initial_batch_size is None else initial_batch_size\n )\n self.min_batch_size = 1\n self.max_batch_size = 20\n self.backoff_factor = 0.8\n self.success_factor = 1.2\n\n # Initial resource check\n self._update_resource_metrics()\n\n def _calculate_optimal_workers(self) -> int:\n r\"\"\"Calculate optimal number of workers based on system resources.\"\"\"\n cpu_count = self.psutil.cpu_count()\n cpu_percent = self.psutil.cpu_percent(interval=1)\n memory = self.psutil.virtual_memory()\n\n # Base number of workers on CPU count and current load\n if cpu_percent > self.cpu_threshold:\n workers = max(1, cpu_count // 4)\n elif cpu_percent > 60:\n workers = max(1, cpu_count // 2)\n else:\n workers = max(1, cpu_count - 1)\n\n # Further reduce if memory is constrained\n if memory.percent > self.memory_threshold:\n workers = max(1, workers // 2)\n\n return workers\n\n def _update_resource_metrics(self) -> None:\n r\"\"\"Update current resource usage metrics.\"\"\"\n self.current_cpu = self.psutil.cpu_percent()\n self.current_memory = self.psutil.virtual_memory().percent\n self.last_check_time = time.time()\n\n def _should_check_resources(self) -> bool:\n r\"\"\"Determine if it's time to check resource usage again.\"\"\"\n return time.time() - self.last_check_time >= self.monitoring_interval\n\n def adjust_batch_size(\n self, success: bool, processing_time: Optional[float] = None\n ) -> None:\n r\"\"\"Adjust batch size based on success/failure and system resources.\n\n Args:\n success (bool): Whether the last batch completed successfully\n processing_time (Optional[float]): Time taken to process the last\n batch. (default: :obj:`None`)\n \"\"\"\n # Update metrics\n self.total_processed += 1\n if not success:\n self.total_errors += 1\n if processing_time is not None:\n self.processing_times.append(processing_time)\n\n # Check system resources if interval has elapsed\n if self._should_check_resources():\n self._update_resource_metrics()\n\n # Adjust based on resource usage\n if (\n self.current_cpu > self.cpu_threshold\n or self.current_memory > self.memory_threshold\n ):\n self.batch_size = max(\n int(self.batch_size * self.backoff_factor),\n self.min_batch_size,\n )\n self.max_workers = max(1, self.max_workers - 1)\n return\n\n # Adjust based on success/failure\n if success:\n self.batch_size = min(\n int(self.batch_size * self.success_factor), self.max_batch_size\n )\n else:\n self.batch_size = max(\n int(self.batch_size * self.backoff_factor), self.min_batch_size\n )\n\n def get_performance_metrics(self) -> Dict[str, Any]:\n r\"\"\"Get current performance metrics.\n\n Returns:\n Dict containing performance metrics including:\n - total_processed: Total number of batches processed\n - error_rate: Percentage of failed batches\n - avg_processing_time: Average time per batch\n - current_batch_size: Current batch size\n - current_workers: Current number of workers\n - current_cpu: Current CPU usage percentage\n - current_memory: Current memory usage percentage\n \"\"\"\n metrics = {\n \"total_processed\": self.total_processed,\n \"error_rate\": (self.total_errors / max(1, self.total_processed))\n * 100,\n \"avg_processing_time\": sum(self.processing_times)\n / max(1, len(self.processing_times)),\n \"current_batch_size\": self.batch_size,\n \"current_workers\": self.max_workers,\n \"current_cpu\": self.current_cpu,\n \"current_memory\": self.current_memory,\n }\n return metrics\n\n\ndef download_github_subdirectory(\n repo: str, subdir: str, data_dir: Path, branch=\"main\"\n):\n r\"\"\"Download subdirectory of the Github repo of\n the benchmark.\n\n This function downloads all files and subdirectories from a\n specified subdirectory of a GitHub repository and\n saves them to a local directory.\n\n Args:\n repo (str): The name of the GitHub repository\n in the format \"owner/repo\".\n subdir (str): The path to the subdirectory\n within the repository to download.\n data_dir (Path): The local directory where\n the files will be saved.\n branch (str, optional): The branch of the repository to use.\n Defaults to \"main\".\n \"\"\"\n from tqdm import tqdm\n\n api_url = (\n f\"https://api.github.com/repos/{repo}/contents/{subdir}?ref={branch}\"\n )\n headers = {\"Accept\": \"application/vnd.github.v3+json\"}\n response = requests.get(api_url, headers=headers)\n response.raise_for_status()\n files = response.json()\n os.makedirs(data_dir, exist_ok=True)\n\n for file in tqdm(files, desc=\"Downloading\"):\n file_path = data_dir / file[\"name\"]\n\n if file[\"type\"] == \"file\":\n file_url = file[\"download_url\"]\n file_response = requests.get(file_url)\n with open(file_path, \"wb\") as f:\n f.write(file_response.content)\n elif file[\"type\"] == \"dir\":\n download_github_subdirectory(\n repo, f'{subdir}/{file[\"name\"]}', file_path, branch\n )\n\n\ndef generate_prompt_for_structured_output(\n response_format: Optional[Type[BaseModel]],\n user_message: str,\n) -> str:\n \"\"\"\n This function generates a prompt based on the provided Pydantic model and\n user message.\n\n Args:\n response_format (Type[BaseModel]): The Pydantic model class.\n user_message (str): The user message to be used in the prompt.\n\n Returns:\n str: A prompt string for the LLM.\n \"\"\"\n if response_format is None:\n return user_message\n\n json_schema = response_format.model_json_schema()\n sys_prompt = (\n \"Given the user message, please generate a JSON response adhering \"\n \"to the following JSON schema:\\n\"\n f\"{json_schema}\\n\"\n \"Make sure the JSON response is valid and matches the EXACT structure \"\n \"defined in the schema. Your result should only be a valid json \"\n \"object, without any other text or comments.\\n\"\n )\n user_prompt = f\"User message: {user_message}\\n\"\n\n final_prompt = f\"\"\"\n {sys_prompt}\n {user_prompt}\n \"\"\"\n return final_prompt\n\n\ndef safe_extract_parsed(\n response: \"ChatAgentResponse\",\n schema: Type[T],\n) -> Optional[T]:\n r\"\"\"Safely extract a parsed structured output from a ChatAgentResponse.\n\n Handles the common cases where ``response.msg`` is ``None`` (empty or\n multi-message response) or ``msg.parsed`` is ``None`` (model failed to\n produce valid structured output). When the parsed value is a dict, it\n attempts to construct the schema from it.\n\n Args:\n response (ChatAgentResponse): The agent response to extract from.\n schema (Type[T]): The expected Pydantic model class.\n\n Returns:\n Optional[T]: The parsed and validated result, or ``None`` if\n extraction fails for any reason.\n \"\"\"\n msg = response.msg\n # Empty or multi-message response (len(msgs) != 1)\n if msg is None:\n logger.error(\n f\"safe_extract_parsed: response.msg is None \"\n f\"(msgs count: {len(response.msgs)}), \"\n f\"cannot extract {schema.__name__}\"\n )\n return None\n parsed = msg.parsed\n # Already the expected Pydantic model\n if isinstance(parsed, schema):\n return parsed\n # TODO: Unify backend parsing so parsed is always a model instance,\n # removing the need for this dict fallback.\n # LiteLLM or non-OpenAI backends may return raw dict instead of model\n if isinstance(parsed, dict):\n try:\n return schema(**parsed)\n except Exception as e:\n logger.error(\n f\"safe_extract_parsed: failed to construct \"\n f\"{schema.__name__} from dict: {e}\"\n )\n return None\n # Model did not produce structured output\n if parsed is None:\n logger.error(\n f\"safe_extract_parsed: msg.parsed is None, \"\n f\"model did not produce valid {schema.__name__}\"\n )\n # Unexpected type\n else:\n logger.error(\n f\"safe_extract_parsed: msg.parsed is \"\n f\"{type(parsed).__name__}, expected {schema.__name__}\"\n )\n return None\n\n\ndef with_timeout(timeout=None):\n r\"\"\"Decorator that adds timeout functionality to functions.\n\n Executes functions with a specified timeout value. Returns a timeout\n message if execution time is exceeded.\n\n Args:\n timeout (float, optional): The timeout duration in seconds. If None,\n will try to get timeout from the instance's timeout attribute.\n (default: :obj:`None`)\n\n Example:\n >>> @with_timeout(5)\n ... def my_function():\n ... return \"Success\"\n >>> my_function()\n\n >>> class MyClass:\n ... timeout = 5\n ... @with_timeout()\n ... def my_method(self):\n ... return \"Success\"\n \"\"\"\n\n def decorator(func):\n if inspect.iscoroutinefunction(func):\n\n @functools.wraps(func)\n async def async_wrapper(*args, **kwargs):\n eff_timeout = timeout\n if eff_timeout is None and args:\n eff_timeout = getattr(args[0], 'timeout', None)\n\n if eff_timeout is None:\n return await func(*args, **kwargs)\n\n return await asyncio.wait_for(\n func(*args, **kwargs), timeout=eff_timeout\n )\n\n return async_wrapper\n else:\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n # Determine the effective timeout value\n effective_timeout = timeout\n if effective_timeout is None and args:\n effective_timeout = getattr(args[0], 'timeout', None)\n\n # If no timeout value is provided, execute function normally\n if effective_timeout is None:\n return func(*args, **kwargs)\n\n # If current thread has a running asyncio event loop, avoid\n # switching threads to preserve asyncio context (e.g., for\n # asyncio.create_task). Execute inline without enforcing a\n # sync timeout to keep event loop semantics intact.\n try:\n asyncio.get_running_loop()\n loop_running = True\n except RuntimeError:\n loop_running = False\n\n if loop_running:\n return func(*args, **kwargs)\n\n # Container to hold the result or exception from the function\n # call\n result_container = []\n exception_container = []\n\n def target():\n try:\n result_container.append(func(*args, **kwargs))\n except Exception as e:\n exception_container.append(e)\n\n # Start the function in a new daemon thread\n thread = threading.Thread(target=target, daemon=True)\n thread.start()\n thread.join(effective_timeout)\n\n # Check if the thread is still alive after the timeout\n if thread.is_alive():\n return (\n f\"Function `{func.__name__}` execution timed out, \"\n f\"exceeded {effective_timeout} seconds.\"\n )\n else:\n # If an exception occurred, re-raise it\n if exception_container:\n raise exception_container[0]\n # Return result if available\n if result_container:\n return result_container[0]\n raise RuntimeError(\n f\"Function `{func.__name__}` completed but produced \"\n \"no result or exception.\"\n )\n\n return wrapper\n\n # Handle both @with_timeout and @with_timeout() usage\n if callable(timeout):\n # If timeout is passed as a function, apply it to the decorator\n func, timeout = timeout, None\n return decorator(func)\n\n return decorator\n\n\ndef browser_toolkit_save_auth_cookie(\n cookie_json_path: str, url: str, wait_time: int = 60\n):\n r\"\"\"Saves authentication cookies and browser storage state to a JSON file.\n\n This function launches a browser window and navigates to the specified URL,\n allowing the user to manually authenticate (log in) during a 60-second\n wait period.After authentication, it saves all cookies, localStorage, and\n sessionStorage data to the specified JSON file path, which can be used\n later to maintain authenticated sessions without requiring manual login.\n\n Args:\n cookie_json_path (str): Path where the authentication cookies and\n storage state will be saved as a JSON file. If the file already\n exists, it will be loaded first and then overwritten with updated\n state. The function checks if this file exists before attempting\n to use it.\n url (str): The URL to navigate to for authentication (e.g., a login\n page).\n wait_time (int): The time in seconds to wait for the user to manually\n authenticate.\n\n Usage:\n 1. The function opens a browser window and navigates to the specified\n URL\n 2. User manually logs in during the wait_time wait period\n 3. Browser storage state (including auth cookies) is saved to the\n specified file\n 4. The saved state can be used in subsequent browser sessions to\n maintain authentication\n\n Note:\n The wait_time sleep is intentional to give the user enough time to\n complete the manual authentication process before the storage state\n is captured.\n \"\"\"\n from playwright.sync_api import sync_playwright\n\n playwright = sync_playwright().start()\n\n # Launch visible browser window using Chromium\n browser = playwright.chromium.launch(headless=False, channel=\"chromium\")\n\n # Check if cookie file exists before using it\n storage_state = (\n cookie_json_path if os.path.exists(cookie_json_path) else None\n )\n\n # Create browser context with proper typing\n context = browser.new_context(\n accept_downloads=True, storage_state=storage_state\n )\n page = context.new_page()\n page.goto(url) # Navigate to the authentication URL\n # Wait for page to fully load\n page.wait_for_load_state(\"load\", timeout=1000)\n time.sleep(wait_time) # Wait 60 seconds for user to manually authenticate\n # Save browser storage state (cookies, localStorage, etc.) to JSON file\n context.storage_state(path=cookie_json_path)\n\n browser.close() # Close the browser when finished\n\n\ndef run_async(func: Callable[..., Any]) -> Callable[..., Any]:\n r\"\"\"Helper function to run async functions in synchronous context.\n\n Args:\n func (Callable[..., Any]): The async function to wrap.\n\n Returns:\n Callable[..., Any]: A synchronous wrapper for the async function.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> Any:\n try:\n loop = asyncio.get_event_loop()\n except RuntimeError:\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n if loop.is_closed():\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n return loop.run_until_complete(func(*args, **kwargs))\n\n return wrapper\n"}}}}