"""Konfiguration des PV RAG Agent (alle Werte per Umgebungsvariable übersteuerbar)."""
from __future__ import annotations

import os
from dataclasses import dataclass


def _env_str(name: str, default: str) -> str:
    v = os.environ.get(name)
    return v if v not in (None, "") else default


def _env_int(name: str, default: int) -> int:
    try:
        return int(os.environ.get(name, default))
    except (TypeError, ValueError):
        return default


def _env_float(name: str, default: float) -> float:
    try:
        return float(os.environ.get(name, default))
    except (TypeError, ValueError):
        return default


def _env_bool(name: str, default: bool) -> bool:
    v = os.environ.get(name)
    if v is None:
        return default
    return v.strip().lower() in ("1", "true", "yes", "on")


@dataclass
class Config:
    # Pfade (relativ zum Repo-Root, sofern nicht absolut)
    kb_dir: str = "wissensbasis"
    db_path: str = "data/index.db"

    # Ollama (Custom-Port 11435 — nicht "korrigieren", s. Skill)
    ollama_url: str = "http://100.183.83.12:11435"
    embed_model: str = "bge-m3"
    answer_model: str = "qwen3.8:27b"  # provisorisch bis Bake-off (M3)

    # Generierung
    temperature: float = 0.1
    num_ctx: int = 16384
    num_predict: int = 1024
    think: bool = False  # Thinking per Request abschalten (Latenz)
    chat_timeout_s: float = 300.0
    embed_timeout_s: float = 240.0

    # Retrieval
    embed_off: bool = False            # True = BM25-only (ohne Dense-Index/-Suche)
    candidate_pool: int = 50           # Kandidaten je Liste vor der Fusion
    context_blocks: int = 8            # Kontext-Blöcke im Prompt
    cross_ref_expand: int = 3          # Top-Einträge, deren cross_refs ergänzt werden
    cross_ref_max_extra: int = 6       # Obergrenze der Ergänzungen
    rrf_k: int = 60
    recency_boost: float = 0.005       # additiv auf RRF-Score, gewichtet nach Stand

    # Service
    port: int = 8080

    @classmethod
    def from_env(cls) -> "Config":
        d = cls()
        return cls(
            kb_dir=_env_str("PV_KB_DIR", d.kb_dir),
            db_path=_env_str("PV_DB_PATH", d.db_path),
            ollama_url=_env_str("OLLAMA_URL", d.ollama_url),
            embed_model=_env_str("PV_EMBED_MODEL", d.embed_model),
            answer_model=_env_str("PV_ANSWER_MODEL", d.answer_model),
            temperature=_env_float("PV_TEMPERATURE", d.temperature),
            num_ctx=_env_int("PV_NUM_CTX", d.num_ctx),
            num_predict=_env_int("PV_NUM_PREDICT", d.num_predict),
            think=_env_bool("PV_THINK", d.think),
            chat_timeout_s=_env_float("PV_CHAT_TIMEOUT_S", d.chat_timeout_s),
            embed_timeout_s=_env_float("PV_EMBED_TIMEOUT_S", d.embed_timeout_s),
            embed_off=_env_bool("PV_EMBED_OFF", d.embed_off),
            candidate_pool=_env_int("PV_CANDIDATE_POOL", d.candidate_pool),
            context_blocks=_env_int("PV_CONTEXT_BLOCKS", d.context_blocks),
            cross_ref_expand=_env_int("PV_CROSS_REF_EXPAND", d.cross_ref_expand),
            cross_ref_max_extra=_env_int("PV_CROSS_REF_MAX_EXTRA", d.cross_ref_max_extra),
            recency_boost=_env_float("PV_RECENCY_BOOST", d.recency_boost),
            port=_env_int("PV_PORT", d.port),
        )