# =============================================================================
# Mnemosyne Django Environment Variables
# =============================================================================
# Copy this file to .env and configure for your environment
# This file contains all variables read by Django settings.py

# --- Security ---
SECRET_KEY=change-me-to-a-real-secret-key
DEBUG=True
ALLOWED_HOSTS=localhost,127.0.0.1,mnemosyne.ouranos.helu.ca
CSRF_TRUSTED_ORIGINS=http://localhost:8000,https://mnemosyne.ouranos.helu.ca

# --- PostgreSQL Database ---
APP_DB_NAME=mnemosyne
APP_DB_USER=mnemosyne
APP_DB_PASSWORD=password
DB_HOST=portia.incus
DB_PORT=5432

# --- Neo4j Graph Database ---
NEOMODEL_NEO4J_BOLT_URL=bolt://neo4j:password@ariel.incus:25554

# --- Memcached ---
KVDB_LOCATION=127.0.0.1:11211
KVDB_PREFIX=mnemosyne

# --- Celery / RabbitMQ ---
CELERY_BROKER_URL=amqp://mnemosyne:password@oberon.incus:5672/mnemosyne
CELERY_RESULT_BACKEND=rpc://
CELERY_TASK_ALWAYS_EAGER=False

# --- S3 Storage (Incus bucket, MinIO-backed) ---
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_STORAGE_BUCKET_NAME=mnemosyne-content
AWS_S3_ENDPOINT_URL=
AWS_S3_USE_SSL=False
AWS_S3_VERIFY=False
AWS_S3_REGION_NAME=us-east-1
# Set to True to use local FileSystemStorage instead of S3 (dev/test)
USE_LOCAL_STORAGE=True

# --- Daedalus S3 (cross-bucket reads for ingest) ---
# Mnemosyne ingests files from the Daedalus S3 bucket. These vars
# configure read access; the file is copied into AWS_STORAGE_BUCKET_NAME
# (Mnemosyne's own bucket) by the ingest Celery task.
DAEDALUS_S3_ENDPOINT_URL=
DAEDALUS_S3_ACCESS_KEY_ID=
DAEDALUS_S3_SECRET_ACCESS_KEY=
DAEDALUS_S3_BUCKET_NAME=daedalus
DAEDALUS_S3_REGION_NAME=us-east-1
DAEDALUS_S3_USE_SSL=False
DAEDALUS_S3_VERIFY=True

# --- MCP Server ---
# Set to False for internal-only deployments where the MCP transport is
# already on a trusted network (10.10.0.0/24).
MCP_REQUIRE_AUTH=True

# --- Email (smtp4dev on Oberon) ---
EMAIL_HOST=oberon.incus
EMAIL_PORT=22025
EMAIL_USE_TLS=False

# --- LLM API Encryption ---
# Encryption key for LLM API keys stored in the database
# Generate with: python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
LLM_API_SECRETS_ENCRYPTION_KEY=

# --- Embedding Pipeline (Phase 2) ---
# Batch size for embedding API calls (smaller for local GPU, larger for cloud)
EMBEDDING_BATCH_SIZE=8
# Timeout in seconds for embedding API requests
EMBEDDING_TIMEOUT=120

# --- Logging ---
# Valid levels: DEBUG, INFO, WARNING, ERROR, CRITICAL
LOGGING_LEVEL=INFO
CELERY_LOGGING_LEVEL=INFO
DJANGO_LOGGING_LEVEL=WARNING

# --- Localization ---
TIME_ZONE=UTC
LANGUAGE_CODE=en-us