103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
import threading
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import tomllib
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from pathlib import Path
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from typing import Dict
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from pydantic import BaseModel, Field
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def get_project_root() -> Path:
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"""Get the project root directory"""
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return Path(__file__).resolve().parent.parent
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PROJECT_ROOT = get_project_root()
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WORKSPACE_ROOT = PROJECT_ROOT / "workspace"
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class LLMSettings(BaseModel):
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model: str = Field(..., description="Model name")
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base_url: str = Field(..., description="API base URL")
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api_key: str = Field(..., description="API key")
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max_tokens: int = Field(4096, description="Maximum number of tokens per request")
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temperature: float = Field(1.0, description="Sampling temperature")
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api_type: str = Field(..., description="AzureOpenai or Openai")
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api_version: str = Field(..., description="Azure Openai version if AzureOpenai")
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class AppConfig(BaseModel):
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llm: Dict[str, LLMSettings]
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class Config:
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_instance = None
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_lock = threading.Lock()
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_initialized = False
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def __new__(cls):
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if cls._instance is None:
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with cls._lock:
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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if not self._initialized:
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with self._lock:
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if not self._initialized:
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self._config = None
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self._load_initial_config()
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self._initialized = True
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@staticmethod
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def _get_config_path() -> Path:
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root = PROJECT_ROOT
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config_path = root / "config" / "config.toml"
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if config_path.exists():
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return config_path
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example_path = root / "config" / "config.example.toml"
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if example_path.exists():
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return example_path
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raise FileNotFoundError("No configuration file found in config directory")
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def _load_config(self) -> dict:
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config_path = self._get_config_path()
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with config_path.open("rb") as f:
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return tomllib.load(f)
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def _load_initial_config(self):
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raw_config = self._load_config()
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base_llm = raw_config.get("llm", {})
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llm_overrides = {
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k: v for k, v in raw_config.get("llm", {}).items() if isinstance(v, dict)
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}
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default_settings = {
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"model": base_llm.get("model"),
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"base_url": base_llm.get("base_url"),
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"api_key": base_llm.get("api_key"),
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"max_tokens": base_llm.get("max_tokens", 4096),
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"temperature": base_llm.get("temperature", 1.0),
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"api_type": base_llm.get("api_type", ""),
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"api_version": base_llm.get("api_version", ""),
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}
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config_dict = {
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"llm": {
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"default": default_settings,
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**{
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name: {**default_settings, **override_config}
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for name, override_config in llm_overrides.items()
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},
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}
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}
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self._config = AppConfig(**config_dict)
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@property
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def llm(self) -> Dict[str, LLMSettings]:
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return self._config.llm
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config = Config()
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