"""Centralized configuration via Pydantic Settings.

All env vars are loaded from .env once at import time and validated.
Import the singleton `settings` everywhere — never read os.environ directly.
"""

from __future__ import annotations

from enum import Enum
from functools import lru_cache
from pathlib import Path

from pydantic import Field, HttpUrl, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict


class LlmRole(str, Enum):
    """Logical role a caller asks for. Routing decides which model serves it."""

    REASONING = "reasoning"  # critical: extraction, NLI, verdict, wiki
    FAST = "fast"  # mass processing (currently disabled)
    VISION = "vision"  # multimodal (currently disabled)


class Settings(BaseSettings):
    """Top-level config. Validated at startup, immutable thereafter."""

    model_config = SettingsConfigDict(
        env_file=Path(__file__).parent.parent / ".env",
        env_file_encoding="utf-8",
        extra="ignore",
    )

    # ---- LLM router ---------------------------------------------------------
    llm_router_url: str = Field(default="http://localhost:14011")
    llm_router_api_key: str = Field(default="")
    llm_vllm_url: str = Field(default="http://localhost:14001")
    llm_llamacpp_urls: str = Field(default="")  # comma-separated

    # ---- Models -------------------------------------------------------------
    model_reasoning: str = Field(default="Qwen3.5-397B-A17B")
    model_reasoning_backend: str = Field(default="llamacpp")

    model_fast: str = Field(default="qwen3.5")
    model_fast_backend: str = Field(default="vllm")
    model_fast_enabled: bool = Field(default=False)

    model_vision: str = Field(default="gemma-3-27b-it")
    model_vision_url: str = Field(default="")
    model_vision_enabled: bool = Field(default=False)

    # ---- Embeddings ---------------------------------------------------------
    embedding_url: str = Field(default="http://10.11.10.15:8200")
    embedding_api_key: str = Field(default="")
    embedding_model: str = Field(default="BAAI/bge-m3")
    embedding_dim: int = Field(default=1024)
    embedding_max_tokens: int = Field(default=8192)

    # ---- Reranker -----------------------------------------------------------
    reranker_url: str = Field(default="http://10.11.10.15:8100")
    reranker_api_key: str = Field(default="")
    reranker_model: str = Field(default="BAAI/bge-reranker-v2-m3")

    # ---- Atomic -------------------------------------------------------------
    atomic_url: str = Field(default="http://localhost:8080")
    atomic_token: str = Field(default="")

    # ---- Postgres -----------------------------------------------------------
    postgres_user: str = Field(default="atomic")
    postgres_password: str = Field(default="atomic_dev_changeme")
    postgres_db: str = Field(default="atomic")
    postgres_port: int = Field(default=5434)
    postgres_host: str = Field(
        default="postgres",
        description="Hostname for direct PG connection (Docker: 'postgres', host: 'localhost')",
    )
    postgres_internal_port: int = Field(
        default=5432,
        description="Port inside the Docker network (external is postgres_port)",
    )

    # ---- Verification cache --------------------------------------------------
    verification_cache_ttl_days: int = Field(
        default=30,
        description="How long cached verification entries live before auto-expiry",
    )
    verification_cache_max_payload_kb: int = Field(
        default=64,
        description="Reject POST /v1/verification_cache with payloads above this cap",
    )

    # ---- Analysis atom tier policy ------------------------------------------
    # These are read live from the AI platform dashboard via RuntimeConfigClient
    # (keys: brain.atom.silver_ttl_days, brain.atom.bronze_ttl_days,
    # brain.atom.confidence_silver_threshold). The values below are fallbacks
    # used at startup until the first dashboard poll completes (~30s).
    atom_silver_ttl_days: int = Field(
        default=90,
        description="TTL for LLM-cached analysis atoms (silver tier)",
    )
    atom_bronze_ttl_days: int = Field(
        default=30,
        description="TTL for low-confidence atoms (never served, kept for audit)",
    )
    atom_confidence_silver_threshold: float = Field(
        default=60.0,
        description="LLM confidence ≥ this stores atom as silver, else bronze",
    )

    # ---- Logging ------------------------------------------------------------
    log_level: str = Field(default="INFO")

    # ---- Runtime config (live polling from AI platform dashboard) -----------
    dashboard_url: str | None = Field(
        default=None,
        description=(
            "Optional dashboard base URL (e.g. http://didiAI-dashboard:51300). "
            "When set, RuntimeConfigClient polls /api/config every 30s for live "
            "overrides on atom_* and log_level."
        ),
    )

    # ---- Computed -----------------------------------------------------------
    @property
    def postgres_dsn(self) -> str:
        """Async-compatible DSN for direct asyncpg connections."""
        return (
            f"postgresql://{self.postgres_user}:{self.postgres_password}"
            f"@{self.postgres_host}:{self.postgres_internal_port}/{self.postgres_db}"
        )

    @property
    def llamacpp_urls_list(self) -> list[str]:
        return [u.strip() for u in self.llm_llamacpp_urls.split(",") if u.strip()]

    def model_for(self, role: LlmRole) -> tuple[str, str] | None:
        """Return (model_id, backend_hint) for a logical role, or None if disabled."""
        if role == LlmRole.REASONING:
            return (self.model_reasoning, self.model_reasoning_backend)
        if role == LlmRole.FAST and self.model_fast_enabled:
            return (self.model_fast, self.model_fast_backend)
        if role == LlmRole.VISION and self.model_vision_enabled:
            return (self.model_vision, "external")
        return None

    @field_validator("log_level")
    @classmethod
    def _validate_log_level(cls, v: str) -> str:
        v = v.upper()
        if v not in {"DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"}:
            raise ValueError(f"invalid log_level: {v}")
        return v


@lru_cache(maxsize=1)
def get_settings() -> Settings:
    """Singleton accessor. Cached so .env is parsed only once per process."""
    return Settings()


# Convenience: most code can `from shared.config import settings`
settings = get_settings()