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2f27ce49a7
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>
101 lines
2.7 KiB
Python
101 lines
2.7 KiB
Python
"""The ai.yaml config — the source of truth for the AI assistant's state.
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AI is opt-in: nothing is downloaded and llama-server does not run until the user enables it on
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the Manage tab. YAML-only (no database)."""
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import contextlib
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from dataclasses import dataclass
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from typing import Optional
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from wrolpi.common import ConfigFile
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@dataclass
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class AIConfigValidator:
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version: int = None
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enabled: bool = None
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active_model: str = None
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idle_unload_minutes: int = None
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context_size: Optional[int] = None
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max_tool_calls: int = None
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class AIConfig(ConfigFile):
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file_name = 'ai.yaml'
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default_config = dict(
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version=0,
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enabled=False,
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active_model='', # a GGUF file name in <media>/ai/models/
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idle_unload_minutes=15,
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context_size=None, # None uses the active model's default from the catalog.
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max_tool_calls=6,
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)
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validator = AIConfigValidator
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def import_config(self, file=None, send_events=False):
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super().import_config(file, send_events)
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# YAML-only config (no database); a successful read is a successful import.
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self.successful_import = True
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@property
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def enabled(self) -> bool:
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return self._config['enabled']
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@enabled.setter
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def enabled(self, value: bool):
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self.update({'enabled': bool(value)})
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@property
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def active_model(self) -> str:
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return self._config['active_model']
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@active_model.setter
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def active_model(self, value: str):
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self.update({'active_model': value or ''})
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@property
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def idle_unload_minutes(self) -> int:
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return self._config['idle_unload_minutes']
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@idle_unload_minutes.setter
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def idle_unload_minutes(self, value: int):
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self.update({'idle_unload_minutes': int(value)})
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@property
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def context_size(self) -> Optional[int]:
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return self._config['context_size']
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@context_size.setter
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def context_size(self, value: Optional[int]):
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self.update({'context_size': int(value) if value else None})
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@property
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def max_tool_calls(self) -> int:
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return self._config['max_tool_calls']
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@max_tool_calls.setter
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def max_tool_calls(self, value: int):
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self.update({'max_tool_calls': int(value)})
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AI_CONFIG: AIConfig = AIConfig()
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# Test override (see get_ai_config).
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TEST_AI_CONFIG: Optional[AIConfig] = None
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def get_ai_config() -> AIConfig:
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global TEST_AI_CONFIG
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if isinstance(TEST_AI_CONFIG, ConfigFile):
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return TEST_AI_CONFIG
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return AI_CONFIG
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@contextlib.contextmanager
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def ai_config_context():
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"""Used to create a test config."""
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global TEST_AI_CONFIG
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TEST_AI_CONFIG = AIConfig()
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try:
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yield TEST_AI_CONFIG
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finally:
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TEST_AI_CONFIG = None
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