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import os
from pathlib import Path
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
import logging
from logging.handlers import RotatingFileHandler
from pydantic_settings import BaseSettings, SettingsConfigDict
# Initialize environment
load_dotenv()
# --- Settings (simple, in-file) ---
class Settings(BaseSettings):
model_config = SettingsConfigDict(env_file='.env', env_file_encoding='utf-8', extra='ignore')
OPENAI_API_KEY: str
OPENAI_BASE_URL: str | None = None
# LlamaParse configuration for advanced PDF parsing
LLAMA_CLOUD_API_KEY: str | None = None
LLAMA_PREMIUM_MODE: bool = False # Set to True for GPT-4o parsing (costs more)
LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
DATA_DIR: str = os.getenv("DATA_DIR", "")
LOG_DIR: str = os.getenv("LOG_DIR", "")
settings = Settings()
# --- File Path Configuration (Cross-platform compatible) ---
PROJECT_ROOT = Path(__file__).parent.parent.absolute()
DATA_DIR = Path(settings.DATA_DIR or (PROJECT_ROOT / "data"))
NEW_DATA = DATA_DIR / "new_data"
PROCESSED_DATA = DATA_DIR / "processed_data"
CHUNKS_PATH = DATA_DIR / "chunks.pkl"
VECTOR_STORE_DIR = DATA_DIR / "vector_store"
DATA_DIR.mkdir(parents=True, exist_ok=True)
NEW_DATA.mkdir(parents=True, exist_ok=True)
PROCESSED_DATA.mkdir(parents=True, exist_ok=True)
VECTOR_STORE_DIR.mkdir(parents=True, exist_ok=True)
# Setup logging
LOG_DIR = Path(settings.LOG_DIR or (Path(__file__).parent.parent / "logs"))
LOG_DIR.mkdir(parents=True, exist_ok=True)
LOG_FILE = LOG_DIR / "app.log"
# Configure application logger (avoid duplicate handlers)
LOG_LEVEL = settings.LOG_LEVEL.upper()
logger = logging.getLogger("AgenticMedicalRAG") # centralized logger
logger.setLevel(LOG_LEVEL)
logger.propagate = False
if not logger.handlers:
formatter = logging.Formatter(
fmt="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
file_handler = RotatingFileHandler(
LOG_FILE,
maxBytes=1000000,
backupCount=3,
encoding="utf-8"
)
file_handler.setFormatter(formatter)
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(stream_handler)
# --- LLM Configuration with lazy loading ---
_llm = None
def get_llm():
"""Get LLM with lazy loading for faster startup"""
global _llm
if _llm is None:
logger.info("Initializing LLM (first time)...")
openai_key = settings.OPENAI_API_KEY
if not openai_key:
logger.error("OPENAI_API_KEY not found in environment variables")
raise ValueError("OpenAI API key is required. Please set OPENAI_API_KEY environment variable.")
try:
_llm = ChatOpenAI(
model="gpt-4o",
api_key=openai_key,
base_url=settings.OPENAI_BASE_URL,
temperature=0.0,
max_tokens=2048,
request_timeout=30, # Increased timeout for stability
max_retries=2,
streaming=True,
)
logger.info("LLM initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize LLM: {e}")
raise
return _llm
def create_llm():
"""Create LLM with proper error handling and fallbacks"""
return get_llm()
# Lazy loading - only initialize when actually needed
LLM = None # Will be loaded on first use
# --- Embedding Model Configuration with lazy loading ---
_embedding_model = None
def get_embedding_model():
"""Get embedding model with lazy loading for faster startup"""
global _embedding_model
if _embedding_model is None:
logger.info("Loading embedding model (first time)...")
try:
_embedding_model = HuggingFaceEmbeddings(
model_name="abhinand/MedEmbed-base-v0.1",
model_kwargs={'device': 'cpu'},
encode_kwargs={'normalize_embeddings': True}
)
logger.info("Embedding model loaded successfully")
except Exception as e:
logger.error(f"Failed to load embedding model: {e}")
raise ValueError("Failed to load embedding model")
return _embedding_model
# For backward compatibility
def create_embedding_model():
"""Create embedding model with proper error handling"""
return get_embedding_model()
# Lazy loading - only load when actually needed
EMBEDDING_MODEL = None # Will be loaded on first use
# Configuration validation
def validate_config():
"""Validate all required configurations"""
required_env_vars = ["OPENAI_API_KEY"]
missing_vars = [var for var in required_env_vars if not getattr(settings, var, None)]
if missing_vars:
raise ValueError(f"Missing required environment variables: {missing_vars}")
logger.info("Configuration validation completed")
# Run validation on import
try:
validate_config()
except Exception as e:
logger.error(f"Configuration validation failed: {e}")
raise e |