from pydantic import BaseModel, Field
from typing import List, Optional, Literal
from app.core.config import settings


class SettingsUpdate(BaseModel):
    """Schema for updating application settings."""
    
    # RAG Settings
    chunk_size: Optional[int] = Field(default=settings.chunk_size, ge=100, le=5000, description="Text chunk size")
    chunk_overlap: Optional[int] = Field(default=settings.chunk_overlap, ge=0, le=1000, description="Chunk overlap size")
    similarity_top_k: Optional[int] = Field(default=settings.similarity_top_k, ge=1, le=20, description="Number of similar docs to retrieve")
    similarity_threshold: Optional[float] = Field(default=settings.similarity_threshold, ge=0, le=1, description="Similarity threshold")
    
    # Model Settings
    llm_provider: Optional[Literal["gemini", "local"]] = Field(default=settings.llm_provider, description="LLM provider to use")
    enable_fallback: Optional[bool] = Field(default=settings.enable_fallback, description="Enable fallback to alternate model")
    gemini_model_name: Optional[str] = Field(default=settings.gemini_model_name, description="Gemini model name")
    local_model_name: Optional[str] = Field(default=settings.local_model_name, description="Local model filename")
    
    # API Settings
    cors_origins: Optional[List[str]] = Field(default=settings.cors_origins, description="Allowed CORS origins")


class SettingsResponse(BaseModel):
    """Schema for settings response."""
    
    # Paths (read-only)
    root_path: str
    model_path: str
    data_path: str
    
    # API Settings
    api_title: str
    api_version: str
    cors_origins: List[str]
    
    # RAG Settings
    chunk_size: int
    chunk_overlap: int
    similarity_top_k: int
    similarity_threshold: float
    collection_name: str
    persist_directory: str
    
    # Model Settings
    llm_provider: str
    enable_fallback: bool
    embedding_model_name: str
    gemini_model_name: str
    local_model_name: str
    
    class Config:
        from_attributes = True