"""Application configuration loaded from .env via pydantic-settings."""

from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    """All environment variables with defaults and validation."""

    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")

    # OpenAI
    openai_api_key: str
    embedding_model: str = "text-embedding-3-large"
    llm_model: str = "gpt-4o-mini"

    # Local embeddings (Ollama) — Admin switches models; host stays in config
    ollama_base_url: str = "http://192.168.10.10:11435"

    # Qdrant
    qdrant_url: str = "http://localhost:6333"
    qdrant_api_key: str | None = None

    # Retrieval
    top_k: int = 5
    # Neighbor Expansion for fixed_size (ADR-0023); 0/0 = off
    neighbor_prev: int = 0
    neighbor_next: int = 0

    # LLM generation
    temperature: float = 0.0
    max_tokens: int = 1024

    # Chunking defaults
    chunk_size: int = 512
    chunk_overlap: int = 50

    # Semantic chunking
    semantic_threshold: float = 0.3
    semantic_min_chunk_size: int = 3

    # Database
    database_url: str = "sqlite:///./data/chunking_benchmark.db"

    # Text PDF gate (reject Scanned PDFs with near-empty text layer)
    pdf_min_total_chars: int = 100
    pdf_min_median_chars_per_page: int = 40


settings = Settings()