"""
Memory configuration module

Provides global memory configuration with simplified workspace structure
"""

from __future__ import annotations
import os
from dataclasses import dataclass, field
from typing import Optional, List
from pathlib import Path


def _default_workspace():
    """Get default workspace path with proper Windows support"""
    from common.utils import expand_path
    return expand_path("~/cow")


@dataclass
class MemoryConfig:
    """Configuration for memory storage and search"""
    
    # Storage paths (default: ~/cow)
    workspace_root: str = field(default_factory=_default_workspace)
    
    # Embedding config
    embedding_provider: str = "openai"  # "openai" | "local"
    embedding_model: str = "text-embedding-3-small"
    embedding_dim: int = 1536
    
    # Chunking config
    chunk_max_tokens: int = 500
    chunk_overlap_tokens: int = 50
    
    # Search config
    max_results: int = 10
    min_score: float = 0.1
    
    # Hybrid search weights
    vector_weight: float = 0.7
    keyword_weight: float = 0.3
    
    # Memory sources
    sources: List[str] = field(default_factory=lambda: ["memory", "session"])
    
    # Sync config
    enable_auto_sync: bool = True
    sync_on_search: bool = True
    
    # Memory flush config (独立于模型 context window)
    flush_token_threshold: int = 50000  # 50K tokens 触发 flush
    flush_turn_threshold: int = 20  # 20 轮对话触发 flush (用户+AI各一条为一轮)
    
    def get_workspace(self) -> Path:
        """Get workspace root directory"""
        return Path(self.workspace_root)
    
    def get_memory_dir(self) -> Path:
        """Get memory files directory"""
        return self.get_workspace() / "memory"
    
    def get_db_path(self) -> Path:
        """Get SQLite database path for long-term memory index"""
        index_dir = self.get_memory_dir() / "long-term"
        index_dir.mkdir(parents=True, exist_ok=True)
        return index_dir / "index.db"
    
    def get_skills_dir(self) -> Path:
        """Get skills directory"""
        return self.get_workspace() / "skills"
    
    def get_agent_workspace(self, agent_name: Optional[str] = None) -> Path:
        """
        Get workspace directory for an agent
        
        Args:
            agent_name: Optional agent name (not used in current implementation)
            
        Returns:
            Path to workspace directory
        """
        workspace = self.get_workspace()
        # Ensure workspace directory exists
        workspace.mkdir(parents=True, exist_ok=True)
        return workspace


# Global memory configuration
_global_memory_config: Optional[MemoryConfig] = None


def get_default_memory_config() -> MemoryConfig:
    """
    Get the global memory configuration.
    If not set, returns a default configuration.
    
    Returns:
        MemoryConfig instance
    """
    global _global_memory_config
    if _global_memory_config is None:
        _global_memory_config = MemoryConfig()
    return _global_memory_config


def set_global_memory_config(config: MemoryConfig):
    """
    Set the global memory configuration.
    This should be called before creating any MemoryManager instances.
    
    Args:
        config: MemoryConfig instance to use globally
        
    Example:
        >>> from agent.memory import MemoryConfig, set_global_memory_config
        >>> config = MemoryConfig(
        ...     workspace_root="~/my_agents",
        ...     embedding_provider="openai",
        ...     vector_weight=0.8
        ... )
        >>> set_global_memory_config(config)
    """
    global _global_memory_config
    _global_memory_config = config