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ai.yaml is the source of truth (opt-in: enabled=False by default) with active model, idle-unload minutes, context override, and tool-call bound. The model catalog follows the map pattern: a bundled constant keeps the Manage tab working offline, the CDN manifest (ai/models.json + detached GPG signature) wins when reachable. GGUF downloads clone the map-extract flow — the per-file meta4 is GPG-verified once and the same bytes handed to aria2c, so the verified hash is the enforced one; unverified downloads are refused. Models land in <media>/ai/models/ (gated by the existing media_mounted download rules). Manage endpoints (catalog with RAM-tier recommendation, settings) live under /api/ai/manage/ which the future agent loop excludes from its tool catalog. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
"""The AI model catalog.
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The bundled AI_MODELS constant is the offline fallback (like the map module's MAP_REGIONS);
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the CDN manifest at ai/models.json is fetched at browse/download time and GPG-verified with the
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committed WROLPi public key. The release process refreshes AI_MODELS from the published manifest.
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"""
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import json
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import tempfile
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from pathlib import Path
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import psutil
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from wrolpi.common import aiohttp_get, get_media_directory, logger, verify_gpg_signature
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logger = logger.getChild(__name__)
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CDN = 'https://wrolpi.nyc3.cdn.digitaloceanspaces.com'
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MODELS_MANIFEST_URL = f'{CDN}/ai/models.json'
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# Sizes are bytes; sha256 values are filled from the published manifest at release time. The
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# meta4 sidecar (GPG-verified at download time) is what actually enforces the hash.
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AI_MODELS = [
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dict(
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name='Qwen3-1.7B-Q4_K_M.gguf',
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tier='small',
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url=f'{CDN}/ai/Qwen3-1.7B-Q4_K_M.gguf',
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sha256='',
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size=1_200_000_000,
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min_ram_gb=4,
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default_context=8_192,
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license='Apache 2.0',
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description='The default assistant for 4GB devices (Pi 4/5). Best tool-calling per byte.',
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),
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dict(
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name='Qwen3-4B-Instruct-2507-Q4_K_M.gguf',
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tier='medium',
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url=f'{CDN}/ai/Qwen3-4B-Instruct-2507-Q4_K_M.gguf',
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sha256='',
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size=2_600_000_000,
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min_ram_gb=8,
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default_context=16_384,
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license='Apache 2.0',
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description='The default assistant for 8GB+ devices. A standout small tool-caller.',
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),
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]
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def get_models_directory() -> Path:
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"""Where GGUF model files are stored. Always inside the media directory."""
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return get_media_directory() / 'ai/models'
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async def fetch_models_manifest(url: str = None) -> dict:
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"""Fetch the models manifest from the CDN and verify its GPG signature (map-manifest pattern)."""
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url = url or MODELS_MANIFEST_URL
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sig_url = f'{url}.sig'
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async with aiohttp_get(url, timeout=30) as response:
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if response.status != 200:
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raise RuntimeError(f'Failed to fetch models manifest: HTTP {response.status}')
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manifest_bytes = await response.content.read()
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async with aiohttp_get(sig_url, timeout=30) as response:
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if response.status != 200:
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raise RuntimeError(f'Failed to fetch models manifest signature: HTTP {response.status}')
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signature_bytes = await response.content.read()
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with tempfile.TemporaryDirectory() as tmpdir:
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manifest_path = Path(tmpdir) / 'models.json'
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sig_path = Path(tmpdir) / 'models.json.sig'
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manifest_path.write_bytes(manifest_bytes)
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sig_path.write_bytes(signature_bytes)
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if not await verify_gpg_signature(manifest_path, sig_path):
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raise RuntimeError('Models manifest GPG signature verification failed')
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logger.info('Models manifest GPG signature verified successfully')
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return json.loads(manifest_bytes)
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async def get_models_catalog() -> (list, str):
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"""The list of downloadable models, and where it came from ('cdn' or 'bundled').
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The CDN manifest wins when reachable; the bundled constant keeps the Manage tab working
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offline."""
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try:
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manifest = await fetch_models_manifest()
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models = manifest.get('models') or []
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if models:
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return models, 'cdn'
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except Exception as e:
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logger.debug(f'Could not fetch models manifest, using bundled catalog: {e}')
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return AI_MODELS, 'bundled'
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def get_total_ram_bytes() -> int:
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return psutil.virtual_memory().total
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def recommend_tier(total_ram_bytes: int = None) -> str:
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"""Recommend a model tier for this device's RAM. Other tiers stay selectable with a warning."""
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total_ram_bytes = total_ram_bytes if total_ram_bytes is not None else get_total_ram_bytes()
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gb = total_ram_bytes / 1024 ** 3
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if gb < 6:
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return 'small'
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if gb < 24:
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return 'medium'
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return 'large'
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