8fec9c41a5
Adds SelfModifyLoop — an edit→validate→test→commit cycle that can read its own failure reports, diagnose root causes, and restart autonomously. Key capabilities: - Multi-backend LLM: Anthropic Claude API, Ollama, or auto-detect - Syntax validation via compile() before writing to disk - Autonomous self-correction loop with configurable max cycles - XML-based output format to avoid triple-quote delimiter conflicts - Branch creation skipped by default to prevent container restarts - CLI: self-modify run "instruction" --backend auto --autonomous - 939 tests passing, 30 skipped Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
131 lines
6.2 KiB
Python
131 lines
6.2 KiB
Python
from typing import Literal
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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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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# LLM model passed to Agno/Ollama — override with OLLAMA_MODEL
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ollama_model: str = "llama3.2"
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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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# ── AirLLM / backend selection ───────────────────────────────────────────
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# "ollama" — always use Ollama (default, safe everywhere)
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# "airllm" — always use AirLLM (requires pip install ".[bigbrain]")
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# "auto" — use AirLLM on Apple Silicon if airllm is installed,
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# fall back to Ollama otherwise
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timmy_model_backend: Literal["ollama", "airllm", "auto"] = "ollama"
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# AirLLM model size when backend is airllm or auto.
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# Larger = smarter, but needs more RAM / disk.
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# 8b ~16 GB | 70b ~140 GB | 405b ~810 GB
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airllm_model_size: Literal["8b", "70b", "405b"] = "70b"
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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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# ── 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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# 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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# ── Self-Modification ──────────────────────────────────────────────
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# Enable self-modification capabilities. When enabled, Timmy 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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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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settings = Settings()
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# ── Startup validation ───────────────────────────────────────────────────────
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# Enforce security requirements — fail fast in production.
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import logging as _logging
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import sys
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_startup_logger = _logging.getLogger("config")
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# Production mode: require secrets to be set
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if settings.timmy_env == "production":
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_missing = []
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if not settings.l402_hmac_secret:
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_missing.append("L402_HMAC_SECRET")
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if not settings.l402_macaroon_secret:
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_missing.append("L402_MACAROON_SECRET")
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if _missing:
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_startup_logger.error(
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"PRODUCTION SECURITY ERROR: The following secrets must be set: %s\n"
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"Generate with: python3 -c \"import secrets; print(secrets.token_hex(32))\"\n"
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"Set in .env file or environment variables.",
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", ".join(_missing),
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)
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sys.exit(1)
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_startup_logger.info("Production mode: security secrets validated ✓")
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else:
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# Development mode: warn but continue
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if not settings.l402_hmac_secret:
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_startup_logger.warning(
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"SEC: L402_HMAC_SECRET is not set — "
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"set a unique secret in .env before deploying to production."
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)
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if not settings.l402_macaroon_secret:
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_startup_logger.warning(
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"SEC: L402_MACAROON_SECRET is not set — "
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"set a unique secret in .env before deploying to production."
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)
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