"""Application configuration via environment variables."""

from __future__ import annotations

from pydantic_settings import BaseSettings
from pydantic import Field


class Settings(BaseSettings):
    # --- Service ---
    service_name: str = "agentic-microservice"
    service_version: str = "1.0.0"
    host: str = "0.0.0.0"
    port: int = 8000
    debug: bool = False

    # --- Redis ---
    redis_host: str = "localhost"
    redis_port: int = 6379
    redis_db: int = 0
    redis_password: str = ""
    redis_key_prefix: str = "agentic"
    session_ttl_seconds: int = 86400  # 24h

    @property
    def redis_url(self) -> str:
        auth = f":{self.redis_password}@" if self.redis_password else ""
        return f"redis://{auth}{self.redis_host}:{self.redis_port}/{self.redis_db}"

    # --- Model providers ---
    anthropic_api_key: str = ""
    anthropic_base_url: str = ""  # Custom base URL (for MiniMax Anthropic-compatible, etc.)
    openai_api_key: str = ""
    openai_base_url: str = ""  # Custom base URL (for MiniMax, DeepInfra, etc.)
    default_model_provider: str = "claude"
    default_model_id: str = "claude-sonnet-4-20250514"
    max_tokens: int = 4096
    temperature: float = 0.3

    # --- Context engine ---
    model_context_window: int = 0  # 0 = use legacy fixed budget / explicit override
    model_max_output_tokens: int = 4096
    context_max_tokens: int = 0  # 0 = auto-budget from model window, fallback legacy 120k
    compaction_threshold_tokens: int = 0  # 0 = derive from ratio
    compaction_threshold_ratio: float = 0.80
    context_reserve_ratio: float = 0.10
    artifact_summary_max_chars: int = 2000
    knowledge_base_max_tokens: int = 30_000
    working_context_max_items: int = 20
    tool_raw_output_max_chars: int = 2000
    conversation_recent_raw_limit: int = 2
    task_history_max_entries: int = 20
    task_history_max_tokens: int = 1500

    # --- MCP ---
    mcp_config_path: str = ""  # Path to mcp.json; empty = legacy single-server mode
    mcp_server_command: str = ""  # Legacy: single server command
    mcp_server_args: list[str] = Field(default_factory=list)
    mcp_timeout_seconds: float = 30.0
    mcp_startup_timeout_seconds: float = 10.0

    # --- Pricing (per 1M tokens) ---
    cost_per_1m_input: float = 2.50
    cost_per_1m_output: float = 15.00

    # --- Orchestrator ---
    max_execution_steps: int = 25
    subagent_max_steps: int = 30
    max_execution_timeout_seconds: float = 300.0  # 5 min global timeout

    # --- SSE ---
    sse_keepalive_seconds: float = 15.0

    model_config = {"env_prefix": "AGENTIC_", "env_file": ".env", "extra": "ignore"}

    @property
    def reserve_tokens(self) -> int:
        if self.model_context_window <= 0:
            return 0
        return max(0, int(self.model_context_window * self.context_reserve_ratio))

    @property
    def effective_context_budget(self) -> int:
        if self.context_max_tokens > 0:
            return self.context_max_tokens

        if self.model_context_window > 0:
            budget = (
                self.model_context_window
                - max(0, self.model_max_output_tokens)
                - self.reserve_tokens
            )
            return max(1, budget)

        return 120_000

    @property
    def effective_compaction_threshold(self) -> int:
        if self.compaction_threshold_tokens > 0:
            return min(self.compaction_threshold_tokens, self.effective_context_budget)
        return max(1, int(self.effective_context_budget * self.compaction_threshold_ratio))


settings = Settings()