env.example
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# .env.example - Environment variables for Entities API Docker setup
#
# HOW TO USE:
# Option A (recommended): Run the setup script — it auto-generates all secrets
# and walks you through optional values interactively:
#
# entities-api docker-manager --mode up
#
# Option B (manual): Copy this file to .env and replace every __PLACEHOLDER__
# with a real value before starting the stack.
#
# cp .env.example .env
#
# UPDATING OPTIONAL VALUES AFTER SETUP:
# entities-api docker-manager configure --set HF_TOKEN=hf_abc123
# entities-api docker-manager configure --interactive
#
# PLACEHOLDERS KEY:
# __GENERATED__ Auto-generated by the setup script. Never set these manually
# unless you know what you're doing. Delete .env and re-run
# the script to regenerate them safely.
# __USER_SUPPLIED__ Must be provided by you. The stack starts without them but
# related features will be unavailable until set.
# __OPTIONAL__ Leave blank if you are not using this service/feature.
#############################
# Base URLs
#############################
# All public-facing URLs route through nginx on port 80.
ASSISTANTS_BASE_URL=http://localhost:80
SANDBOX_SERVER_URL=http://localhost:80
DOWNLOAD_BASE_URL=http://localhost:80/v1/files/download
HYPERBOLIC_BASE_URL=https://api.hyperbolic.xyz/v1
TOGETHER_BASE_URL=https://api.together.xyz/v1
OLLAMA_BASE_URL=http://ollama:11434
#############################
# AI Model Configuration
#############################
# Your HuggingFace personal access token.
# Required only for downloading gated models via vLLM or the training stack.
# Get one at: https://huggingface.co/settings/tokens
# The stack starts fine without this — press Enter to skip during setup,
# or set it later with:
# entities-api docker-manager configure --set HF_TOKEN=hf_abc123
HF_TOKEN=__USER_SUPPLIED__
# Local path where HuggingFace model weights are cached.
# Auto-resolved to ~/.cache/huggingface by the setup script if left blank.
HF_CACHE_PATH=__USER_SUPPLIED__
# The vLLM model to load on startup (static vllm service, --profile ai).
# Override this to switch models, e.g. Qwen/Qwen2.5-VL-7B-Instruct
VLLM_MODEL=Qwen/Qwen2.5-VL-3B-Instruct
# Extra flags passed to the static vLLM service command.
# Leave blank for defaults. Example: --max-model-len 4096
VLLM_EXTRA_FLAGS=
#############################
# Database Configuration
#############################
# Both DATABASE_URL and SPECIAL_DB_URL are auto-constructed by the setup
# script from the individual DB components below. Listed here for reference.
DATABASE_URL=mysql+pymysql://api_user:__GENERATED__@db:3306/entities_db
SPECIAL_DB_URL=mysql+pymysql://api_user:__GENERATED__@localhost:3307/entities_db
# !! Auto-generated by setup script — do not set a weak password manually !!
MYSQL_ROOT_PASSWORD=__GENERATED__
MYSQL_DATABASE=entities_db
MYSQL_USER=api_user
# !! Auto-generated by setup script — do not set a weak password manually !!
MYSQL_PASSWORD=__GENERATED__
MYSQL_HOST=db
MYSQL_PORT=3306
REDIS_URL=redis://redis:6379/0
#############################
# API Keys & External Services
#############################
# !! Auto-generated by setup script !!
API_KEY=__GENERATED__
# Third-party inference provider keys — leave blank if not using these providers.
TOGETHER_API_KEY=__OPTIONAL__
HYPERBOLIC_API_KEY=__OPTIONAL__
ADMIN_API_KEY=__OPTIONAL__
ENTITIES_API_KEY=__OPTIONAL__
ENTITIES_USER_ID=__OPTIONAL__
DEEP_SEEK_API_KEY=__OPTIONAL__
#############################
# Platform Settings
#############################
BASE_URL_HEALTH=http://localhost:80/v1/health
SHELL_SERVER_URL=ws://sandbox_api:8000/ws/computer
SHELL_SERVER_EXTERNAL_URL=ws://localhost:8000/ws/computer
CODE_EXECUTION_URL=ws://sandbox_api:8000/ws/execute
# !! Auto-generated by setup script !!
SIGNED_URL_SECRET=__GENERATED__
# !! Auto-generated by setup script !!
SANDBOX_AUTH_SECRET=__GENERATED__
DISABLE_FIREJAIL=true
# !! Auto-generated by setup script !!
SECRET_KEY=__GENERATED__
# !! Auto-generated by setup script !!
DEFAULT_SECRET_KEY=__GENERATED__
SHARED_PATH=./shared_data
AUTO_MIGRATE=1
#############################
# Admin Configuration
#############################
ADMIN_USER_EMAIL=admin@example.com
ADMIN_USER_ID=__OPTIONAL__
ADMIN_KEY_PREFIX=__OPTIONAL__
#############################
# SMB / Samba Configuration
#############################
SMBCLIENT_SERVER=samba_server
SMBCLIENT_SHARE=cosmic_share
SMBCLIENT_USERNAME=samba_user
# !! Auto-generated by setup script !!
SMBCLIENT_PASSWORD=__GENERATED__
SMBCLIENT_PORT=445
# Samba process ownership — match your host UID/GID (default 1000)
SAMBA_USERID=1000
SAMBA_GROUPID=1000
#############################
# Training Stack (--profile training)
#############################
# Training profile controls resource allocation inside the worker.
# Options: laptop | standard | high_end
# laptop — conservative VRAM usage, smaller batch sizes
# standard — balanced defaults for mid-range GPUs
# high_end — maximises throughput for 24GB+ VRAM cards
TRAINING_PROFILE=laptop
# Ray cluster configuration.
# RAY_ADDRESS: leave blank to start this node as Ray head.
# Set to ray://<head_ip>:10001 to join an existing cluster as a worker.
# Adding a new GPU node: set RAY_ADDRESS and run docker compose up training-worker.
RAY_ADDRESS=
RAY_DASHBOARD_PORT=8265
# Searxng secret key
SEARXNG_SECRET_KEY=__GENERATED__
#############################
# Tool Identifiers
#############################
# Auto-generated by setup script. Replace only if you need stable IDs
# across reinstalls (e.g. if tool IDs are persisted in a database).
TOOL_CODE_INTERPRETER=tool___GENERATED__
TOOL_WEB_SEARCH=tool___GENERATED__
TOOL_COMPUTER=tool___GENERATED__
#############################
# Other
#############################
LOG_LEVEL=INFO
PYTHONUNBUFFERED=1