7e87dbaa10
- Add System Requirements section to README.md covering AirLLM (Apple Silicon M1-M4, 16 GB RAM, ~15 GB disk) and Ollama (any OS, 8 GB RAM) paths with an explicit fallback note - Add "airllm" to timmy_model_backend Literal in config.py so the setting is a valid pydantic value (was previously undocumented) - Add AirLLM import guard in create_timmy(): logs a warning and falls back to Ollama on non-Apple-Silicon or when airllm is not installed - Expand .env.example TIMMY_MODEL_BACKEND comment with hardware specs and install instructions - Add tests/unit/test_airllm_backend.py — 9 tests covering is_apple_silicon() detection and airllm graceful degradation on Intel/Linux and missing-package paths Fixes #1284 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
625 lines
32 KiB
Python
625 lines
32 KiB
Python
import logging as _logging
|
|
import os
|
|
import sys
|
|
from datetime import UTC
|
|
from datetime import datetime as _datetime
|
|
from typing import Literal
|
|
|
|
from pydantic_settings import BaseSettings, SettingsConfigDict
|
|
|
|
APP_START_TIME: _datetime = _datetime.now(UTC)
|
|
|
|
|
|
def normalize_ollama_url(url: str) -> str:
|
|
"""Replace localhost with 127.0.0.1 to avoid IPv6 resolution delays."""
|
|
return url.replace("localhost", "127.0.0.1")
|
|
|
|
|
|
class Settings(BaseSettings):
|
|
"""Central configuration — all env-var access goes through this class."""
|
|
|
|
# Display name for the primary agent — override with AGENT_NAME env var
|
|
agent_name: str = "Agent"
|
|
|
|
# Ollama host — override with OLLAMA_URL env var or .env file
|
|
ollama_url: str = "http://localhost:11434"
|
|
|
|
@property
|
|
def normalized_ollama_url(self) -> str:
|
|
"""Return ollama_url with localhost replaced by 127.0.0.1."""
|
|
return normalize_ollama_url(self.ollama_url)
|
|
|
|
# LLM model passed to Agno/Ollama — override with OLLAMA_MODEL
|
|
# qwen3:14b (Q5_K_M) is the primary model: tool calling F1 0.971, ~17.5 GB
|
|
# at 32K context — optimal for M3 Max 36 GB (Issue #1063).
|
|
# qwen3:30b exceeded memory budget at 32K+ context on 36 GB hardware.
|
|
ollama_model: str = "qwen3:14b"
|
|
|
|
# Fast routing model — override with OLLAMA_FAST_MODEL
|
|
# qwen3:8b (Q6_K): tool calling F1 0.933 at ~45-55 tok/s (2x speed of 14B).
|
|
# Use for routine tasks: simple tool calls, file reads, status checks.
|
|
# Combined memory with qwen3:14b: ~17 GB — both can stay loaded simultaneously.
|
|
ollama_fast_model: str = "qwen3:8b"
|
|
|
|
# Maximum concurrently loaded Ollama models — override with OLLAMA_MAX_LOADED_MODELS
|
|
# Set to 2 to keep qwen3:8b (fast) + qwen3:14b (primary) both hot.
|
|
# Requires setting OLLAMA_MAX_LOADED_MODELS=2 in the Ollama server environment.
|
|
ollama_max_loaded_models: int = 2
|
|
|
|
# Context window size for Ollama inference — override with OLLAMA_NUM_CTX
|
|
# qwen3:14b at 32K: ~17.5 GB total (weights + KV cache) on M3 Max 36 GB.
|
|
# Set to 0 to use model defaults.
|
|
ollama_num_ctx: int = 32768
|
|
|
|
# Maximum models loaded simultaneously in Ollama — override with OLLAMA_MAX_LOADED_MODELS
|
|
# Set to 2 so Qwen3-8B and Qwen3-14B can stay hot concurrently (~17 GB combined).
|
|
# Requires Ollama ≥ 0.1.33. Export this to the Ollama process environment:
|
|
# OLLAMA_MAX_LOADED_MODELS=2 ollama serve
|
|
# or add it to your systemd/launchd unit before starting the harness.
|
|
ollama_max_loaded_models: int = 2
|
|
|
|
# Fallback model chains — override with FALLBACK_MODELS / VISION_FALLBACK_MODELS
|
|
# as comma-separated strings, e.g. FALLBACK_MODELS="qwen3:8b,qwen2.5:14b"
|
|
# Or edit config/providers.yaml → fallback_chains for the canonical source.
|
|
fallback_models: list[str] = [
|
|
"qwen3:8b",
|
|
"qwen2.5:14b",
|
|
"qwen2.5:7b",
|
|
"llama3.1:8b-instruct",
|
|
"llama3.1",
|
|
"llama3.2:3b",
|
|
]
|
|
vision_fallback_models: list[str] = [
|
|
"llama3.2:3b",
|
|
"llava:7b",
|
|
"qwen2.5-vl:3b",
|
|
"moondream:1.8b",
|
|
]
|
|
|
|
# Set DEBUG=true to enable /docs and /redoc (disabled by default)
|
|
debug: bool = False
|
|
|
|
# Telegram bot token — set via TELEGRAM_TOKEN env var or the /telegram/setup endpoint
|
|
telegram_token: str = ""
|
|
|
|
# Discord bot token — set via DISCORD_TOKEN env var or the /discord/setup endpoint
|
|
discord_token: str = ""
|
|
|
|
# ── Discord action confirmation ──────────────────────────────────────────
|
|
# When True, dangerous tools (shell, write_file, python) require user
|
|
# confirmation via Discord button before executing.
|
|
discord_confirm_actions: bool = True
|
|
# Seconds to wait for user confirmation before auto-rejecting.
|
|
discord_confirm_timeout: int = 120
|
|
|
|
# ── Backend selection ────────────────────────────────────────────────────
|
|
# "ollama" — always use Ollama (default, safe everywhere)
|
|
# "airllm" — AirLLM layer-by-layer loading (Apple Silicon only; degrades to Ollama)
|
|
# "auto" — pick best available local backend, fall back to Ollama
|
|
timmy_model_backend: Literal["ollama", "airllm", "grok", "claude", "auto"] = "ollama"
|
|
|
|
# ── Grok (xAI) — opt-in premium cloud backend ────────────────────────
|
|
# Grok is a premium augmentation layer — local-first ethos preserved.
|
|
# Only used when explicitly enabled and query complexity warrants it.
|
|
grok_enabled: bool = False
|
|
xai_api_key: str = ""
|
|
xai_base_url: str = "https://api.x.ai/v1"
|
|
grok_default_model: str = "grok-3-fast"
|
|
grok_max_sats_per_query: int = 200
|
|
grok_sats_hard_cap: int = 100 # Absolute ceiling on sats per Grok query
|
|
grok_free: bool = False # Skip Lightning invoice when user has own API key
|
|
|
|
# ── Database ──────────────────────────────────────────────────────────
|
|
db_busy_timeout_ms: int = 5000 # SQLite PRAGMA busy_timeout (ms)
|
|
|
|
# ── Claude (Anthropic) — cloud fallback backend ────────────────────────
|
|
# Used when Ollama is offline and local inference isn't available.
|
|
# Set ANTHROPIC_API_KEY to enable. Default model is Haiku (fast + cheap).
|
|
anthropic_api_key: str = ""
|
|
claude_model: str = "haiku"
|
|
|
|
# ── Content Moderation ──────────────────────────────────────────────
|
|
# Three-layer moderation pipeline for AI narrator output.
|
|
# Uses Llama Guard via Ollama with regex fallback.
|
|
moderation_enabled: bool = True
|
|
moderation_guard_model: str = "llama-guard3:1b"
|
|
# Default confidence threshold — per-game profiles can override.
|
|
moderation_threshold: float = 0.8
|
|
|
|
# ── Spark Intelligence ────────────────────────────────────────────────
|
|
# Enable/disable the Spark cognitive layer.
|
|
# When enabled, Spark captures swarm events, runs EIDOS predictions,
|
|
# consolidates memories, and generates advisory recommendations.
|
|
spark_enabled: bool = True
|
|
|
|
# ── Git / DevOps ──────────────────────────────────────────────────────
|
|
git_default_repo_dir: str = "~/repos"
|
|
|
|
# Repository root - auto-detected but can be overridden
|
|
# This is the main project directory where .git lives
|
|
repo_root: str = ""
|
|
|
|
# ── Creative — Image Generation (Pixel) ───────────────────────────────
|
|
flux_model_id: str = "black-forest-labs/FLUX.1-schnell"
|
|
image_output_dir: str = "data/images"
|
|
image_default_steps: int = 4
|
|
|
|
# ── Creative — Music Generation (Lyra) ────────────────────────────────
|
|
music_output_dir: str = "data/music"
|
|
ace_step_model: str = "ace-step/ACE-Step-v1.5"
|
|
|
|
# ── Creative — Video Generation (Reel) ────────────────────────────────
|
|
video_output_dir: str = "data/video"
|
|
wan_model_id: str = "Wan-AI/Wan2.1-T2V-1.3B"
|
|
video_default_resolution: str = "480p"
|
|
|
|
# ── Creative — Pipeline / Assembly ────────────────────────────────────
|
|
creative_output_dir: str = "data/creative"
|
|
video_transition_duration: float = 1.0
|
|
default_video_codec: str = "libx264"
|
|
|
|
# ── L402 Lightning ───────────────────────────────────────────────────
|
|
# HMAC secrets for macaroon signing and invoice verification.
|
|
# Generate with: python3 -c "import secrets; print(secrets.token_hex(32))"
|
|
# In production (TIMMY_ENV=production), these MUST be set or the app will refuse to start.
|
|
l402_hmac_secret: str = ""
|
|
l402_macaroon_secret: str = ""
|
|
lightning_backend: Literal["mock", "lnd"] = "mock"
|
|
|
|
# ── Privacy / Sovereignty ────────────────────────────────────────────
|
|
# Disable Agno telemetry for air-gapped/sovereign deployments.
|
|
# Default is False (telemetry disabled) to align with sovereign AI vision.
|
|
telemetry_enabled: bool = False
|
|
|
|
# ── Sovereignty Metrics ──────────────────────────────────────────────
|
|
# Alert when API cost per research task exceeds this threshold (USD).
|
|
sovereignty_api_cost_alert_threshold: float = 1.00
|
|
|
|
# CORS allowed origins for the web chat interface (Gitea Pages, etc.)
|
|
# Set CORS_ORIGINS as a comma-separated list, e.g. "http://localhost:3000,https://example.com"
|
|
cors_origins: list[str] = [
|
|
"http://localhost:3000",
|
|
"http://localhost:8000",
|
|
"http://127.0.0.1:3000",
|
|
"http://127.0.0.1:8000",
|
|
]
|
|
|
|
# ── Matrix Frontend Integration ────────────────────────────────────────
|
|
# URL of the Matrix frontend (Replit/Tailscale) for CORS.
|
|
# When set, this origin is added to CORS allowed_origins.
|
|
# Example: "http://100.124.176.28:8080" or "https://alexanderwhitestone.com"
|
|
matrix_frontend_url: str = "" # Empty = disabled
|
|
|
|
# WebSocket authentication token for Matrix connections.
|
|
# When set, clients must provide this token via ?token= query param
|
|
# or in the first message as {"type": "auth", "token": "..."}.
|
|
# Empty/unset = auth disabled (dev mode).
|
|
matrix_ws_token: str = ""
|
|
|
|
# Trusted hosts for the Host header check (TrustedHostMiddleware).
|
|
# Set TRUSTED_HOSTS as a comma-separated list. Wildcards supported (e.g. "*.ts.net").
|
|
# Defaults include localhost + Tailscale MagicDNS. Add your Tailscale IP if needed.
|
|
trusted_hosts: list[str] = [
|
|
"localhost",
|
|
"127.0.0.1",
|
|
"*.local",
|
|
"*.ts.net",
|
|
"testserver",
|
|
]
|
|
|
|
# Environment mode: development | production
|
|
# In production, security settings are strictly enforced.
|
|
timmy_env: Literal["development", "production"] = "development"
|
|
|
|
# ── Memory Management ──────────────────────────────────────────────
|
|
# Auto-prune vector store memories older than this many days on startup.
|
|
# Set to 0 to disable auto-pruning.
|
|
memory_prune_days: int = 90
|
|
# When True, fact-type memories are kept even when older than the TTL.
|
|
memory_prune_keep_facts: bool = True
|
|
# Maximum size in MB for the memory/notes/ vault directory.
|
|
# When exceeded, a warning is logged. Set to 0 to disable.
|
|
memory_vault_max_mb: int = 100
|
|
# Auto-prune thoughts older than this many days. 0 = disabled.
|
|
thoughts_prune_days: int = 90
|
|
# Minimum thoughts to keep regardless of age.
|
|
thoughts_prune_keep_min: int = 200
|
|
# Auto-prune system events older than this many days. 0 = disabled.
|
|
events_prune_days: int = 90
|
|
# Minimum events to keep regardless of age.
|
|
events_prune_keep_min: int = 200
|
|
|
|
# ── Agentic Loop ──────────────────────────────────────────────────
|
|
# Maximum steps the agentic loop will execute before stopping.
|
|
max_agent_steps: int = 10
|
|
|
|
# ── Test / Diagnostics ─────────────────────────────────────────────
|
|
# Skip loading heavy embedding models (for tests / low-memory envs).
|
|
timmy_skip_embeddings: bool = False
|
|
# Embedding backend: "ollama" for Ollama, "local" for sentence-transformers.
|
|
timmy_embedding_backend: Literal["ollama", "local"] = "local"
|
|
# Ollama model to use for embeddings (e.g., "nomic-embed-text").
|
|
ollama_embedding_model: str = "nomic-embed-text"
|
|
# Disable CSRF middleware entirely (for tests).
|
|
timmy_disable_csrf: bool = False
|
|
# Mark the process as running in test mode.
|
|
timmy_test_mode: bool = False
|
|
|
|
# ── Brain / rqlite ─────────────────────────────────────────────────
|
|
# URL of the local rqlite node for distributed memory.
|
|
# Empty string means rqlite is not configured.
|
|
rqlite_url: str = ""
|
|
# Source identifier for brain memory entries.
|
|
brain_source: str = "default"
|
|
# Path override for the local brain SQLite database.
|
|
brain_db_path: str = ""
|
|
|
|
# ── Security Tuning ───────────────────────────────────────────────
|
|
# Set to True in production to mark CSRF cookies as Secure (HTTPS only).
|
|
csrf_cookie_secure: bool = False
|
|
# Maximum size in bytes for chat API request bodies.
|
|
chat_api_max_body_bytes: int = 1_048_576 # 1 MB
|
|
|
|
# ── Self-Modification ──────────────────────────────────────────────
|
|
# Enable self-modification capabilities. When enabled, the agent can
|
|
# edit its own source code, run tests, and commit changes.
|
|
self_modify_enabled: bool = False
|
|
self_modify_max_retries: int = 2
|
|
self_modify_allowed_dirs: str = "src,tests"
|
|
self_modify_backend: str = "auto" # "ollama", "anthropic", or "auto"
|
|
|
|
# ── Work Orders ──────────────────────────────────────────────────
|
|
# External users and agents can submit work orders for improvements.
|
|
work_orders_enabled: bool = True
|
|
work_orders_auto_execute: bool = False # Master switch for auto-execution
|
|
work_orders_auto_threshold: str = (
|
|
"low" # Max priority that auto-executes: "low" | "medium" | "high" | "none"
|
|
)
|
|
|
|
# ── Custom Weights & Models ──────────────────────────────────────
|
|
# Directory for custom model weights (GGUF, safetensors, HF checkpoints).
|
|
# Models placed here can be registered at runtime and assigned to agents.
|
|
custom_weights_dir: str = "data/models"
|
|
# Enable the reward model for scoring agent outputs (PRM-style).
|
|
reward_model_enabled: bool = False
|
|
# Reward model name (must be available via Ollama or a custom weight path).
|
|
reward_model_name: str = ""
|
|
# Minimum votes for majority-vote reward scoring (odd number recommended).
|
|
reward_model_votes: int = 3
|
|
|
|
# ── Browser Local Models (iPhone / WebGPU) ───────────────────────
|
|
# Enable in-browser LLM inference via WebLLM for offline iPhone use.
|
|
# When enabled, the mobile dashboard loads a small model directly
|
|
# in the browser — no server or Ollama required.
|
|
browser_model_enabled: bool = True
|
|
# WebLLM model ID — must be a pre-compiled MLC model.
|
|
# Recommended for iPhone: SmolLM2-360M (fast) or Qwen3-0.6B (smart).
|
|
browser_model_id: str = "SmolLM2-360M-Instruct-q4f16_1-MLC"
|
|
# Fallback to server when browser model is unavailable or too slow.
|
|
browser_model_fallback: bool = True
|
|
|
|
# ── Deep Focus Mode ─────────────────────────────────────────────
|
|
# "deep" = single-problem context; "broad" = default multi-task.
|
|
focus_mode: Literal["deep", "broad"] = "broad"
|
|
|
|
# ── Default Thinking ──────────────────────────────────────────────
|
|
# When enabled, the agent starts an internal thought loop on server start.
|
|
thinking_enabled: bool = True
|
|
thinking_interval_seconds: int = 300 # 5 minutes between thoughts
|
|
thinking_timeout_seconds: int = 120 # max wall-clock time per thinking cycle
|
|
thinking_distill_every: int = 10 # distill facts from thoughts every Nth thought
|
|
thinking_issue_every: int = 20 # file Gitea issues from thoughts every Nth thought
|
|
thinking_memory_check_every: int = 50 # check memory status every Nth thought
|
|
thinking_idle_timeout_minutes: int = 60 # pause thoughts after N minutes without user input
|
|
|
|
# ── Gitea Integration ─────────────────────────────────────────────
|
|
# Local Gitea instance for issue tracking and self-improvement.
|
|
# These values are passed as env vars to the gitea-mcp server process.
|
|
gitea_url: str = "http://localhost:3000"
|
|
gitea_token: str = "" # GITEA_TOKEN env var; falls back to .timmy_gitea_token
|
|
gitea_repo: str = "rockachopa/Timmy-time-dashboard" # owner/repo
|
|
gitea_enabled: bool = True
|
|
|
|
# ── MCP Servers ────────────────────────────────────────────────────
|
|
# External tool servers connected via Model Context Protocol (stdio).
|
|
mcp_gitea_command: str = "gitea-mcp-server -t stdio"
|
|
mcp_filesystem_command: str = "npx -y @modelcontextprotocol/server-filesystem"
|
|
mcp_timeout: int = 15
|
|
mcp_bridge_timeout: int = 60 # HTTP timeout for MCP bridge Ollama calls (seconds)
|
|
|
|
# ── Backlog Triage Loop ────────────────────────────────────────────
|
|
# Autonomous loop: fetch open issues, score, assign to agents.
|
|
backlog_triage_enabled: bool = False
|
|
# Seconds between triage cycles (default: 15 minutes).
|
|
backlog_triage_interval_seconds: int = 900
|
|
# When True, score and summarize but don't write to Gitea.
|
|
backlog_triage_dry_run: bool = False
|
|
# Create a daily triage summary issue/comment.
|
|
backlog_triage_daily_summary: bool = True
|
|
|
|
# ── Loop QA (Self-Testing) ─────────────────────────────────────────
|
|
# Self-test orchestrator that probes capabilities alongside the thinking loop.
|
|
loop_qa_enabled: bool = True
|
|
loop_qa_interval_ticks: int = 5 # run 1 self-test every Nth thinking tick (~25 min)
|
|
loop_qa_upgrade_threshold: int = 3 # consecutive failures → file task
|
|
loop_qa_max_per_hour: int = 12 # safety throttle
|
|
|
|
# ── Vassal Protocol (Autonomous Orchestrator) ─────────────────────
|
|
# Timmy as lead decision-maker: triage backlog, dispatch agents, monitor health.
|
|
# See timmy/vassal/ for implementation.
|
|
vassal_enabled: bool = False # off by default — enable when Qwen3-14B is loaded
|
|
vassal_cycle_interval: int = 300 # seconds between orchestration cycles (5 min)
|
|
vassal_max_dispatch_per_cycle: int = 10 # cap on new dispatches per cycle
|
|
vassal_stuck_threshold_minutes: int = 120 # minutes before agent issue is "stuck"
|
|
vassal_idle_threshold_minutes: int = 30 # minutes before agent is "idle"
|
|
|
|
# ── Paperclip AI — orchestration bridge ────────────────────────────
|
|
# URL where the Paperclip server listens.
|
|
# For VPS deployment behind nginx, use the public domain.
|
|
paperclip_url: str = "http://localhost:3100"
|
|
# Enable/disable the Paperclip integration.
|
|
paperclip_enabled: bool = False
|
|
# API key or auth-gate cookie for authenticating with Paperclip.
|
|
paperclip_api_key: str = ""
|
|
# Timmy's agent ID in the Paperclip org chart.
|
|
paperclip_agent_id: str = ""
|
|
# Company ID in Paperclip — required for most API calls.
|
|
paperclip_company_id: str = ""
|
|
# Timeout in seconds for Paperclip HTTP calls.
|
|
paperclip_timeout: int = 30
|
|
# How often (seconds) Timmy polls Paperclip for work (0 = disabled).
|
|
paperclip_poll_interval: int = 0
|
|
|
|
# ── OpenFang — vendored agent runtime ─────────────────────────────
|
|
# URL where the OpenFang sidecar listens. Set to the Docker service
|
|
# name when running in compose, or localhost for bare-metal dev.
|
|
openfang_url: str = "http://localhost:8080"
|
|
# Enable/disable OpenFang integration. When disabled, the tool
|
|
# executor falls back to Timmy's native (simulated) execution.
|
|
openfang_enabled: bool = False
|
|
# Timeout in seconds for OpenFang hand execution (some hands are slow).
|
|
openfang_timeout: int = 120
|
|
|
|
# ── Autoresearch — autonomous ML experiment loops ──────────────────
|
|
# Integrates Karpathy's autoresearch pattern: agents modify training
|
|
# code, run time-boxed experiments, evaluate metrics, and iterate.
|
|
autoresearch_enabled: bool = False
|
|
autoresearch_workspace: str = "data/experiments"
|
|
autoresearch_time_budget: int = 300 # seconds per experiment run
|
|
autoresearch_max_iterations: int = 100
|
|
autoresearch_metric: str = "val_bpb" # metric to optimise (lower = better)
|
|
# M3 Max / Apple Silicon tuning (Issue #905).
|
|
# dataset: "tinystories" (default, lower-entropy, recommended for Mac) or "openwebtext".
|
|
autoresearch_dataset: str = "tinystories"
|
|
# backend: "auto" detects MLX on Apple Silicon; "cpu" forces CPU fallback.
|
|
autoresearch_backend: str = "auto"
|
|
|
|
# ── Weekly Narrative Summary ───────────────────────────────────────
|
|
# Generates a human-readable weekly summary of development activity.
|
|
# Disabling this will stop the weekly narrative generation.
|
|
weekly_narrative_enabled: bool = True
|
|
weekly_narrative_lookback_days: int = 7
|
|
weekly_narrative_output_dir: str = ".loop"
|
|
|
|
# ── Local Hands (Shell + Git) ──────────────────────────────────────
|
|
# Enable local shell/git execution hands.
|
|
hands_shell_enabled: bool = True
|
|
# Default timeout in seconds for shell commands.
|
|
hands_shell_timeout: int = 60
|
|
# Comma-separated additional command prefixes to allow.
|
|
hands_shell_extra_allowed: str = ""
|
|
# Enable the git hand for version-control operations.
|
|
hands_git_enabled: bool = True
|
|
# Default timeout for git operations.
|
|
hands_git_timeout: int = 60
|
|
|
|
# ── Hermes Health Monitor ─────────────────────────────────────────
|
|
# Enable the Hermes system health monitor (memory, disk, Ollama, processes, network).
|
|
hermes_enabled: bool = True
|
|
# How often Hermes runs a full health cycle (seconds). Default: 5 minutes.
|
|
hermes_interval_seconds: int = 300
|
|
# Alert threshold: free memory below this triggers model unloading / alert (GB).
|
|
hermes_memory_free_min_gb: float = 4.0
|
|
# Alert threshold: free disk below this triggers cleanup / alert (GB).
|
|
hermes_disk_free_min_gb: float = 10.0
|
|
|
|
# ── Energy Budget Monitoring ───────────────────────────────────────
|
|
# Enable energy budget monitoring (tracks CPU/GPU power during inference).
|
|
energy_budget_enabled: bool = True
|
|
# Watts threshold that auto-activates low power mode (on-battery only).
|
|
energy_budget_watts_threshold: float = 15.0
|
|
# Model to prefer in low power mode (smaller = more efficient).
|
|
energy_low_power_model: str = "qwen3:1b"
|
|
|
|
# ── Error Logging ─────────────────────────────────────────────────
|
|
error_log_enabled: bool = True
|
|
error_log_dir: str = "logs"
|
|
error_log_max_bytes: int = 5_242_880 # 5 MB
|
|
error_log_backup_count: int = 5
|
|
error_feedback_enabled: bool = True # Auto-create bug report tasks
|
|
error_dedup_window_seconds: int = 300 # 5-min dedup window
|
|
|
|
# ── Bannerlord / GABS ────────────────────────────────────────────
|
|
# GABS (Game Action Bridge Server) TCP JSON-RPC endpoint.
|
|
# The GABS mod runs inside the Windows VM and exposes a JSON-RPC server
|
|
# on port 4825 that Timmy uses to read and act on Bannerlord game state.
|
|
# Set GABS_HOST to the VM's LAN IP (e.g. "10.0.0.50") to enable.
|
|
gabs_enabled: bool = False
|
|
gabs_host: str = "127.0.0.1"
|
|
gabs_port: int = 4825
|
|
gabs_timeout: float = 5.0 # socket timeout in seconds
|
|
# How often (seconds) the observer polls GABS for fresh game state.
|
|
gabs_poll_interval: int = 60
|
|
# Path to the Bannerlord journal inside the memory vault.
|
|
# Relative to repo root. Written by the GABS observer loop.
|
|
gabs_journal_path: str = "memory/bannerlord/journal.md"
|
|
|
|
# ── Scripture / Biblical Integration ──────────────────────────────
|
|
# Enable the biblical text module.
|
|
scripture_enabled: bool = True
|
|
# Primary translation for retrieval and citation.
|
|
scripture_translation: str = "ESV"
|
|
# Meditation mode: sequential | thematic | lectionary
|
|
scripture_meditation_mode: str = "sequential"
|
|
# Background meditation interval in seconds (0 = disabled).
|
|
scripture_meditation_interval: int = 0
|
|
|
|
def _compute_repo_root(self) -> str:
|
|
"""Auto-detect repo root if not set."""
|
|
if self.repo_root:
|
|
return self.repo_root
|
|
# Walk up from this file to find .git
|
|
import os
|
|
|
|
path = os.path.dirname(os.path.abspath(__file__))
|
|
path = os.path.dirname(os.path.dirname(path)) # src/ -> project root
|
|
while path != os.path.dirname(path):
|
|
if os.path.exists(os.path.join(path, ".git")):
|
|
return path
|
|
path = os.path.dirname(path)
|
|
return os.getcwd()
|
|
|
|
def model_post_init(self, __context) -> None:
|
|
"""Post-init: resolve gitea_token from file if not set via env."""
|
|
if not self.gitea_token:
|
|
# Priority: Timmy's own token → legacy admin token
|
|
repo_root = self._compute_repo_root()
|
|
timmy_token_path = os.path.join(repo_root, ".timmy_gitea_token")
|
|
legacy_token_path = os.path.expanduser("~/.config/gitea/token")
|
|
for token_path in (timmy_token_path, legacy_token_path):
|
|
try:
|
|
if os.path.isfile(token_path):
|
|
token = open(token_path).read().strip() # noqa: SIM115
|
|
if token:
|
|
self.gitea_token = token
|
|
break
|
|
except OSError:
|
|
pass
|
|
|
|
model_config = SettingsConfigDict(
|
|
env_file=".env",
|
|
env_file_encoding="utf-8",
|
|
extra="ignore",
|
|
)
|
|
|
|
|
|
settings = Settings()
|
|
# Ensure repo_root is computed if not set
|
|
if not settings.repo_root:
|
|
settings.repo_root = settings._compute_repo_root()
|
|
|
|
# ── Model fallback configuration ────────────────────────────────────────────
|
|
# Fallback chains are now in settings.fallback_models / settings.vision_fallback_models.
|
|
# Override via env vars (FALLBACK_MODELS, VISION_FALLBACK_MODELS) or
|
|
# edit config/providers.yaml → fallback_chains.
|
|
|
|
|
|
def check_ollama_model_available(model_name: str) -> bool:
|
|
"""Check if a specific Ollama model is available locally."""
|
|
try:
|
|
import json
|
|
import urllib.request
|
|
|
|
url = settings.normalized_ollama_url
|
|
req = urllib.request.Request(
|
|
f"{url}/api/tags",
|
|
method="GET",
|
|
headers={"Accept": "application/json"},
|
|
)
|
|
with urllib.request.urlopen(req, timeout=5) as response:
|
|
data = json.loads(response.read().decode())
|
|
models = [m.get("name", "") for m in data.get("models", [])]
|
|
return any(
|
|
model_name == m or model_name == m.split(":")[0] or m.startswith(model_name)
|
|
for m in models
|
|
)
|
|
except (OSError, ValueError) as exc:
|
|
_startup_logger.debug("Ollama model check failed: %s", exc)
|
|
return False
|
|
|
|
|
|
def get_effective_ollama_model() -> str:
|
|
"""Get the effective Ollama model, with fallback logic.
|
|
|
|
Walks the configurable ``settings.fallback_models`` chain when the
|
|
user's preferred model is not available locally.
|
|
"""
|
|
user_model = settings.ollama_model
|
|
|
|
if check_ollama_model_available(user_model):
|
|
return user_model
|
|
|
|
# Walk the configurable fallback chain
|
|
for fallback in settings.fallback_models:
|
|
if check_ollama_model_available(fallback):
|
|
_startup_logger.warning(
|
|
"Requested model '%s' not available. Using fallback: %s",
|
|
user_model,
|
|
fallback,
|
|
)
|
|
return fallback
|
|
|
|
# Last resort - return user's setting and hope for the best
|
|
return user_model
|
|
|
|
|
|
# ── Startup validation ───────────────────────────────────────────────────────
|
|
_startup_logger = _logging.getLogger("config")
|
|
_startup_validated = False
|
|
|
|
|
|
def validate_startup(*, force: bool = False) -> None:
|
|
"""Enforce security requirements — call from app entry points, not import.
|
|
|
|
Skipped in test mode (TIMMY_TEST_MODE=1) unless force=True.
|
|
In production: sys.exit(1) if required secrets are missing.
|
|
In development: log warnings only.
|
|
"""
|
|
global _startup_validated
|
|
if _startup_validated and not force:
|
|
return
|
|
|
|
if os.environ.get("TIMMY_TEST_MODE") == "1" and not force:
|
|
_startup_validated = True
|
|
return
|
|
|
|
if settings.timmy_env == "production":
|
|
_missing = []
|
|
if not settings.l402_hmac_secret:
|
|
_missing.append("L402_HMAC_SECRET")
|
|
if not settings.l402_macaroon_secret:
|
|
_missing.append("L402_MACAROON_SECRET")
|
|
if _missing:
|
|
_startup_logger.error(
|
|
"PRODUCTION SECURITY ERROR: The following secrets must be set: %s\n"
|
|
'Generate with: python3 -c "import secrets; print(secrets.token_hex(32))"\n'
|
|
"Set in .env file or environment variables.",
|
|
", ".join(_missing),
|
|
)
|
|
sys.exit(1)
|
|
if "*" in settings.cors_origins:
|
|
_startup_logger.error(
|
|
"PRODUCTION SECURITY ERROR: CORS wildcard '*' is not allowed "
|
|
"in production. Set CORS_ORIGINS to explicit origins."
|
|
)
|
|
sys.exit(1)
|
|
_startup_logger.info("Production mode: security secrets validated ✓")
|
|
else:
|
|
if "*" in settings.cors_origins:
|
|
_startup_logger.warning(
|
|
"SEC: CORS_ORIGINS contains wildcard '*' — "
|
|
"restrict to explicit origins before deploying to production."
|
|
)
|
|
if not settings.l402_hmac_secret:
|
|
_startup_logger.warning(
|
|
"SEC: L402_HMAC_SECRET is not set — "
|
|
"set a unique secret in .env before deploying to production."
|
|
)
|
|
if not settings.l402_macaroon_secret:
|
|
_startup_logger.warning(
|
|
"SEC: L402_MACAROON_SECRET is not set — "
|
|
"set a unique secret in .env before deploying to production."
|
|
)
|
|
|
|
_startup_validated = True
|