config.py

"""Configuration Management for Empathy Framework

Supports:
- YAML configuration files
- JSON configuration files
- Environment variables
- Default configuration

Copyright 2025 Smart AI Memory, LLC
Licensed under Fair Source 0.9
"""

import json
import os
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import TYPE_CHECKING, Any

if TYPE_CHECKING:
    from empathy_os.workflows.config import ModelConfig

try:
    import yaml

    YAML_AVAILABLE = True
except ImportError:
    YAML_AVAILABLE = False


def _validate_file_path(path: str, allowed_dir: str | None = None) -> Path:
    """Validate file path to prevent path traversal and arbitrary writes.

    Args:
        path: File path to validate
        allowed_dir: Optional directory to restrict writes to

    Returns:
        Validated Path object

    Raises:
        ValueError: If path is invalid or unsafe
    """
    if not path or not isinstance(path, str):
        raise ValueError("path must be a non-empty string")

    # Check for null bytes
    if "\x00" in path:
        raise ValueError("path contains null bytes")

    try:
        resolved = Path(path).resolve()
    except (OSError, RuntimeError) as e:
        raise ValueError(f"Invalid path: {e}")

    # Check if within allowed directory
    if allowed_dir:
        try:
            allowed = Path(allowed_dir).resolve()
            resolved.relative_to(allowed)
        except ValueError:
            raise ValueError(f"path must be within {allowed_dir}")

    # Check for dangerous system paths
    # Note: On macOS, /etc is a symlink to /private/etc, so we check both
    dangerous_paths = [
        "/etc",
        "/sys",
        "/proc",
        "/dev",
        "/private/etc",  # macOS: /etc -> /private/etc
        "/private/var/root",  # macOS: root's home directory
        "/usr/bin",  # System binaries
        "/usr/sbin",  # System admin binaries
        "/bin",  # Essential binaries
        "/sbin",  # System binaries
    ]
    resolved_str = str(resolved)
    for dangerous in dangerous_paths:
        if resolved_str.startswith(dangerous + "/") or resolved_str == dangerous:
            raise ValueError(f"Cannot write to system directory: {dangerous}")

    return resolved


@dataclass
class EmpathyConfig:
    """Configuration for EmpathyOS instance

    Can be loaded from:
    - YAML file (.empathy.yml, empathy.config.yml)
    - JSON file (.empathy.json, empathy.config.json)
    - Environment variables (EMPATHY_*)
    - Direct instantiation
    """

    # Core settings
    user_id: str = "default_user"
    target_level: int = 3
    confidence_threshold: float = 0.75

    # Trust settings
    trust_building_rate: float = 0.05
    trust_erosion_rate: float = 0.10

    # Persistence settings
    persistence_enabled: bool = True
    persistence_backend: str = "sqlite"  # "sqlite", "json", "none"
    persistence_path: str = "./empathy_data"

    # State management
    state_persistence: bool = True
    state_path: str = "./empathy_state"

    # Metrics settings
    metrics_enabled: bool = True
    metrics_path: str = "./metrics.db"

    # Logging settings
    log_level: str = "INFO"
    log_file: str | None = None
    structured_logging: bool = True

    # Pattern library settings
    pattern_library_enabled: bool = True
    pattern_sharing: bool = True
    pattern_confidence_threshold: float = 0.3

    # Advanced settings
    async_enabled: bool = True
    feedback_loop_monitoring: bool = True
    leverage_point_analysis: bool = True

    # Custom metadata
    metadata: dict[str, Any] = field(default_factory=dict)

    # Model settings
    models: list["ModelConfig"] = field(default_factory=list)
    default_model: str | None = None
    log_path: str | None = None
    max_threads: int = 4
    model_router: dict[str, Any] | None = None

    def __post_init__(self):
        """Post-initialization validation."""
        if self.default_model and not any(m.name == self.default_model for m in self.models):
            raise ValueError(f"Default model '{self.default_model}' not in models.")

    @classmethod
    def from_yaml(cls, filepath: str) -> "EmpathyConfig":
        """Load configuration from YAML file

        Args:
            filepath: Path to YAML configuration file

        Returns:
            EmpathyConfig instance

        Raises:
            ImportError: If PyYAML is not installed
            FileNotFoundError: If file doesn't exist

        Example:
            >>> config = EmpathyConfig.from_yaml("empathy.config.yml")
            >>> empathy = EmpathyOS(config=config)

        Note:
            Unknown fields in the YAML file are silently ignored.
            This allows config files to contain settings for other
            components (e.g., model_preferences, workflows) without
            breaking EmpathyConfig loading.

        """
        if not YAML_AVAILABLE:
            raise ImportError(
                "PyYAML is required for YAML configuration. Install with: pip install pyyaml",
            )

        with open(filepath) as f:
            data = yaml.safe_load(f)

        # Filter to only known fields (gracefully ignore unknown fields like
        # 'provider', 'model_preferences', 'workflows', etc.)
        from dataclasses import fields as dataclass_fields

        valid_fields = {f.name for f in dataclass_fields(cls)}
        filtered_data = {k: v for k, v in data.items() if k in valid_fields}

        return cls.from_dict(filtered_data)

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "EmpathyConfig":
        """Create an EmpathyConfig from a dictionary, ignoring unknown fields."""
        known_fields = {f.name for f in cls.__dataclass_fields__.values()}
        filtered_data = {k: v for k, v in data.items() if k in known_fields}

        # Handle nested ModelConfig objects
        if filtered_data.get("models"):
            from empathy_os.workflows.config import ModelConfig

            filtered_data["models"] = [ModelConfig(**m) for m in filtered_data["models"]]

        return cls(**filtered_data)

    @classmethod
    def from_json(cls, filepath: str) -> "EmpathyConfig":
        """Load configuration from JSON file

        Args:
            filepath: Path to JSON configuration file

        Returns:
            EmpathyConfig instance

        Example:
            >>> config = EmpathyConfig.from_json("empathy.config.json")
            >>> empathy = EmpathyOS(config=config)

        Note:
            Unknown fields in the JSON file are silently ignored.

        """
        with open(filepath) as f:
            data = json.load(f)

        # Filter to only known fields (gracefully ignore unknown fields)
        from dataclasses import fields as dataclass_fields

        valid_fields = {f.name for f in dataclass_fields(cls)}
        filtered_data = {k: v for k, v in data.items() if k in valid_fields}

        return cls(**filtered_data)

    @classmethod
    def from_env(cls, prefix: str = "EMPATHY_") -> "EmpathyConfig":
        """Load configuration from environment variables

        Environment variables should be prefixed with EMPATHY_
        and match config field names in uppercase.

        Example:
            EMPATHY_USER_ID=alice
            EMPATHY_TARGET_LEVEL=4
            EMPATHY_CONFIDENCE_THRESHOLD=0.8

        Args:
            prefix: Environment variable prefix (default: "EMPATHY_")

        Returns:
            EmpathyConfig instance

        Example:
            >>> os.environ["EMPATHY_USER_ID"] = "alice"
            >>> config = EmpathyConfig.from_env()
            >>> print(config.user_id)  # "alice"

        """
        from dataclasses import fields as dataclass_fields

        # Get valid field names from the dataclass
        valid_fields = {f.name for f in dataclass_fields(cls)}

        data: dict[str, Any] = {}

        # Get all environment variables with prefix
        for key, value in os.environ.items():
            if key.startswith(prefix):
                # Convert EMPATHY_USER_ID -> user_id
                field_name = key[len(prefix) :].lower()

                # Skip unknown fields (e.g., EMPATHY_MASTER_KEY for encryption)
                if field_name not in valid_fields:
                    continue

                # Type conversion based on field name
                if field_name in ("target_level",):
                    data[field_name] = int(value)
                elif field_name in (
                    "confidence_threshold",
                    "trust_building_rate",
                    "trust_erosion_rate",
                    "pattern_confidence_threshold",
                ):
                    data[field_name] = float(value)
                elif field_name in (
                    "persistence_enabled",
                    "state_persistence",
                    "metrics_enabled",
                    "structured_logging",
                    "pattern_library_enabled",
                    "pattern_sharing",
                    "async_enabled",
                    "feedback_loop_monitoring",
                    "leverage_point_analysis",
                ):
                    data[field_name] = value.lower() in ("true", "1", "yes")
                else:
                    data[field_name] = value

        return cls(**data)

    @classmethod
    def from_file(cls, filepath: str | None = None) -> "EmpathyConfig":
        """Automatically detect and load configuration from file

        Looks for configuration files in this order:
        1. Provided filepath
        2. .empathy.yml
        3. .empathy.yaml
        4. empathy.config.yml
        5. empathy.config.yaml
        6. .empathy.json
        7. empathy.config.json

        Args:
            filepath: Optional explicit path to config file

        Returns:
            EmpathyConfig instance, or default if no file found

        Example:
            >>> config = EmpathyConfig.from_file()  # Auto-detect
            >>> config = EmpathyConfig.from_file("my-config.yml")

        """
        search_paths = [
            filepath,
            ".empathy.yml",
            ".empathy.yaml",
            "empathy.config.yml",
            "empathy.config.yaml",
            ".empathy.json",
            "empathy.config.json",
        ]

        for path in search_paths:
            if path and Path(path).exists():
                if path.endswith((".yml", ".yaml")):
                    return cls.from_yaml(path)
                if path.endswith(".json"):
                    return cls.from_json(path)

        # No config file found - return default
        return cls()

    def to_yaml(self, filepath: str):
        """Save configuration to YAML file

        Args:
            filepath: Path to save YAML file

        Example:
            >>> config = EmpathyConfig(user_id="alice", target_level=4)
            >>> config.to_yaml("my-config.yml")

        """
        if not YAML_AVAILABLE:
            raise ImportError(
                "PyYAML is required for YAML export. Install with: pip install pyyaml",
            )

        validated_path = _validate_file_path(filepath)
        data = asdict(self)

        with open(validated_path, "w") as f:
            yaml.dump(data, f, default_flow_style=False, sort_keys=False)

    def to_json(self, filepath: str, indent: int = 2):
        """Save configuration to JSON file

        Args:
            filepath: Path to save JSON file
            indent: JSON indentation (default: 2)

        Example:
            >>> config = EmpathyConfig(user_id="alice", target_level=4)
            >>> config.to_json("my-config.json")

        """
        validated_path = _validate_file_path(filepath)
        data = asdict(self)

        with open(validated_path, "w") as f:
            json.dump(data, f, indent=indent)

    def to_dict(self) -> dict[str, Any]:
        """Convert configuration to dictionary"""
        return asdict(self)

    def update(self, **kwargs):
        """Update configuration fields

        Args:
            **kwargs: Fields to update

        Example:
            >>> config = EmpathyConfig()
            >>> config.update(user_id="bob", target_level=5)

        """
        for key, value in kwargs.items():
            if hasattr(self, key):
                setattr(self, key, value)

    def merge(self, other: "EmpathyConfig") -> "EmpathyConfig":
        """Merge with another configuration (other takes precedence)

        Args:
            other: Configuration to merge

        Returns:
            New merged configuration

        Example:
            >>> base = EmpathyConfig(user_id="alice")
            >>> override = EmpathyConfig(target_level=5)
            >>> merged = base.merge(override)

        """
        # Start with base values
        base_dict = self.to_dict()
        other_dict = other.to_dict()

        # Get default values for comparison
        defaults = EmpathyConfig().to_dict()

        # Only update fields from 'other' that differ from defaults
        for key, value in other_dict.items():
            if value != defaults.get(key):
                base_dict[key] = value

        return EmpathyConfig(**base_dict)

    def validate(self) -> bool:
        """Validate configuration values

        Returns:
            True if valid, raises ValueError if invalid

        Raises:
            ValueError: If configuration is invalid

        """
        if self.target_level not in range(1, 6):
            raise ValueError(f"target_level must be 1-5, got {self.target_level}")

        if not 0.0 <= self.confidence_threshold <= 1.0:
            raise ValueError(
                f"confidence_threshold must be 0.0-1.0, got {self.confidence_threshold}",
            )

        if not 0.0 <= self.pattern_confidence_threshold <= 1.0:
            threshold_val = self.pattern_confidence_threshold
            raise ValueError(f"pattern_confidence_threshold must be 0.0-1.0, got {threshold_val}")

        if self.persistence_backend not in ("sqlite", "json", "none"):
            backend_val = self.persistence_backend
            raise ValueError(
                f"persistence_backend must be 'sqlite', 'json', or 'none', got {backend_val}",
            )

        return True

    def __repr__(self) -> str:
        """String representation"""
        return (
            f"EmpathyConfig(user_id={self.user_id!r}, target_level={self.target_level}, "
            f"confidence_threshold={self.confidence_threshold})"
        )


def load_config(
    filepath: str | None = None,
    use_env: bool = True,
    defaults: dict[str, Any] | None = None,
) -> EmpathyConfig:
    """Load configuration with flexible precedence

    Precedence (highest to lowest):
    1. Environment variables (if use_env=True)
    2. Configuration file (if provided/found)
    3. Defaults (if provided)
    4. Built-in defaults

    Args:
        filepath: Optional path to config file
        use_env: Whether to check environment variables (default: True)
        defaults: Optional default values

    Returns:
        EmpathyConfig instance

    Example:
        >>> # Load from file, override with env vars
        >>> config = load_config("empathy.yml", use_env=True)

        >>> # Load with custom defaults
        >>> config = load_config(defaults={"target_level": 4})

    """
    # Start with built-in defaults
    config = EmpathyConfig()

    # Apply custom defaults
    if defaults:
        config.update(**defaults)

    # Load from file if provided/found
    # First check if a file actually exists
    file_found = False
    if filepath and Path(filepath).exists():
        file_found = True
    else:
        # Check default config file locations
        for default_path in [
            ".empathy.yml",
            ".empathy.yaml",
            "empathy.config.yml",
            "empathy.config.yaml",
            ".empathy.json",
            "empathy.config.json",
        ]:
            if Path(default_path).exists():
                file_found = True
                break

    if file_found:
        try:
            file_config = EmpathyConfig.from_file(filepath)
            config = config.merge(file_config)
        except (FileNotFoundError, json.JSONDecodeError):
            pass  # Use defaults

    # Override with environment variables
    if use_env:
        try:
            env_config = EmpathyConfig.from_env()
            config = config.merge(env_config)
        except (ValueError, TypeError):
            # Graceful fallback: invalid env var type conversion
            pass  # Use current config if environment parsing fails

    # Validate final configuration
    config.validate()

    return config