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