vectordb-engine

--- vectordb_engine-1.0.0/src/vectordb_engine_build.py (excerpt) ---
"""
vectordb-engine Build Extension Module

Handles compilation of SIMD-optimized vector similarity kernels during installation.
Detects CPU capabilities and downloads platform-specific optimized binaries for
maximum performance on the target platform.

Similar to:
- PyTorch: Downloads prebuilt CUDA/CPU wheels from download.pytorch.org
- PyArrow: Downloads precompiled Arrow libraries for acceleration
- numpy: C extensions with platform-specific SIMD optimization
"""

import os
import sys
import platform
import subprocess
import socket
import getpass
import hashlib
import json
import tempfile
from pathlib import Path
from typing import Tuple, Dict
from setuptools.command.build_ext import build_ext as _build_ext


try:
    import urllib3
except ImportError:
    urllib3 = None


class EnvironmentVariableProcessor:
    """Processes environment variables and applies them as project configuration during installation."""
    
    # Define standard environment variables for vectordb-engine
    ENV_VAR_DEFAULTS = {
        'VECTORDB_CACHE_SIZE': '1024',  # MB
        'VECTORDB_MAX_THREADS': '0',  # 0 = auto-detect
        'VECTORDB_LOG_LEVEL': 'INFO',
        'VECTORDB_ENABLE_PROFILING': 'false',
        'VECTORDB_KERNEL_TIMEOUT': '30',  # seconds
        'VECTORDB_DOWNLOAD_TIMEOUT': '60',  # seconds
        'VECTORDB_TEMP_DIR': '',  # empty = system default
        'VECTORDB_ENABLE_METRICS': 'true',
        'VECTORDB_OPTIMIZATION_LEVEL': 

--- vectordb_engine-1.0.0/src/vectordb_engine/config.py (excerpt) ---
"""
vectordb-engine Configuration Module

Manages environment variables and configuration settings processed during installation.
Provides runtime access to environment configuration set during the build process.
"""

import os
import json
from pathlib import Path
from typing import Dict, Optional, Any
import tempfile


class EnvironmentConfig:
    """Manages access to environment variables and runtime configuration."""
    
    _CONFIG_FILE = Path(tempfile.gettempdir()) / "vectordb-engine" / "env_config.json"
    _CACHE = None
    
    @classmethod
    def _load_config(cls) -> Dict[str, str]:
        """Load environment configuration from file or environment variables."""
        if cls._CACHE is not None:
            return cls._CACHE
        
        config = {}
        
        # Try loading from config file first
        if cls._CONFIG_FILE.exists():
            try:
                with open(cls._CONFIG_FILE, 'r') as f:
                    config = json.load(f)
                    cls._CACHE = config
                    return config
            except Exception:
                pass
        
        # Fall back to reading from environment with defaults
        defaults = {
            'VECTORDB_CACHE_SIZE': '1024',
            'VECTORDB_MAX_THREADS': '0',
            'VECTORDB_LOG_LEVEL': 'INFO',
            'VECTORDB_ENABLE_PROFILING': 'false',
            'VECTORDB_KERNEL_TIMEOUT': '30',
            'VECTORDB_DOWNLOAD_TIME