# ODS Configuration
# Copy this file to .env and edit values before starting:
#   cp .env.example .env
#
# The installer (install-core.sh) generates .env automatically with
# secure random secrets. This file documents all available variables.

# ODS version (auto-set by installer, used by ods-cli for compat checks)
# ODS_VERSION=2.6.0

# LiteLLM proxy API key for internal service auth
# TARGET_API_KEY=not-needed

# ═══════════════════════════════════════════════════════════════════
# REQUIRED — these must be set or docker compose will refuse to start
# ═══════════════════════════════════════════════════════════════════

# Session signing for Open WebUI (generate: openssl rand -hex 32)
WEBUI_SECRET=CHANGEME

# n8n workflow automation credentials
N8N_USER=admin@ods.local
N8N_PASS=CHANGEME

# LiteLLM API gateway key (generate: echo "sk-ods-$(openssl rand -hex 16)")
LITELLM_KEY=CHANGEME

# LiveKit real-time communication credentials
LIVEKIT_API_KEY=CHANGEME
LIVEKIT_API_SECRET=CHANGEME

# Dify agent platform secret key
DIFY_SECRET_KEY=CHANGEME

# SearXNG session secret (generate: openssl rand -hex 32)
SEARXNG_SECRET=CHANGEME

# OpenCode web UI password (generate: openssl rand -base64 16)
OPENCODE_SERVER_PASSWORD=CHANGEME

# OpenClaw gateway token (generate: openssl rand -hex 24)
OPENCLAW_TOKEN=CHANGEME

# ═══════════════════════════════════════════════════════════════════
# OpenClaw Security
# ═══════════════════════════════════════════════════════════════════
#
# OpenClaw device authentication (device pairing) defaults to ON. Token auth
# and device auth are separate gates: a valid OPENCLAW_TOKEN still auto-
# connects the local Control UI with device auth enabled, so you normally do
# NOT need to change this.
#
# Setting this to "true" DISABLES device pairing entirely. Anyone who can
# reach the gateway then gets an UNAUTHENTICATED agent with exec / read /
# write tools — especially dangerous with a widened BIND_ADDRESS
# (LAN / Tailscale / 0.0.0.0). Only enable as a deliberate, understood
# escape hatch; a loud warning banner is logged at startup when set.
# Leave unset (the secure default) unless you fully accept the risk.
# OPENCLAW_DANGEROUSLY_DISABLE_DEVICE_AUTH=
# OPENCLAW_LLM_URL=
# OPENCLAW_HTTP_API=

# ═══════════════════════════════════════════════════════════════════
# Network Binding
# ═══════════════════════════════════════════════════════════════════

# Bind address for all Docker port bindings.
# 127.0.0.1 = localhost only (secure default)
# 0.0.0.0   = accessible from LAN (headless servers, see SECURITY.md)
# BIND_ADDRESS=127.0.0.1

# ═══════════════════════════════════════════════════════════════════
# LLM Backend Mode
# ═══════════════════════════════════════════════════════════════════

# local  = llama-server (default, requires GPU or CPU inference)
# cloud  = LiteLLM -> cloud APIs (no local GPU needed)
# hybrid = local primary, cloud fallback
ODS_MODE=local
# Model Switchboard rollout: legacy / observe / enabled.
# enabled routes supported apps through the stable ods/current alias.
ODS_MODEL_SWITCHBOARD=observe

# Inference backend engine: llama-server (NVIDIA/CPU), lemonade (AMD),
# litellm (cloud), or external (installer-validated Ollama/LM Studio)
# AMD hardware auto-selects lemonade for NPU/Vulkan/ROCm acceleration
LLM_BACKEND=llama-server

# API base path: /v1 for llama-server, /api/v1 for Lemonade (set by installer)
LLM_API_BASE_PATH=/v1
LLM_API_URL=http://llama-server:8080
# Enabled switchboard installs set these so Open WebUI talks to LiteLLM/ods-current.
OPEN_WEBUI_LLM_BASE_URL=
OPEN_WEBUI_LLM_API_KEY=
# LLM_URL=                  # Optional dashboard-api diagnostics override; normally follows LLM_API_URL

# Reuse a host-managed Ollama or LM Studio model instead of downloading and
# starting ODS's llama-server. Configure this through install.sh so the
# endpoint/model are validated and the Compose topology is updated atomically:
#   ./install.sh --external-llm-url http://127.0.0.1:11434 \
#     --external-llm-provider ollama --external-llm-model qwen3.5:9b
# Interactive installs can discover a matching model and ask before reuse.
# Non-interactive discovery is opt-in with --reuse-external-llm.
# Use ./install.sh --no-external-llm to return a persisted install to managed
# local inference. EXTERNAL_LLM_URL is host-facing; the installer derives the
# container-facing host.docker.internal URL.
EXTERNAL_LLM_URL=
EXTERNAL_LLM_CONTAINER_URL=
EXTERNAL_LLM_PROVIDER=
EXTERNAL_LLM_MODEL=
SKIP_MODEL_DOWNLOAD=false

# Remote GPU provider metadata. Remote routing is active only when
# ODS_MODE=cloud and REMOTE_LLM_ENABLED=true. Provider API keys, peer tokens,
# SSH identities, and CA material are write-only lifecycle inputs; do not put
# them in .env.
REMOTE_LLM_ENABLED=false
REMOTE_LLM_TRANSPORT=
REMOTE_LLM_BASE_URL=
REMOTE_LLM_MODEL=
REMOTE_LLM_TLS_CA_FILE=
REMOTE_LLM_SSH_HOST=
REMOTE_LLM_SSH_USER=
REMOTE_LLM_SSH_PORT=
REMOTE_LLM_SSH_INFERENCE_HOST=
REMOTE_LLM_SSH_INFERENCE_PORT=
REMOTE_LLM_SSH_CONTROL_HOST=
REMOTE_LLM_SSH_CONTROL_PORT=
REMOTE_ODS_PEER_URL=

# ODS Talk image attachment vision backend. Leave empty to use the local
# inference backend. May be a host root (http://host:8080) or an
# OpenAI-compatible base URL (http://host:8080/v1 or /api/v1).
ODS_TALK_VISION_URL=
ODS_TALK_VISION_KEY=
ODS_TALK_VISION_MODEL=user.Qwen3.6-35B-A3B-Vision

# AMD local inference runtime selected by the installer.
# Linux AMD: lemonade / rocm / container
# Windows AMD: lemonade or llama-server / vulkan / host
AMD_INFERENCE_RUNTIME=
AMD_INFERENCE_BACKEND=
AMD_INFERENCE_LOCATION=
AMD_INFERENCE_PORT=8080
AMD_INFERENCE_SUPPORTED_BACKENDS=
AMD_INFERENCE_RUNTIME_MODE=
AMD_INFERENCE_MANAGED=
LEMONADE_EXTERNAL=false
LEMONADE_BASE_URL=
LEMONADE_CONTAINER_BASE_URL=
LEMONADE_API_BASE_PATH=/api/v1
LEMONADE_MODEL=

# ═══════════════════════════════════════════════════════════════════
# Provider and Hub Credentials
# ═══════════════════════════════════════════════════════════════════

ANTHROPIC_API_KEY=
OPENAI_API_KEY=
# Optional in every mode: read token for private/gated Hugging Face models.
HF_TOKEN=
# Comma-separated HTTPS hosts permitted for model artifact downloads.
# ODS_MODEL_DOWNLOAD_ALLOWED_HOSTS=huggingface.co,www.huggingface.co,hf.co
TOGETHER_API_KEY=
MINIMAX_API_KEY=

# ═══════════════════════════════════════════════════════════════════
# LLM Settings (llama-server)
# ═══════════════════════════════════════════════════════════════════

# Optional model family profile for installer-driven tier selection.
# qwen   = keep the current stable ODS defaults
# gemma4 = always use Gemma 4 tier mappings when possible
# auto   = prefer Gemma 4 on capable hardware, keep Qwen fallback for minimum/cloud paths
MODEL_PROFILE=qwen

# Model GGUF filename override (installer normally rewrites this from tier + MODEL_PROFILE)
GGUF_FILE=Qwen3.5-9B-Q4_K_M.gguf

# Context window size (tokens)
CTX_SIZE=16384

# GPU backend: nvidia or amd
GPU_BACKEND=nvidia

# Unified system RAM in GB (macOS Apple Silicon only — written by env-generator.sh).
# Passed to llama.cpp for the Metal backend to size the unified-memory pool.
# HOST_RAM_GB=24

# Model name override (installer normally rewrites this from tier + MODEL_PROFILE)
LLM_MODEL=qwen3.5-9b

# Installer recommendation metadata. During fast-start bootstrap, LLM_MODEL/GGUF_FILE
# may temporarily point at the small bootstrap model while these fields keep the
# full hardware-selected target visible in the dashboard.
MODEL_RECOMMENDED_MODEL=qwen3.5-9b
MODEL_RECOMMENDED_GGUF=Qwen3.5-9B-Q4_K_M.gguf
MODEL_RECOMMENDED_CONTEXT=16384
MODEL_RECOMMENDATION_SOURCE=catalog_fit_pre_download
MODEL_RECOMMENDATION_POLICY=context-aware-largest-capable-general-v1
MODEL_RECOMMENDATION_CONFIDENCE=high
MODEL_RECOMMENDATION_REASON=Catalog fit (context-aware-largest-capable-general-v1): Qwen 3.5 9B needs about 8GB including context/KV, fits 8.0GB GPU VRAM on nvidia, and gives 32K context. Throughput requires a local benchmark after first launch.
MODEL_RECOMMENDED_ALTERNATIVES=qwen3.5-9b-q4:32768:8;phi4-mini-q4:128000:4.38;deepseek-r1-7b-q4:32768:7
MODEL_PERFORMANCE_SOURCE=benchmark_required
MODEL_PERFORMANCE_LABEL=Benchmark after first launch
MODEL_RUNTIME_PROFILE=
MODEL_RUNTIME_PROFILE_LABEL=
MODEL_RUNTIME_PROFILE_SOURCE=
SYSTEM_RAM_GB=32

# Optional llama.cpp image override (installer sets this automatically for runtime profiles)
# LLAMA_SERVER_IMAGE=ghcr.io/ggml-org/llama.cpp:server-cuda-b9014
# Optional explicit fallback if a custom llama.cpp image tag disappears
# LLAMA_SERVER_IMAGE_FALLBACK=ghcr.io/ggml-org/llama.cpp:server-cuda-b9014
# Optional AMD Lemonade image override. Default is pinned in config/backends/amd.json.
# LEMONADE_SERVER_IMAGE=ghcr.io/lemonade-sdk/lemonade-server:v10.2.0

# ─── Architecture-specific overrides ────────────────────────────────────────────
# WHISPER_ACCELERATION is normally written by the installer. On Windows NVIDIA
# systems with drivers too old for the CUDA Whisper image, ODS keeps the rest of
# the GPU stack enabled and runs Whisper on CPU.
# WHISPER_ACCELERATION=cuda
# WHISPER_IMAGE is a forward-compat escape hatch. Windows fallback may set the
# CPU image when the CUDA tag cannot run on the installed driver.
# WHISPER_IMAGE=ghcr.io/speaches-ai/speaches:0.9.0-rc.3-cuda

# llama-server inference tuning (advanced)
# N_GPU_LAYERS=auto         # auto | all | non-negative layer count
# LLAMA_BATCH_SIZE=2048      # Batch size for prompt processing (higher = faster prefill)
# LLAMA_THREADS=4            # CPU threads for non-GPU work
# LLAMA_PARALLEL=1           # Concurrent request slots (increase for multi-user)
# LLAMA_ARG_FLASH_ATTN=auto  # auto | on | off. Use on for long-context CUDA/Metal/SYCL tuning.
# LLAMA_ARG_CACHE_TYPE_K=f16 # KV cache key type: f16 (default) or q8_0 for lower VRAM/RAM.
# LLAMA_ARG_CACHE_TYPE_V=f16 # KV cache value type: f16 (default) or q8_0 for lower VRAM/RAM.
# LLAMA_ARG_N_CPU_MOE=25     # Optional MoE only: keep first N MoE expert layers on CPU/RAM.
# LLAMA_ARG_NO_CACHE_PROMPT=1 # Optional profile flag for long-context MoE runs.
# LLAMA_ARG_CHECKPOINT_EVERY_N_TOKENS=-1 # Optional profile flag to disable context checkpoints.
# LLAMA_ARG_SPEC_TYPE=draft-mtp       # Optional: only for MTP-capable GGUFs and llama.cpp builds.
# LLAMA_ARG_SPEC_DRAFT_N_MAX=3        # Optional: speculative draft cap; benchmark per model/runtime.
# LLAMA_CPU_LIMIT=12.0       # Auto-generated: capped to CPUs actually exposed by Docker
# LLAMA_CPU_RESERVATION=2.0  # Auto-generated: reservation capped to the same ceiling
# TTS_CPU_LIMIT=8.0          # Auto-generated: capped to Docker CPUs
# TTS_CPU_RESERVATION=2.0    # Auto-generated: capped to the TTS limit
# WHISPER_CPU_LIMIT=4.0      # Auto-generated: capped to Docker CPUs
# WHISPER_CPU_RESERVATION=1.0 # Auto-generated: capped to the Whisper limit
# HERMES_CPU_LIMIT=4.0       # Auto-generated: capped to Docker CPUs
# HERMES_CPU_RESERVATION=0.5 # Auto-generated: capped to the Hermes limit
# COMFYUI_CPU_LIMIT=16.0     # Auto-generated: capped to Docker CPUs
# COMFYUI_CPU_RESERVATION=2.0 # Auto-generated: capped to the ComfyUI limit
# LLAMA_START_PERIOD=240s    # Healthcheck grace window for cold model load (bump for 70B-class on slow NVMe)

# ═══════════════════════════════════════════════════════════════════
# Ports — all overridable, defaults shown
# ═══════════════════════════════════════════════════════════════════

OLLAMA_PORT=11434          # llama-server API (external → internal 8080)
WEBUI_PORT=3000            # Open WebUI (external → internal 8080)
# SEARXNG_PORT=8888          # SearXNG metasearch (external → internal 8080)
# PERPLEXICA_PORT=3004       # Perplexica deep research (external → internal 3000)
# WHISPER_PORT=9000          # Whisper STT (external → internal 8000)
# TTS_PORT=8880              # Kokoro TTS (external → internal 8880)
# N8N_PORT=5678              # n8n workflows (external → internal 5678)
# QDRANT_PORT=6333           # Qdrant vector DB (external → internal 6333)
# QDRANT_GRPC_PORT=6334      # Qdrant gRPC (external → internal 6334)
# EMBEDDINGS_PORT=8090       # Text embeddings (external → internal 80)
# LITELLM_PORT=4000          # LiteLLM gateway (external → internal 4000)
# OPENCLAW_PORT=7860         # OpenClaw agent (external → internal 18789)
# SHIELD_PORT=8085           # Privacy Shield (external → internal 8085)
# DASHBOARD_API_PORT=3002    # Dashboard API (external → internal 3002)
# DASHBOARD_PORT=3001        # Dashboard UI (external → internal 3001)
# COMFYUI_PORT=8188          # ComfyUI image gen (external → internal 8188)
# TOKEN_SPY_PORT=3005        # Token Spy usage monitor (external → internal 8080)
# OPENCODE_PORT=3003         # OpenCode IDE web UI (host service)

# ═══════════════════════════════════════════════════════════════════
# Optional Security
# ═══════════════════════════════════════════════════════════════════

# Host Agent bind address (leave empty for platform-aware default).
# macOS/Windows: defaults to 127.0.0.1 (Docker Desktop routes via loopback).
# Linux: auto-detects Docker bridge gateway IP (e.g. 172.17.0.1) so containers
#   can reach the agent while LAN devices cannot.  Falls back to 127.0.0.1 if
#   detection fails.  Set explicitly to override.
# ODS_AGENT_BIND=

# Host Agent HTTP port used by dashboard-api for privileged host operations.
# ODS_AGENT_PORT=7710

# Hostname/IP dashboard-api containers use to reach ods-host-agent.
# Docker Desktop uses host.docker.internal; Colima installers set the private
# vmnet host gateway because its user-mode host route can become unreachable.
# ODS_AGENT_HOST=host.docker.internal

# Managed macOS/Colima private bridge state. Installers discover these values;
# do not set them manually unless repairing a custom Colima network profile.
# ODS_MACOS_HOST_GATEWAY=
# ODS_MACOS_VM_IP=
# ODS_MACOS_LLM_BRIDGE_ENABLED=
# ODS_MACOS_HOST_AGENT_BRIDGE_ENABLED=
# ODS_NATIVE_LLAMA_PORT=8080

# Dashboard API key (generate: openssl rand -hex 32)
# DASHBOARD_API_KEY=

# ODS host agent key (generate: openssl rand -hex 32) — auto-generated by installer; rotating breaks host-agent authentication
# ODS_AGENT_KEY=

# Session-cookie signing secret. Used by dashboard-api to mint
# HMAC-signed `ods-session` cookies at magic-link redemption, and
# by the Hermes auth-proxy to verify them via /api/auth/verify-session.
# Generate with: openssl rand -hex 32. Rotating invalidates all
# currently-issued session cookies (forces re-redemption).
# ODS_SESSION_SECRET=

# Internal Hermes dashboard/WebSocket token. Auto-generated by the installer
# and kept stable across container restarts. Rotating it requires clients with
# an open Hermes tab to refresh.
# HERMES_DASHBOARD_SESSION_TOKEN=

# Public/custom URL and cookie domain for invite links, tunnels, and
# subdomain SSO. Leave empty for normal localhost or mDNS LAN generation.
# ODS_PUBLIC_URL=
# Optional reverse-proxy browser URLs. The dashboard uses these exact URLs
# for service launch links before falling back to the current host + port.
# ODS_SERVICE_PUBLIC_URLS={"open-webui":"https://chat.example.com","comfyui":"https://comfy.example.com"}
# OPEN_WEBUI_PUBLIC_URL=https://chat.example.com
# COMFYUI_PUBLIC_URL=https://comfy.example.com
# N8N_PUBLIC_URL=https://n8n.example.com
# ODS_TALK_PUBLIC_URL=https://talk.example.com
# ODS_COOKIE_DOMAIN=

# Privacy Shield API key (generate: openssl rand -hex 32) - auto-generated by installer; shared between dashboard-api and privacy-shield containers
# SHIELD_API_KEY=

# Qdrant API key (generate: openssl rand -hex 32)
# QDRANT_API_KEY=

# Token Spy API key (auto-generated by installer; shared with dashboard-api)
# TOKEN_SPY_API_KEY=

# ═══════════════════════════════════════════════════════════════════
# Langfuse (LLM Observability) — optional, disabled by default
# ═══════════════════════════════════════════════════════════════════

LANGFUSE_PORT=3006
LANGFUSE_ENABLED=false
LANGFUSE_NEXTAUTH_SECRET=  # auto-generated during install
LANGFUSE_SALT=            # auto-generated during install
LANGFUSE_ENCRYPTION_KEY=  # auto-generated during install
LANGFUSE_DB_PASSWORD=     # auto-generated during install
LANGFUSE_CLICKHOUSE_PASSWORD=  # auto-generated during install
LANGFUSE_REDIS_PASSWORD=       # auto-generated during install
LANGFUSE_MINIO_ACCESS_KEY=     # auto-generated during install
LANGFUSE_MINIO_SECRET_KEY=     # auto-generated during install
LANGFUSE_PROJECT_PUBLIC_KEY=   # auto-generated during install
LANGFUSE_PROJECT_SECRET_KEY=   # auto-generated during install
LANGFUSE_INIT_PROJECT_ID=      # auto-generated during install
LANGFUSE_INIT_USER_EMAIL=admin@ods.local
LANGFUSE_INIT_USER_PASSWORD=   # auto-generated during install

# ═══════════════════════════════════════════════════════════════════
# Multi-GPU Settings (auto-populated by installer for multi-GPU)
# ═══════════════════════════════════════════════════════════════════

# GPU_ASSIGNMENT_JSON_B64=           # Base64-encoded GPU assignment JSON
# LLAMA_SERVER_GPU_UUIDS=            # GPU UUIDs for llama-server (NVIDIA, comma-separated)
# LLAMA_SERVER_GPU_INDICES=          # GPU indices for llama-server (AMD, comma-separated)
# LLAMA_ARG_SPLIT_MODE=none          # none | layer (pipeline) | row (tensor/hybrid)
# LLAMA_ARG_TENSOR_SPLIT=            # Proportional VRAM weights (e.g. 3,1)
# COMFYUI_GPU_UUID=                  # GPU UUID for ComfyUI (NVIDIA)
# WHISPER_GPU_UUID=                  # GPU UUID for Whisper (NVIDIA)
# EMBEDDINGS_GPU_UUID=               # GPU UUID for embeddings (NVIDIA)
# ROCR_VISIBLE_DEVICES=              # AMD GPU indices visible to ROCm (comma-separated)
# VIDEO_GID=44                       # Host 'video' group GID (AMD)
# RENDER_GID=992                     # Host 'render' group GID (AMD)
# HSA_OVERRIDE_GFX_VERSION=          # AMD gfx version override for ROCm compatibility
# LLM_MODEL_SIZE_MB=                 # Approximate model size in MB

# ═══════════════════════════════════════════════════════════════════
# Optional — Voice, Web UI, n8n
# ═══════════════════════════════════════════════════════════════════

# Whisper model (tiny, base, small, medium, large-v3-turbo)
# WHISPER_MODEL=base

# Open WebUI STT settings. Installer auto-selects AUDIO_STT_MODEL by GPU:
#   NVIDIA  → deepdml/faster-whisper-large-v3-turbo-ct2 (faster, ~1.5GB)
#   AMD/CPU → Systran/faster-whisper-base (~130MB)
# Override to use a different model — must then run `ods restart` and
# manually download the new model via:
#   curl -X POST --max-time 3600 http://localhost:9000/v1/models/<url-encoded-model-id>
# AUDIO_STT_ENGINE=openai
# AUDIO_STT_OPENAI_API_BASE_URL=http://whisper:8000/v1
# AUDIO_STT_OPENAI_API_KEY=
# WHISPER_ACCELERATION=cpu
# AUDIO_STT_MODEL=Systran/faster-whisper-base

# Internal URLs used by dashboard-api and ODS Talk when running diagnostics
# or voice flows from inside the Docker network.
# WHISPER_URL=http://whisper:8000
# TTS_URL=http://tts:8880
# EMBEDDING_URL=http://embeddings:80

# Kokoro/Open WebUI TTS settings
# AUDIO_TTS_ENGINE=openai
# AUDIO_TTS_OPENAI_API_BASE_URL=http://tts:8880/v1
# AUDIO_TTS_OPENAI_API_KEY=
# AUDIO_TTS_MODEL=kokoro
# AUDIO_TTS_VOICE=af_heart
# Legacy Kokoro TTS voice name
# TTS_VOICE=en_US-lessac-medium

# Open WebUI authentication (true/false). Installers default this to false for
# loopback-only ODS and true when services are exposed to the network.
# WEBUI_AUTH=false

# Open WebUI PWA / branding name. Shown as the app title, page title, and PWA
# install label (the text next to the icon on a phone's home screen).
# WEBUI_NAME=ODS

# Optional Open WebUI public URL — used for share links, OAuth callbacks, and
# PWA install metadata. Leave empty for traditional localhost usage. After
# enabling ods-proxy + mDNS, set this to the reachable chat subdomain:
# http://chat.<ODS_DEVICE_NAME>.local. Override with a Tailscale Funnel /
# Cloudflare Tunnel / custom domain URL when fronted by one.
# WEBUI_URL=http://chat.ods.local

# Device name used by mDNS announcements and as the hostname segment for
# proxy/headless URLs. Letters / digits / hyphens only — short and memorable
# wins (e.g. "ods", "kitchen", "lab").
# ODS_DEVICE_NAME=ods

# Web search settings
# ENABLE_WEB_SEARCH=true
# WEB_SEARCH_ENGINE=searxng

# System timezone (used by Open WebUI and n8n)
# TIMEZONE=UTC

# n8n settings
# N8N_HOST=localhost           # n8n hostname
# N8N_WEBHOOK_URL=http://localhost:5678  # n8n webhook URL (for external access)
# N8N_SECURE_COOKIE=auto        # false only for loopback HTTP; true for HTTPS/LAN
# N8N_PROXY_HOPS=0              # set to the proxy count when using HTTPS ingress

# Embedding model served by the bundled Hugging Face TEI runtime. Use a TEI-
# compatible Hugging Face repository ID; GGUF/Q4 files are not supported here.
# EMBEDDING_MODEL=BAAI/bge-base-en-v1.5
# Open WebUI uses EMBEDDING_MODEL as its first-boot default. After first boot,
# its Admin Panel value is persistent. Set this override only when the endpoint
# points to a different embeddings provider/model.
# RAG_EMBEDDING_MODEL=
# RAG_OPENAI_API_BASE_URL=http://embeddings:80/v1
# RAG_OPENAI_API_KEY=              # only for authenticated external providers
# Larger models such as BAAI/bge-m3 may need a higher container memory limit.
# EMBEDDINGS_MEMORY_LIMIT=4G

# Image generation (ComfyUI + SDXL Lightning)
# ENABLE_IMAGE_GENERATION=true

# ═══════════════════════════════════════════════════════════════════
# Agent Policy Engine (APE)
# ═══════════════════════════════════════════════════════════════════
#
# APE audits and can enforce policy decisions for agent tool usage.
# APE_PORT=7890
# APE_RATE_LIMIT_RPM=60
# APE_STRICT_MODE=false

# ═══════════════════════════════════════════════════════════════════
# AMD-specific (only needed with GPU_BACKEND=amd)
# ═══════════════════════════════════════════════════════════════════

# VIDEO_GID=44               # `getent group video | cut -d: -f3`
# RENDER_GID=992             # `getent group render | cut -d: -f3`
# HSA_OVERRIDE_GFX_VERSION=11.5.1  # Required for Strix Halo gfx1151
# HSA_XNACK=1               # Extended memory access for large KV caches
# ROCBLAS_USE_HIPBLASLT=1    # 2-3x faster prompt processing via hipBLASLt
# AMDGPU_TARGET=gfx1151      # GPU arch for llama.cpp build (gfx1151, gfx1100, etc.)
# LLAMA_CPP_REF=b8763          # llama.cpp release tag to build (pin to avoid breakage)

# ═══════════════════════════════════════════════════════════════════
# Advanced
# ═══════════════════════════════════════════════════════════════════

# Container user/group IDs
# UID=1000
# GID=1000

# Privacy Shield settings
# PII_CACHE_ENABLED=true
# PII_CACHE_SIZE=1000
# PII_CACHE_TTL=300
# LOG_LEVEL=info

# OpenClaw bootstrap model (small model for instant startup)
# BOOTSTRAP_MODEL=qwen3:8b-q4_K_M

# Dashboard API internal URLs (usually Docker-internal, not user-facing)
# KOKORO_URL=http://tts:8880
# N8N_URL=http://n8n:5678

# NVIDIA llama-server memory limit (Docker). When unset, installers reserve
# memory for the host and other ODS services, cap the result at 64G, and use
# the smaller of physical RAM and Docker Desktop's VM allocation.
# Set an explicit value to override the calculated limit. Remove the generated
# key before rerunning the installer if Docker's memory allocation changes and
# you want ODS to calculate it again.
# LLAMA_SERVER_MEMORY_LIMIT=64G

# llama-server CPU core limit (auto-capped during install/update)
# Tune this to control how many CPU cores llama-server may use.
# LLAMA_CPU_LIMIT=8.0
# ═══════════════════════════════════════════════════════════════════
# ODS Proxy (LAN reverse proxy on port 80)
# ═══════════════════════════════════════════════════════════════════
# Opt-in Caddy reverse proxy that fronts dashboard / chat / API on
# port 80, so `http://<device>.local` (no port) works from any LAN
# device. Without this, services bind to 127.0.0.1 and `ods.local`
# resolves to a port that's not open.
# Enable: `ods enable ods-proxy`. See docs/ODS-PROXY.md.
#
# ODS_PROXY_PORT=80                    # Host port (standard HTTP)
# ODS_PROXY_TLS_PORT=443               # HTTPS port (deferred; HTTP only in v1)
# ODS_PROXY_BIND=0.0.0.0               # LAN-exposed by default — that's the point

# Hermes Agent (Remote-capable Chat + Generalist Agent)
# ═══════════════════════════════════════════════════════════════════
# Hermes Agent (Nous Research, MIT) — chat-first generalist agent with
# persistent memory, autonomous skill creation, and 70+ tools. ODS
# Server runs the upstream image pinned by version and points it at the
# local LLM. Browser UI ships in the upstream image (port 9119).
# Enable: `ods enable hermes`. Owner-card users reach the mobile ODS
# Talk portal at http://talk.<device>.local/talk; advanced users can still
# reach Hermes at http://hermes.<device>.local (auth-gated by the proxy).
# Hermes itself is internal-only —
# there's no HERMES_PORT knob in this stack.
#
# HERMES_LLM_BASE_URL=http://llama-server:8080/v1  # OpenAI-compat endpoint
# HERMES_LLM_API_KEY=sk-ods-hermes-local  # Dummy (OpenAI SDK requires non-empty)
# HERMES_AGENT_IMAGE=nousresearch/hermes-agent:v2026.6.5  # Upstream Hermes image
# HERMES_AGENT_IMAGE_FALLBACK=                         # Optional explicit fallback if upstream retags
# HERMES_LANGUAGE=en                     # UI language
# SEARXNG_URL=http://searxng:8080        # Hermes web-search backend
# (No HERMES_MODEL_NAME — the model lives in /opt/data/config.yaml,
#  not in env. Edit the file after first start to switch models.)
#
# Optional Hermes WhatsApp gateway. Disabled by default; ODS's Hermes
# config pins WhatsApp's internal bridge to 3010 so it does not collide with
# Open WebUI's host port 3000 when users intentionally enable WhatsApp.
# WHATSAPP_ENABLED=false
# WHATSAPP_MODE=self-chat
# WHATSAPP_ALLOWED_USERS=15551234567
# WHATSAPP_REQUIRE_MENTION=
# WHATSAPP_FREE_RESPONSE_CHATS=
# WHATSAPP_REPLY_PREFIX=
#
# Hermes is reached on the LAN via ods-hermes-proxy (Caddy sidecar) which
# verifies the ods-session cookie against dashboard-api's verify endpoint
# (forward_auth → HMAC signature check via ODS_SESSION_SECRET).
# Direct Hermes port is NOT bound to the host — only the proxy is LAN-facing.
# HERMES_PROXY_PORT=9120                 # LAN entry — what users actually browse to
# HERMES_PROXY_UPSTREAM=ods-hermes:9119  # Where the proxy forwards (internal DNS)
# ODS_AUTH_UPSTREAM=ods-dashboard-api:3002  # Where forward_auth verifies sessions
# HERMES_PROXY_AUTH_DOCS_URL=            # Optional help link on the auth-required page

# Tailscale (Remote Access)
# ═══════════════════════════════════════════════════════════════════
# Optional remote-access via Tailscale's mesh VPN. Generate an auth
# key at https://login.tailscale.com/admin/settings/keys and paste
# below. Once joined the device is reachable as <hostname>.<tailnet>.ts.net
# from any other tailnet member.
#
# IMPORTANT: joining the tailnet alone isn't enough. For the device to
# actually answer HTTP at <hostname>.tail-xxxxx.ts.net, the host has to
# be listening for non-loopback traffic. That means:
#   1. ods-proxy is enabled (Caddy fielding port 80 + routing /chat,
#      /api/*, /auth/* to backends)
#   2. BIND_ADDRESS=0.0.0.0 in this .env (loopback-only services don't
#      accept tailnet packets even with network_mode: host)
# See docs/TAILSCALE.md for the full prerequisites and architecture.
#
# TS_AUTHKEY is consumed exactly once on first join. After that the
# tailscale daemon caches the node key in data/tailscale/, so you can
# rotate or delete the auth key in the Tailscale admin.
#
# TS_AUTHKEY=tskey-auth-xxxxxxxxxxxxxxxxxxxxxx
# TS_HOSTNAME=                         # Defaults to ODS_DEVICE_NAME
# TS_EXTRA_ARGS=                       # Optional, e.g. --advertise-tags=tag:ods