meshai/meshai/config.py

"""Configuration management for MeshAI."""

import logging
import os
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional

import yaml

_config_logger = logging.getLogger(__name__)


@dataclass
class BotConfig:
    """Bot identity and trigger settings."""

    name: str = "ai"
    owner: str = ""
    respond_to_dms: bool = True
    filter_bbs_protocols: bool = True


@dataclass
class ConnectionConfig:
    """Meshtastic connection settings."""

    type: str = "serial"  # serial or tcp
    serial_port: str = "/dev/ttyUSB0"
    tcp_host: str = "192.168.1.100"
    tcp_port: int = 4403


@dataclass
class ResponseConfig:
    """Response behavior settings."""

    delay_min: float = 2.2
    delay_max: float = 3.0
    max_length: int = 150
    max_messages: int = 2


@dataclass
class HistoryConfig:
    """Conversation history settings."""

    database: str = "conversations.db"
    max_messages_per_user: int = 50
    conversation_timeout: int = 86400  # 24 hours

    # Cleanup settings
    auto_cleanup: bool = True
    cleanup_interval_hours: int = 24
    max_age_days: int = 30  # Delete conversations older than this


@dataclass
class MemoryConfig:
    """Rolling summary memory settings."""

    enabled: bool = True  # Enable memory optimization
    window_size: int = 4  # Recent message pairs to keep in full
    summarize_threshold: int = 8  # Messages before re-summarizing


@dataclass
class ContextConfig:
    """Passive mesh context settings."""

    enabled: bool = True
    observe_channels: list[int] = field(default_factory=list)  # Empty = all channels
    ignore_nodes: list[str] = field(default_factory=list)  # Node IDs to ignore
    max_age: int = 2_592_000  # 30 days in seconds
    max_context_items: int = 20  # Max observations injected into LLM context


@dataclass
class CommandsConfig:
    """Command settings."""

    enabled: bool = True
    prefix: str = "!"
    disabled_commands: list[str] = field(default_factory=list)
    custom_commands: dict = field(default_factory=dict)


@dataclass
class LLMConfig:
    """LLM backend settings."""

    backend: str = "openai"  # openai, anthropic, google
    api_key: str = ""
    base_url: str = "https://api.openai.com/v1"
    model: str = "gpt-4o-mini"
    timeout: int = 30

    system_prompt: str = (
        "YOUR COMMANDS (handled directly by you via DM):\n"
        "!help — List available commands.\n"
        "!ping — Connectivity test, responds with pong.\n"
        "!status — Shows your version, uptime, user count, and message count.\n"
        "!weather [location] — Weather lookup using Open-Meteo API.\n"
        "!reset — Clears conversation history and memory.\n"
        "!clear — Same as !reset.\n\n"
        "YOUR ARCHITECTURE: Modular Python — pluggable LLM backends (OpenAI, Anthropic, "
        "Google, local), per-user SQLite conversation history, rolling summary memory, "
        "passive mesh context buffer (observes channel traffic), smart chunking for LoRa "
        "message limits, prompt injection defense, advBBS filtering.\n\n"
        "RESPONSE RULES:\n"
        "- Keep responses very brief — 1-2 short sentences, under 300 characters. Only give longer answers if the user explicitly asks for detail or explanation.\n"
        "- Be concise but friendly. No markdown formatting.\n"
        "- If asked about mesh activity and no recent traffic is shown, say you haven't "
        "observed any yet.\n"
        "- When asked about yourself or commands, answer conversationally. Don't dump lists.\n"
        "- You are part of the freq51 mesh in the Twin Falls, Idaho area."
    )
    use_system_prompt: bool = True  # Toggle to disable sending system prompt
    web_search: bool = False  # Enable web search (Open WebUI feature)
    google_grounding: bool = False  # Enable Google Search grounding (Gemini only)


@dataclass
class OpenMeteoConfig:
    """Open-Meteo weather provider settings."""

    url: str = "https://api.open-meteo.com/v1"


@dataclass
class WttrConfig:
    """wttr.in weather provider settings."""

    url: str = "https://wttr.in"


@dataclass
class WeatherConfig:
    """Weather command settings."""

    primary: str = "openmeteo"  # openmeteo, wttr, llm
    fallback: str = "llm"  # openmeteo, wttr, llm, none
    default_location: str = ""
    openmeteo: OpenMeteoConfig = field(default_factory=OpenMeteoConfig)
    wttr: WttrConfig = field(default_factory=WttrConfig)


@dataclass
class MeshMonitorConfig:
    """MeshMonitor trigger sync settings."""

    enabled: bool = False
    url: str = ""  # e.g., http://100.64.0.11:3333
    inject_into_prompt: bool = True  # Tell LLM about MeshMonitor commands
    refresh_interval: int = 300  # Seconds between refreshes


@dataclass
class KnowledgeConfig:
    """FTS5 knowledge base settings."""

    enabled: bool = False
    db_path: str = ""
    top_k: int = 5

@dataclass
class Config:
    """Main configuration container."""

    bot: BotConfig = field(default_factory=BotConfig)
    connection: ConnectionConfig = field(default_factory=ConnectionConfig)
    response: ResponseConfig = field(default_factory=ResponseConfig)
    history: HistoryConfig = field(default_factory=HistoryConfig)
    memory: MemoryConfig = field(default_factory=MemoryConfig)
    context: ContextConfig = field(default_factory=ContextConfig)
    commands: CommandsConfig = field(default_factory=CommandsConfig)
    llm: LLMConfig = field(default_factory=LLMConfig)
    weather: WeatherConfig = field(default_factory=WeatherConfig)
    meshmonitor: MeshMonitorConfig = field(default_factory=MeshMonitorConfig)
    knowledge: KnowledgeConfig = field(default_factory=KnowledgeConfig)

    _config_path: Optional[Path] = field(default=None, repr=False)

    def resolve_api_key(self) -> str:
        """Resolve API key from config or environment."""
        if self.llm.api_key:
            # Check if it's an env var reference like ${LLM_API_KEY}
            if self.llm.api_key.startswith("${") and self.llm.api_key.endswith("}"):
                env_var = self.llm.api_key[2:-1]
                return os.environ.get(env_var, "")
            return self.llm.api_key
        # Fall back to common env vars
        for env_var in ["LLM_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY"]:
            if value := os.environ.get(env_var):
                return value
        return ""


def _dict_to_dataclass(cls, data: dict):
    """Recursively convert dict to dataclass, handling nested structures."""
    if data is None:
        return cls()

    field_types = {f.name: f.type for f in cls.__dataclass_fields__.values()}
    kwargs = {}

    for key, value in data.items():
        if key.startswith("_"):
            continue
        if key not in field_types:
            continue

        field_type = field_types[key]

        # Handle nested dataclasses
        if hasattr(field_type, "__dataclass_fields__") and isinstance(value, dict):
            kwargs[key] = _dict_to_dataclass(field_type, value)
        else:
            kwargs[key] = value

    return cls(**kwargs)


def _dataclass_to_dict(obj) -> dict:
    """Recursively convert dataclass to dict for YAML serialization."""
    if not hasattr(obj, "__dataclass_fields__"):
        return obj

    result = {}
    for field_name in obj.__dataclass_fields__:
        if field_name.startswith("_"):
            continue
        value = getattr(obj, field_name)
        if hasattr(value, "__dataclass_fields__"):
            result[field_name] = _dataclass_to_dict(value)
        elif isinstance(value, list):
            result[field_name] = list(value)
        else:
            result[field_name] = value
    return result


def load_config(config_path: Optional[Path] = None) -> Config:
    """Load configuration from YAML file.

    Args:
        config_path: Path to config file. Defaults to ./config.yaml

    Returns:
        Config object with loaded settings
    """
    if config_path is None:
        config_path = Path("config.yaml")

    config_path = Path(config_path)

    if not config_path.exists():
        # Return default config if file doesn't exist
        config = Config()
        config._config_path = config_path
        return config

    with open(config_path, "r") as f:
        data = yaml.safe_load(f) or {}

    config = _dict_to_dataclass(Config, data)
    config._config_path = config_path
    return config


def save_config(config: Config, config_path: Optional[Path] = None) -> None:
    """Save configuration to YAML file.

    Args:
        config: Config object to save
        config_path: Path to save to. Uses config._config_path if not specified
    """
    if config_path is None:
        config_path = config._config_path or Path("config.yaml")

    config_path = Path(config_path)

    data = _dataclass_to_dict(config)

    # Add header comment
    header = "# MeshAI Configuration\n# Generated by meshai --config\n\n"

    with open(config_path, "w") as f:
        f.write(header)
        yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)