"""
Configuration settings for the RAG backend.
"""
from pydantic_settings import BaseSettings
from pathlib import Path
from typing import Optional
import os
class Settings(BaseSettings):
"""Application settings with environment variable support."""
# App settings
APP_NAME: str = "ClientSphere RAG Backend"
DEBUG: bool = True
ENV: str = "dev" # "dev" or "prod" - controls tenant_id security
# Paths
BASE_DIR: Path = Path(__file__).parent.parent
DATA_DIR: Path = BASE_DIR / "data"
UPLOADS_DIR: Path = DATA_DIR / "uploads"
PROCESSED_DIR: Path = DATA_DIR / "processed"
VECTORDB_DIR: Path = DATA_DIR / "vectordb"
# Chunking settings (optimized for retrieval quality)
CHUNK_SIZE: int = 600 # tokens (increased for better context)
CHUNK_OVERLAP: int = 150 # tokens (increased for better continuity)
MIN_CHUNK_SIZE: int = 100 # minimum tokens per chunk (increased to avoid tiny chunks)
# Embedding settings
EMBEDDING_MODEL: str = "all-MiniLM-L6-v2" # Fast, good quality
EMBEDDING_DIMENSION: int = 384
# Vector store settings
COLLECTION_NAME: str = "clientsphere_kb"
# Retrieval settings (optimized for maximum confidence)
TOP_K: int = 10 # Number of chunks to retrieve (increased to maximize chance of finding strong matches)
SIMILARITY_THRESHOLD: float = 0.15 # Minimum similarity score (0-1) - lowered to include more potentially relevant chunks
SIMILARITY_THRESHOLD_STRICT: float = 0.45 # Strict threshold for answer generation (anti-hallucination)
# LLM settings
LLM_PROVIDER: str = "gemini" # Options: "gemini", "openai"
GEMINI_API_KEY: Optional[str] = None
OPENAI_API_KEY: Optional[str] = None
GEMINI_MODEL: str = "gemini-1.5-flash" # Use latest stable model
OPENAI_MODEL: str = "gpt-3.5-turbo"
# Response settings
MAX_CONTEXT_TOKENS: int = 2500 # Max tokens for context in prompt (reduced for focus)
TEMPERATURE: float = 0.0 # Zero temperature for maximum determinism (anti-hallucination)
REQUIRE_VERIFIER: bool = True # Always use verifier for hallucination prevention
# Security settings
MAX_FILE_SIZE_MB: int = 50 # Maximum file size in MB
ALLOWED_ORIGINS: str = "*" # CORS allowed origins (comma-separated, use "*" for all)
RATE_LIMIT_PER_MINUTE: int = 60 # Rate limit per user per minute
JWT_SECRET: Optional[str] = None # JWT secret for authentication
# Rate limiting
RATE_LIMIT_ENABLED: bool = True # Enable/disable rate limiting
class Config:
env_file = ".env"
env_file_encoding = "utf-8"
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Create directories if they don't exist
self.UPLOADS_DIR.mkdir(parents=True, exist_ok=True)
self.PROCESSED_DIR.mkdir(parents=True, exist_ok=True)
self.VECTORDB_DIR.mkdir(parents=True, exist_ok=True)
# Global settings instance
settings = Settings()
