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"""
Environment setup script for the RAG system
"""
import os
import sys
import subprocess
import logging
from pathlib import Path
import json
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class EnvironmentSetup:
"""Setup environment for the RAG system"""
def __init__(self):
self.project_root = Path(__file__).parent.parent
self.requirements_file = self.project_root / "requirements.txt"
self.env_file = self.project_root / ".env"
self.token_file = self.project_root / "llama_token.txt"
def setup_directories(self):
"""Create necessary directories"""
directories = [
self.project_root / "data" / "knowledge_base" / "vector_db",
self.project_root / "data" / "knowledge_base" / "rag_exports",
self.project_root / "logs",
self.project_root / "cache",
self.project_root / "models"
]
for directory in directories:
directory.mkdir(parents=True, exist_ok=True)
logger.info(f"Created directory: {directory}")
def check_hf_token(self):
"""Check if Hugging Face token is available"""
if self.token_file.exists():
with open(self.token_file, 'r') as f:
token = f.read().strip()
if token and token.startswith('hf_'):
logger.info("✅ Hugging Face token found")
return True
else:
logger.warning("⚠️ Invalid Hugging Face token format")
return False
else:
logger.warning("⚠️ No llama_token.txt found")
return False
def create_env_file(self):
"""Create .env file with default settings"""
if not self.env_file.exists():
env_content = """# RAG System Environment Variables
# GPU Configuration
CUDA_VISIBLE_DEVICES=0
PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
# LLM Configuration
USE_OLLAMA=true
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL_PRIMARY=llama3.1:70b-instruct-q4_K_M
OLLAMA_MODEL_FAST=llama3.1:8b-instruct-fp16
# Hugging Face Configuration (fallback)
HF_TOKEN=your_huggingface_token_here
HF_MODEL_PRIMARY=meta-llama/Llama-3.1-70B-Instruct
HF_MODEL_FAST=meta-llama/Llama-3.1-8B-Instruct
# API Configuration
API_HOST=0.0.0.0
API_PORT=8000
API_WORKERS=1
# Model Configuration
EMBEDDING_MODEL_NAME=BAAI/bge-large-en-v1.5
EMBEDDING_BATCH_SIZE=256
MAX_CONTEXT_LENGTH=8192
# Search Configuration
DEFAULT_TOP_K=10
SEMANTIC_WEIGHT=0.7
# Logging
RAG_LOGGING_LEVEL=INFO
"""
with open(self.env_file, 'w') as f:
f.write(env_content)
logger.info(f"Created .env file: {self.env_file}")
else:
logger.info(f".env file already exists: {self.env_file}")
def check_python_version(self):
"""Check Python version"""
version = sys.version_info
if version.major < 3 or (version.major == 3 and version.minor < 10):
logger.error("Python 3.10 or higher is required")
return False
logger.info(f"Python version: {version.major}.{version.minor}.{version.micro}")
return True
def check_gpu(self):
"""Check GPU availability"""
try:
import torch
if torch.cuda.is_available():
gpu_name = torch.cuda.get_device_name(0)
gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
logger.info(f"GPU detected: {gpu_name} ({gpu_memory:.1f} GB)")
return True
else:
logger.warning("No GPU detected. System will use CPU (slower performance)")
return False
except ImportError:
logger.warning("PyTorch not installed. Cannot check GPU.")
return False
def install_dependencies(self):
"""Install Python dependencies"""
if not self.requirements_file.exists():
logger.error(f"Requirements file not found: {self.requirements_file}")
return False
try:
logger.info("Installing Python dependencies...")
subprocess.run([
sys.executable, "-m", "pip", "install", "-r", str(self.requirements_file)
], check=True)
logger.info("Dependencies installed successfully")
return True
except subprocess.CalledProcessError as e:
logger.error(f"Failed to install dependencies: {e}")
return False
def check_ollama(self):
"""Check if Ollama is installed and running"""
try:
result = subprocess.run(["ollama", "--version"], capture_output=True, text=True)
if result.returncode == 0:
logger.info(f"Ollama found: {result.stdout.strip()}")
return True
else:
logger.warning("Ollama not found or not working")
return False
except FileNotFoundError:
logger.warning("Ollama not installed. Please install Ollama from https://ollama.ai")
return False
def download_ollama_models(self):
"""Download required Ollama models"""
models = [
"llama3.1:8b-instruct-fp16"
# Removed 70B model to avoid disk space issues
# "llama3.1:70b-instruct-q4_K_M"
]
for model in models:
try:
logger.info(f"Downloading Ollama model: {model}")
subprocess.run(["ollama", "pull", model], check=True)
logger.info(f"Successfully downloaded: {model}")
except subprocess.CalledProcessError as e:
logger.error(f"Failed to download {model}: {e}")
return False
return True
def test_hf_models(self):
"""Test Hugging Face model access"""
if not self.check_hf_token():
return False
try:
import requests
token = self.token_file.read_text().strip()
headers = {"Authorization": f"Bearer {token}"}
# Test access to Llama models
test_url = "https://huggingface.co/api/models/meta-llama/Llama-3.1-8B-Instruct"
response = requests.get(test_url, headers=headers, timeout=10)
if response.status_code == 200:
logger.info("✅ Hugging Face models accessible")
return True
else:
logger.warning(f"⚠️ Hugging Face access failed: {response.status_code}")
return False
except Exception as e:
logger.warning(f"⚠️ Could not test Hugging Face access: {e}")
return False
def create_requirements_file(self):
"""Create requirements.txt if it doesn't exist"""
if not self.requirements_file.exists():
requirements = """# Core dependencies
fastapi==0.104.1
uvicorn[standard]==0.24.0
pydantic==2.5.0
pydantic-settings==2.1.0
# Vector database
chromadb==0.4.18
# Embeddings
sentence-transformers==2.2.2
torch==2.1.1
torchvision==0.16.1
torchaudio==2.1.1
# HTTP client
aiohttp==3.9.1
requests==2.31.0
# Data processing
numpy==1.24.3
pandas==2.1.4
tqdm==4.66.1
# UI
gradio==4.7.1
# Utilities
python-dotenv==1.0.0
psutil==5.9.6
"""
with open(self.requirements_file, 'w') as f:
f.write(requirements)
logger.info(f"Created requirements.txt: {self.requirements_file}")
def run_health_check(self):
"""Run health check on the system"""
logger.info("Running health check...")
checks = {
"Python Version": self.check_python_version(),
"GPU Available": self.check_gpu(),
"Hugging Face Token": self.check_hf_token(),
"Hugging Face Models": self.test_hf_models(),
"Ollama Installed": self.check_ollama(),
"Directories Created": True, # Will be set after setup
"Requirements File": self.requirements_file.exists()
}
logger.info("Health Check Results:")
for check, status in checks.items():
status_str = "✅ PASS" if status else "❌ FAIL"
logger.info(f" {check}: {status_str}")
return all(checks.values())
def run(self):
"""Run complete setup"""
logger.info("🚀 Starting RAG System Environment Setup")
logger.info("=" * 50)
# Create requirements file
self.create_requirements_file()
# Setup directories
self.setup_directories()
# Create .env file
self.create_env_file()
# Install dependencies
if not self.install_dependencies():
logger.error("Failed to install dependencies")
return False
# Check Hugging Face token
hf_available = self.check_hf_token() and self.test_hf_models()
# Check Ollama
ollama_available = self.check_ollama()
if ollama_available:
# Download models
if not self.download_ollama_models():
logger.warning("Failed to download some Ollama models")
# Run health check
health_ok = self.run_health_check()
logger.info("=" * 50)
if health_ok:
logger.info("✅ Environment setup completed successfully!")
logger.info("Next steps:")
if ollama_available:
logger.info("1. Start Ollama: ollama serve")
elif hf_available:
logger.info("1. Using Hugging Face models (no local setup needed)")
else:
logger.info("1. Install Ollama or ensure Hugging Face token is valid")
logger.info("2. Build KG: python src/constructors/kg_builder_without_neo4j")
logger.info("3. Export data: python src/generators/export_kg_for_rag_without_neo4j")
logger.info("4. Build vector DB: python -m src.generators.rag_system --build")
logger.info("5. Start API: python -m uvicorn src.api.main:app --host 0.0.0.0 --port 8000")
logger.info("6. Start UI: python src/ui/gradio_app.py")
else:
logger.error("❌ Environment setup completed with issues")
logger.error("Please check the logs above and resolve any issues")
return health_ok
def main():
"""Main function"""
setup = EnvironmentSetup()
success = setup.run()
sys.exit(0 if success else 1)
if __name__ == "__main__":
main() |