File size: 3,024 Bytes
a33458e 9f0d171 a33458e 2a735cc a33458e f8ed285 a33458e 9f0d171 a33458e 9f0d171 a33458e 2a735cc a33458e 2a735cc a33458e f8ed285 a33458e 9f0d171 2a735cc a33458e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import os
import random
from dotenv import load_dotenv
from pathlib import Path
# Load environment variables
env_path = Path('.') / '.env'
load_dotenv(dotenv_path=env_path)
# API Keys
HF_API_KEY = os.getenv('HF_API_KEY', '')
TELEGRAM_BOT_TOKEN = os.getenv('TELEGRAM_BOT_TOKEN', '')
# LLM Configuration
# Use models that are freely accessible and don't require authentication
LLM_MODEL = os.getenv('LLM_MODEL', 'gpt2') # Changed from distilgpt2 to gpt2
EMBEDDING_MODEL = os.getenv('EMBEDDING_MODEL', 'sentence-transformers/all-MiniLM-L6-v2')
# Vector Database
# Determine which vector DB path to use based on deployment environment
if os.path.exists("/app/data/vector_db_1"):
# We're in the Docker container, use one of the multiple DB paths
vector_db_options = [
'./data/vector_db_1',
'./data/vector_db_2',
'./data/vector_db_3',
]
# Choose a random DB path to reduce collision probability
VECTOR_DB_PATH = os.getenv('VECTOR_DB_PATH', random.choice(vector_db_options))
else:
# Local development, use the standard path
VECTOR_DB_PATH = os.getenv('VECTOR_DB_PATH', './data/vector_db')
COLLECTION_NAME = os.getenv('COLLECTION_NAME', 'personal_assistant')
# Application Settings
DEFAULT_TEMPERATURE = float(os.getenv('DEFAULT_TEMPERATURE', 0.7))
CHUNK_SIZE = int(os.getenv('CHUNK_SIZE', 512))
CHUNK_OVERLAP = int(os.getenv('CHUNK_OVERLAP', 128))
MAX_TOKENS = int(os.getenv('MAX_TOKENS', 256))
# Telegram Bot Settings
TELEGRAM_ENABLED = os.getenv('TELEGRAM_ENABLED', 'false').lower() == 'true'
TELEGRAM_ALLOWED_USERS = os.getenv('TELEGRAM_ALLOWED_USERS', '')
if TELEGRAM_ALLOWED_USERS:
TELEGRAM_ALLOWED_USERS = [int(user_id.strip()) for user_id in TELEGRAM_ALLOWED_USERS.split(',')]
else:
TELEGRAM_ALLOWED_USERS = []
# Hugging Face Dataset Settings for Chat History
HF_DATASET_NAME = os.getenv('HF_DATASET_NAME', '') # Format: username/repo-name
CHAT_HISTORY_DIR = os.getenv('CHAT_HISTORY_DIR', './data/chat_history')
# How often to sync chat history to HF Hub (in minutes)
SYNC_INTERVAL = int(os.getenv('SYNC_INTERVAL', 60))
# Create a template .env file if it doesn't exist
def create_env_example():
if not os.path.exists('.env.example'):
with open('.env.example', 'w') as f:
f.write("""# API Keys
HF_API_KEY=your_huggingface_api_key_here
TELEGRAM_BOT_TOKEN=your_telegram_bot_token_here
# LLM Configuration
LLM_MODEL=gpt2 # Use small model for Hugging Face Spaces
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
# Vector Database
VECTOR_DB_PATH=./data/vector_db
COLLECTION_NAME=personal_assistant
# Application Settings
DEFAULT_TEMPERATURE=0.7
CHUNK_SIZE=512
CHUNK_OVERLAP=128
MAX_TOKENS=256
# Telegram Bot Settings
TELEGRAM_ENABLED=false
TELEGRAM_ALLOWED_USERS= # Comma-separated list of Telegram user IDs
# Hugging Face Dataset Settings
HF_DATASET_NAME=username/second-brain-history # Your username/dataset-name
CHAT_HISTORY_DIR=./data/chat_history
SYNC_INTERVAL=60 # How often to sync history to HF (minutes)
""") |