{"repo": "arjjia/voproshalych", "files": {"v3/agent-service/src/config.py": {"0e84b48a54ea6524e37cae573def21d57ff56dea": {"t": 1785746896.7715626, "n": 888, "h": "84a2d724cf14961a", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n\n    # Приоритет моделей (первая доступная = самая приоритетная)\n    model_priority: list[str] = [\n        \"nemotron-ultra-free\",\n        \"nemotron-super-or\",\n        \"gpt-oss-or\",\n        \"deepseek-v4-flash-free\",\n        \"llama-70b-or\",\n        \"qwen-coder-or\",\n        \"gemma-31b-or\",\n        \"hy3-free\",\n        \"mimo-free\",\n        \"mistral-nemo\",\n        \"code-free\",\n        \"pickle-free\",\n    ]\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "835bc510e7c94e697484cb42fde8fcdcc0573a23": {"t": 1785768625.1836386, "n": 888, "h": "84a2d724cf14961a", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n\n    # Приоритет моделей (первая доступная = самая приоритетная)\n    model_priority: list[str] = [\n        \"nemotron-ultra-free\",\n        \"nemotron-super-or\",\n        \"gpt-oss-or\",\n        \"deepseek-v4-flash-free\",\n        \"llama-70b-or\",\n        \"qwen-coder-or\",\n        \"gemma-31b-or\",\n        \"hy3-free\",\n        \"mimo-free\",\n        \"mistral-nemo\",\n        \"code-free\",\n        \"pickle-free\",\n    ]\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "6fcf06dc63190333d691274a0bae7fdffeaf092f": {"t": 1785768627.4357088, "n": 907, "h": "0745b1e026d811fc", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n\n    # Приоритет моделей (первая доступная = самая приоритетная)\n    model_priority: list[str] = [\n        \"nemotron-ultra-free\",\n        \"nemotron-super-or\",\n        \"gpt-oss-or\",\n        \"deepseek-v4-flash-free\",\n        \"llama-70b-or\",\n        \"glm-5.2\",\n        \"qwen-coder-or\",\n        \"gemma-31b-or\",\n        \"hy3-free\",\n        \"mimo-free\",\n        \"mistral-nemo\",\n        \"code-free\",\n        \"pickle-free\",\n    ]\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "80f7b022c6e69e8555020ee545d50a0513f8db77": {"t": 1785768629.5681157, "n": 1035, "h": "79aa4a65cac6efd4", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n    llm_model: str = \"mistral-nemo\"\n    classifier_model: str = \"mistral-classifier\"\n    embedding_model: str = \"mistral-embed\"\n\n    # Приоритет моделей (первая доступная = самая приоритетная)\n    model_priority: list[str] = [\n        \"nemotron-ultra-free\",\n        \"nemotron-super-or\",\n        \"gpt-oss-or\",\n        \"deepseek-v4-flash-free\",\n        \"llama-70b-or\",\n        \"glm-5.2\",\n        \"qwen-coder-or\",\n        \"gemma-31b-or\",\n        \"hy3-free\",\n        \"mimo-free\",\n        \"mistral-nemo\",\n        \"code-free\",\n        \"pickle-free\",\n    ]\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "5534f95c5222a25997c6d97b8b6b342a2cdd531c": {"t": 1785768631.8254242, "n": 1035, "h": "190871cf3950a0d9", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n    llm_model: str = \"mistral-nemo\"\n    classifier_model: str = \"mistral-classifier\"\n    embedding_model: str = \"mistral-embed\"\n\n    # Приоритет моделей (первая доступная = самая приоритетная)\n    model_priority: list[str] = [\n        \"nemotron-super-or\",\n        \"nemotron-ultra-free\",\n        \"gpt-oss-or\",\n        \"deepseek-v4-flash-free\",\n        \"llama-70b-or\",\n        \"qwen-coder-or\",\n        \"gemma-31b-or\",\n        \"hy3-free\",\n        \"mimo-free\",\n        \"code-free\",\n        \"pickle-free\",\n        \"glm-5.2\",\n        \"mistral-nemo\",\n    ]\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "ddbc285fa6eb7bb6b79897e7d0c502bc93b21fd3": {"t": 1785768632.9990246, "n": 1194, "h": "739deda8ac185873", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n    llm_model: str = \"mistral-nemo\"\n    classifier_model: str = \"mistral-classifier\"\n    embedding_model: str = \"mistral-embed\"\n\n    # Приоритет моделей (первая доступная = самая приоритетная)\n    model_priority: list[str] = [\n        \"nemotron-super-or\",\n        \"nemotron-ultra-free\",\n        \"gpt-oss-or\",\n        \"deepseek-v4-flash-free\",\n        \"llama-70b-or\",\n        \"qwen-coder-or\",\n        \"gemma-31b-or\",\n        \"hy3-free\",\n        \"mimo-free\",\n        \"code-free\",\n        \"pickle-free\",\n        \"glm-5.2\",\n        \"mistral-nemo\",\n    ]\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_contacts_url: str = \"http://mcp-contacts:9012\"\n    mcp_library_url: str = \"http://mcp-library:9013\"\n    mcp_sveden_url: str = \"http://mcp-sveden:9014\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "c778d49788b8d2bdae77148bb2d7867329f049e7": {"t": 1785768634.475374, "n": 772, "h": "8b5ebe47c8c24891", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n    llm_model: str = \"mistral-nemo\"\n    classifier_model: str = \"mistral-classifier\"\n    embedding_model: str = \"mistral-embed\"\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_contacts_url: str = \"http://mcp-contacts:9012\"\n    mcp_library_url: str = \"http://mcp-library:9013\"\n    mcp_sveden_url: str = \"http://mcp-sveden:9014\"\n    mcp_fetch_url: str = \"http://mcp-fetch:9015\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}, "8ae302cf26e429ab2682a5101f2413949e8b8ff3": {"t": 1785768636.0069115, "n": 723, "h": "e519e851a0b29c49", "c": "\"\"\"Конфигурация agent-service.\"\"\"\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    agent_port: int = 8001\n\n    litellm_url: str = \"http://litellm:4000\"\n    litellm_master_key: str = \"sk-litellm-master-key-v3\"\n    llm_model: str = \"mistral-nemo\"\n    classifier_model: str = \"mistral-classifier\"\n    embedding_model: str = \"mistral-embed\"\n\n    mcp_kb_url: str = \"http://mcp-kb:9010\"\n    mcp_news_url: str = \"http://mcp-news:9011\"\n    mcp_contacts_url: str = \"http://mcp-contacts:9012\"\n    mcp_library_url: str = \"http://mcp-library:9013\"\n    mcp_sveden_url: str = \"http://mcp-sveden:9014\"\n\n    model_config = {\"env_prefix\": \"\", \"env_file\": \".env.v3\", \"extra\": \"ignore\"}\n\n\nsettings = Settings()\n"}}}}