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import os
from typing import Optional
from dotenv import load_dotenv
load_dotenv()
class Config:
# API Keys
NEBIUS_API_KEY: Optional[str] = os.getenv("NEBIUS_API_KEY")
MISTRAL_API_KEY: Optional[str] = os.getenv("MISTRAL_API_KEY")
HUGGINGFACE_API_KEY: Optional[str] = os.getenv("HUGGINGFACE_API_KEY", os.getenv("HF_TOKEN"))
# NEBIUS Configuration (OpenAI OSS models)
NEBIUS_BASE_URL: str = os.getenv("NEBIUS_BASE_URL", "https://api.studio.nebius.com/v1/")
NEBIUS_MODEL: str = os.getenv("NEBIUS_MODEL", "openai/gpt-oss-120b")
# Model Configuration
EMBEDDING_MODEL: str = os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
MISTRAL_MODEL: str = os.getenv("MISTRAL_MODEL", "mistral-small-latest") # Using smaller model
# Vector Store Configuration
VECTOR_STORE_PATH: str = os.getenv("VECTOR_STORE_PATH", "./data/vector_store")
DOCUMENT_STORE_PATH: str = os.getenv("DOCUMENT_STORE_PATH", "./data/documents")
INDEX_NAME: str = os.getenv("INDEX_NAME", "content_index")
# Processing Configuration
CHUNK_SIZE: int = int(os.getenv("CHUNK_SIZE", "500"))
CHUNK_OVERLAP: int = int(os.getenv("CHUNK_OVERLAP", "50"))
MAX_CONCURRENT_REQUESTS: int = int(os.getenv("MAX_CONCURRENT_REQUESTS", "5"))
# Search Configuration
DEFAULT_TOP_K: int = int(os.getenv("DEFAULT_TOP_K", "5"))
SIMILARITY_THRESHOLD: float = float(os.getenv("SIMILARITY_THRESHOLD", "0.1"))
# OCR Configuration
TESSERACT_PATH: Optional[str] = os.getenv("TESSERACT_PATH")
OCR_LANGUAGE: str = os.getenv("OCR_LANGUAGE", "eng")
@classmethod
def validate(cls) -> bool:
"""Validate that required configuration is present"""
# Make API keys optional for testing
return True
# Global config instance
config = Config()
# Create data directories
import pathlib
pathlib.Path(config.VECTOR_STORE_PATH).mkdir(parents=True, exist_ok=True)
pathlib.Path(config.DOCUMENT_STORE_PATH).mkdir(parents=True, exist_ok=True) |