forked from Rockachopa/Timmy-time-dashboard
28d1905df4
Adds vLLM (high-throughput OpenAI-compatible inference server) as a selectable backend alongside the existing Ollama and vllm-mlx backends. vLLM's continuous batching gives 3-10x throughput for agentic workloads. Changes: - config.py: add `vllm` to timmy_model_backend Literal; add vllm_url / vllm_model settings (VLLM_URL / VLLM_MODEL env vars) - cascade.py: add vllm provider type with _check_provider_available (hits /health) and _call_vllm (OpenAI-compatible completions) - providers.yaml: add disabled-by-default vllm-local provider (priority 3, port 8001); bump OpenAI/Anthropic backup priorities to 4/5 - health.py: add _check_vllm/_check_vllm_sync with 30-second TTL cache; /health and /health/sovereignty reflect vLLM status when it is the active backend - docker-compose.yml: add vllm service behind 'vllm' profile (GPU passthrough commented-out template included); add vllm-cache volume - CLAUDE.md: add vLLM row to Service Fallback Matrix - tests: 26 new unit tests covering availability checks, _call_vllm, providers.yaml validation, config options, and health helpers Graceful fallback: if vLLM is unavailable the cascade router automatically falls back to Ollama. The app never crashes. Fixes #1281 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
634 lines
32 KiB
Python
634 lines
32 KiB
Python
import logging as _logging
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import os
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import sys
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from datetime import UTC
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from datetime import datetime as _datetime
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from typing import Literal
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from pydantic_settings import BaseSettings, SettingsConfigDict
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APP_START_TIME: _datetime = _datetime.now(UTC)
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def normalize_ollama_url(url: str) -> str:
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"""Replace localhost with 127.0.0.1 to avoid IPv6 resolution delays."""
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return url.replace("localhost", "127.0.0.1")
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class Settings(BaseSettings):
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"""Central configuration — all env-var access goes through this class."""
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# Display name for the primary agent — override with AGENT_NAME env var
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agent_name: str = "Agent"
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# Ollama host — override with OLLAMA_URL env var or .env file
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ollama_url: str = "http://localhost:11434"
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@property
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def normalized_ollama_url(self) -> str:
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"""Return ollama_url with localhost replaced by 127.0.0.1."""
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return normalize_ollama_url(self.ollama_url)
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# LLM model passed to Agno/Ollama — override with OLLAMA_MODEL
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# qwen3:14b (Q5_K_M) is the primary model: tool calling F1 0.971, ~17.5 GB
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# at 32K context — optimal for M3 Max 36 GB (Issue #1063).
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# qwen3:30b exceeded memory budget at 32K+ context on 36 GB hardware.
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ollama_model: str = "qwen3:14b"
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# Fast routing model — override with OLLAMA_FAST_MODEL
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# qwen3:8b (Q6_K): tool calling F1 0.933 at ~45-55 tok/s (2x speed of 14B).
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# Use for routine tasks: simple tool calls, file reads, status checks.
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# Combined memory with qwen3:14b: ~17 GB — both can stay loaded simultaneously.
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ollama_fast_model: str = "qwen3:8b"
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# Maximum concurrently loaded Ollama models — override with OLLAMA_MAX_LOADED_MODELS
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# Set to 2 to keep qwen3:8b (fast) + qwen3:14b (primary) both hot.
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# Requires setting OLLAMA_MAX_LOADED_MODELS=2 in the Ollama server environment.
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ollama_max_loaded_models: int = 2
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# Context window size for Ollama inference — override with OLLAMA_NUM_CTX
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# qwen3:14b at 32K: ~17.5 GB total (weights + KV cache) on M3 Max 36 GB.
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# Set to 0 to use model defaults.
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ollama_num_ctx: int = 32768
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# Maximum models loaded simultaneously in Ollama — override with OLLAMA_MAX_LOADED_MODELS
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# Set to 2 so Qwen3-8B and Qwen3-14B can stay hot concurrently (~17 GB combined).
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# Requires Ollama ≥ 0.1.33. Export this to the Ollama process environment:
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# OLLAMA_MAX_LOADED_MODELS=2 ollama serve
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# or add it to your systemd/launchd unit before starting the harness.
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ollama_max_loaded_models: int = 2
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# Fallback model chains — override with FALLBACK_MODELS / VISION_FALLBACK_MODELS
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# as comma-separated strings, e.g. FALLBACK_MODELS="qwen3:8b,qwen2.5:14b"
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# Or edit config/providers.yaml → fallback_chains for the canonical source.
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fallback_models: list[str] = [
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"qwen3:8b",
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"qwen2.5:14b",
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"qwen2.5:7b",
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"llama3.1:8b-instruct",
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"llama3.1",
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"llama3.2:3b",
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]
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vision_fallback_models: list[str] = [
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"llama3.2:3b",
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"llava:7b",
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"qwen2.5-vl:3b",
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"moondream:1.8b",
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]
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# Set DEBUG=true to enable /docs and /redoc (disabled by default)
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debug: bool = False
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# Telegram bot token — set via TELEGRAM_TOKEN env var or the /telegram/setup endpoint
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telegram_token: str = ""
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# Discord bot token — set via DISCORD_TOKEN env var or the /discord/setup endpoint
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discord_token: str = ""
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# ── Discord action confirmation ──────────────────────────────────────────
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# When True, dangerous tools (shell, write_file, python) require user
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# confirmation via Discord button before executing.
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discord_confirm_actions: bool = True
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# Seconds to wait for user confirmation before auto-rejecting.
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discord_confirm_timeout: int = 120
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# ── Backend selection ────────────────────────────────────────────────────
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# "ollama" — always use Ollama (default, safe everywhere)
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# "vllm" — use vLLM inference server (OpenAI-compatible, faster throughput)
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# "auto" — pick best available local backend, fall back to Ollama
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timmy_model_backend: Literal["ollama", "vllm", "grok", "claude", "auto"] = "ollama"
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# ── vLLM backend ──────────────────────────────────────────────────────────
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# vLLM is an OpenAI-compatible inference server optimised for continuous
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# batching — 3–10x higher throughput than Ollama for agentic workloads.
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# Start server: python -m vllm.entrypoints.openai.api_server \
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# --model Qwen/Qwen2.5-14B-Instruct --port 8001
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# Then set TIMMY_LLM_BACKEND=vllm (or enable vllm-local in providers.yaml)
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vllm_url: str = "http://localhost:8001"
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vllm_model: str = "Qwen/Qwen2.5-14B-Instruct"
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# ── Grok (xAI) — opt-in premium cloud backend ────────────────────────
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# Grok is a premium augmentation layer — local-first ethos preserved.
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# Only used when explicitly enabled and query complexity warrants it.
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grok_enabled: bool = False
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xai_api_key: str = ""
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xai_base_url: str = "https://api.x.ai/v1"
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grok_default_model: str = "grok-3-fast"
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grok_max_sats_per_query: int = 200
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grok_sats_hard_cap: int = 100 # Absolute ceiling on sats per Grok query
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grok_free: bool = False # Skip Lightning invoice when user has own API key
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# ── Database ──────────────────────────────────────────────────────────
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db_busy_timeout_ms: int = 5000 # SQLite PRAGMA busy_timeout (ms)
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# ── Claude (Anthropic) — cloud fallback backend ────────────────────────
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# Used when Ollama is offline and local inference isn't available.
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# Set ANTHROPIC_API_KEY to enable. Default model is Haiku (fast + cheap).
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anthropic_api_key: str = ""
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claude_model: str = "haiku"
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# ── Content Moderation ──────────────────────────────────────────────
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# Three-layer moderation pipeline for AI narrator output.
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# Uses Llama Guard via Ollama with regex fallback.
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moderation_enabled: bool = True
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moderation_guard_model: str = "llama-guard3:1b"
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# Default confidence threshold — per-game profiles can override.
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moderation_threshold: float = 0.8
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# ── Spark Intelligence ────────────────────────────────────────────────
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# Enable/disable the Spark cognitive layer.
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# When enabled, Spark captures swarm events, runs EIDOS predictions,
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# consolidates memories, and generates advisory recommendations.
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spark_enabled: bool = True
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# ── Git / DevOps ──────────────────────────────────────────────────────
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git_default_repo_dir: str = "~/repos"
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# Repository root - auto-detected but can be overridden
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# This is the main project directory where .git lives
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repo_root: str = ""
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# ── Creative — Image Generation (Pixel) ───────────────────────────────
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flux_model_id: str = "black-forest-labs/FLUX.1-schnell"
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image_output_dir: str = "data/images"
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image_default_steps: int = 4
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# ── Creative — Music Generation (Lyra) ────────────────────────────────
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music_output_dir: str = "data/music"
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ace_step_model: str = "ace-step/ACE-Step-v1.5"
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# ── Creative — Video Generation (Reel) ────────────────────────────────
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video_output_dir: str = "data/video"
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wan_model_id: str = "Wan-AI/Wan2.1-T2V-1.3B"
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video_default_resolution: str = "480p"
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# ── Creative — Pipeline / Assembly ────────────────────────────────────
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creative_output_dir: str = "data/creative"
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video_transition_duration: float = 1.0
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default_video_codec: str = "libx264"
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# ── L402 Lightning ───────────────────────────────────────────────────
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# HMAC secrets for macaroon signing and invoice verification.
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# Generate with: python3 -c "import secrets; print(secrets.token_hex(32))"
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# In production (TIMMY_ENV=production), these MUST be set or the app will refuse to start.
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l402_hmac_secret: str = ""
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l402_macaroon_secret: str = ""
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lightning_backend: Literal["mock", "lnd"] = "mock"
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# ── Privacy / Sovereignty ────────────────────────────────────────────
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# Disable Agno telemetry for air-gapped/sovereign deployments.
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# Default is False (telemetry disabled) to align with sovereign AI vision.
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telemetry_enabled: bool = False
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# ── Sovereignty Metrics ──────────────────────────────────────────────
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# Alert when API cost per research task exceeds this threshold (USD).
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sovereignty_api_cost_alert_threshold: float = 1.00
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# CORS allowed origins for the web chat interface (Gitea Pages, etc.)
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# Set CORS_ORIGINS as a comma-separated list, e.g. "http://localhost:3000,https://example.com"
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cors_origins: list[str] = [
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"http://localhost:3000",
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"http://localhost:8000",
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"http://127.0.0.1:3000",
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"http://127.0.0.1:8000",
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]
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# ── Matrix Frontend Integration ────────────────────────────────────────
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# URL of the Matrix frontend (Replit/Tailscale) for CORS.
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# When set, this origin is added to CORS allowed_origins.
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# Example: "http://100.124.176.28:8080" or "https://alexanderwhitestone.com"
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matrix_frontend_url: str = "" # Empty = disabled
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# WebSocket authentication token for Matrix connections.
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# When set, clients must provide this token via ?token= query param
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# or in the first message as {"type": "auth", "token": "..."}.
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# Empty/unset = auth disabled (dev mode).
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matrix_ws_token: str = ""
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# Trusted hosts for the Host header check (TrustedHostMiddleware).
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# Set TRUSTED_HOSTS as a comma-separated list. Wildcards supported (e.g. "*.ts.net").
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# Defaults include localhost + Tailscale MagicDNS. Add your Tailscale IP if needed.
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trusted_hosts: list[str] = [
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"localhost",
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"127.0.0.1",
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"*.local",
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"*.ts.net",
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"testserver",
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]
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# Environment mode: development | production
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# In production, security settings are strictly enforced.
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timmy_env: Literal["development", "production"] = "development"
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# ── Memory Management ──────────────────────────────────────────────
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# Auto-prune vector store memories older than this many days on startup.
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# Set to 0 to disable auto-pruning.
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memory_prune_days: int = 90
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# When True, fact-type memories are kept even when older than the TTL.
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memory_prune_keep_facts: bool = True
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# Maximum size in MB for the memory/notes/ vault directory.
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# When exceeded, a warning is logged. Set to 0 to disable.
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memory_vault_max_mb: int = 100
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# Auto-prune thoughts older than this many days. 0 = disabled.
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thoughts_prune_days: int = 90
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# Minimum thoughts to keep regardless of age.
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thoughts_prune_keep_min: int = 200
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# Auto-prune system events older than this many days. 0 = disabled.
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events_prune_days: int = 90
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# Minimum events to keep regardless of age.
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events_prune_keep_min: int = 200
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# ── Agentic Loop ──────────────────────────────────────────────────
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# Maximum steps the agentic loop will execute before stopping.
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max_agent_steps: int = 10
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# ── Test / Diagnostics ─────────────────────────────────────────────
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# Skip loading heavy embedding models (for tests / low-memory envs).
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timmy_skip_embeddings: bool = False
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# Embedding backend: "ollama" for Ollama, "local" for sentence-transformers.
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timmy_embedding_backend: Literal["ollama", "local"] = "local"
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# Ollama model to use for embeddings (e.g., "nomic-embed-text").
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ollama_embedding_model: str = "nomic-embed-text"
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# Disable CSRF middleware entirely (for tests).
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timmy_disable_csrf: bool = False
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# Mark the process as running in test mode.
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timmy_test_mode: bool = False
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# ── Brain / rqlite ─────────────────────────────────────────────────
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# URL of the local rqlite node for distributed memory.
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# Empty string means rqlite is not configured.
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rqlite_url: str = ""
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# Source identifier for brain memory entries.
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brain_source: str = "default"
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# Path override for the local brain SQLite database.
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brain_db_path: str = ""
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# ── Security Tuning ───────────────────────────────────────────────
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# Set to True in production to mark CSRF cookies as Secure (HTTPS only).
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csrf_cookie_secure: bool = False
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# Maximum size in bytes for chat API request bodies.
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chat_api_max_body_bytes: int = 1_048_576 # 1 MB
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# ── Self-Modification ──────────────────────────────────────────────
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# Enable self-modification capabilities. When enabled, the agent can
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# edit its own source code, run tests, and commit changes.
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self_modify_enabled: bool = False
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self_modify_max_retries: int = 2
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self_modify_allowed_dirs: str = "src,tests"
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self_modify_backend: str = "auto" # "ollama", "anthropic", or "auto"
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# ── Work Orders ──────────────────────────────────────────────────
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# External users and agents can submit work orders for improvements.
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work_orders_enabled: bool = True
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work_orders_auto_execute: bool = False # Master switch for auto-execution
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work_orders_auto_threshold: str = (
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"low" # Max priority that auto-executes: "low" | "medium" | "high" | "none"
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)
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# ── Custom Weights & Models ──────────────────────────────────────
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# Directory for custom model weights (GGUF, safetensors, HF checkpoints).
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# Models placed here can be registered at runtime and assigned to agents.
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custom_weights_dir: str = "data/models"
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# Enable the reward model for scoring agent outputs (PRM-style).
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reward_model_enabled: bool = False
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# Reward model name (must be available via Ollama or a custom weight path).
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reward_model_name: str = ""
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# Minimum votes for majority-vote reward scoring (odd number recommended).
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reward_model_votes: int = 3
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# ── Browser Local Models (iPhone / WebGPU) ───────────────────────
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# Enable in-browser LLM inference via WebLLM for offline iPhone use.
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# When enabled, the mobile dashboard loads a small model directly
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# in the browser — no server or Ollama required.
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browser_model_enabled: bool = True
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# WebLLM model ID — must be a pre-compiled MLC model.
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# Recommended for iPhone: SmolLM2-360M (fast) or Qwen3-0.6B (smart).
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browser_model_id: str = "SmolLM2-360M-Instruct-q4f16_1-MLC"
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# Fallback to server when browser model is unavailable or too slow.
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browser_model_fallback: bool = True
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# ── Deep Focus Mode ─────────────────────────────────────────────
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# "deep" = single-problem context; "broad" = default multi-task.
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focus_mode: Literal["deep", "broad"] = "broad"
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# ── Default Thinking ──────────────────────────────────────────────
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# When enabled, the agent starts an internal thought loop on server start.
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thinking_enabled: bool = True
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thinking_interval_seconds: int = 300 # 5 minutes between thoughts
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thinking_timeout_seconds: int = 120 # max wall-clock time per thinking cycle
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thinking_distill_every: int = 10 # distill facts from thoughts every Nth thought
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thinking_issue_every: int = 20 # file Gitea issues from thoughts every Nth thought
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thinking_memory_check_every: int = 50 # check memory status every Nth thought
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thinking_idle_timeout_minutes: int = 60 # pause thoughts after N minutes without user input
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# ── Gitea Integration ─────────────────────────────────────────────
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# Local Gitea instance for issue tracking and self-improvement.
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# These values are passed as env vars to the gitea-mcp server process.
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gitea_url: str = "http://localhost:3000"
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gitea_token: str = "" # GITEA_TOKEN env var; falls back to .timmy_gitea_token
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gitea_repo: str = "rockachopa/Timmy-time-dashboard" # owner/repo
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gitea_enabled: bool = True
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# ── MCP Servers ────────────────────────────────────────────────────
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# External tool servers connected via Model Context Protocol (stdio).
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mcp_gitea_command: str = "gitea-mcp-server -t stdio"
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mcp_filesystem_command: str = "npx -y @modelcontextprotocol/server-filesystem"
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mcp_timeout: int = 15
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mcp_bridge_timeout: int = 60 # HTTP timeout for MCP bridge Ollama calls (seconds)
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# ── Backlog Triage Loop ────────────────────────────────────────────
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# Autonomous loop: fetch open issues, score, assign to agents.
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backlog_triage_enabled: bool = False
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# Seconds between triage cycles (default: 15 minutes).
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backlog_triage_interval_seconds: int = 900
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# When True, score and summarize but don't write to Gitea.
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backlog_triage_dry_run: bool = False
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# Create a daily triage summary issue/comment.
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backlog_triage_daily_summary: bool = True
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# ── Loop QA (Self-Testing) ─────────────────────────────────────────
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# Self-test orchestrator that probes capabilities alongside the thinking loop.
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loop_qa_enabled: bool = True
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loop_qa_interval_ticks: int = 5 # run 1 self-test every Nth thinking tick (~25 min)
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loop_qa_upgrade_threshold: int = 3 # consecutive failures → file task
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loop_qa_max_per_hour: int = 12 # safety throttle
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# ── Vassal Protocol (Autonomous Orchestrator) ─────────────────────
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# Timmy as lead decision-maker: triage backlog, dispatch agents, monitor health.
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# See timmy/vassal/ for implementation.
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vassal_enabled: bool = False # off by default — enable when Qwen3-14B is loaded
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vassal_cycle_interval: int = 300 # seconds between orchestration cycles (5 min)
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vassal_max_dispatch_per_cycle: int = 10 # cap on new dispatches per cycle
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vassal_stuck_threshold_minutes: int = 120 # minutes before agent issue is "stuck"
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vassal_idle_threshold_minutes: int = 30 # minutes before agent is "idle"
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# ── Paperclip AI — orchestration bridge ────────────────────────────
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# URL where the Paperclip server listens.
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# For VPS deployment behind nginx, use the public domain.
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paperclip_url: str = "http://localhost:3100"
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# Enable/disable the Paperclip integration.
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paperclip_enabled: bool = False
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# API key or auth-gate cookie for authenticating with Paperclip.
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paperclip_api_key: str = ""
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# Timmy's agent ID in the Paperclip org chart.
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paperclip_agent_id: str = ""
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# Company ID in Paperclip — required for most API calls.
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paperclip_company_id: str = ""
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# Timeout in seconds for Paperclip HTTP calls.
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paperclip_timeout: int = 30
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||
# 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
|