Add Calibrate AI Control UI and fleet LLM backend wiring.
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Operators toggle Logic gates vs AI Control on Settings, refresh local Ollama models, and save ai_endpoint settings via Calibrate PUT; server scheduler and agent snapshot/command paths support stateless 60s fleet decisions.
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AetherForge
176
server/web/src/components/CalibrationAIControl.tsx
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176
server/web/src/components/CalibrationAIControl.tsx
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import { useState } from 'react';
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import type { ServerSettings } from '../types';
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import { api } from '../api/client';
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import { HelpTip, FieldHint } from './HelpTip';
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import { ADAPTIVE_STRATEGY_HELP } from '../help/lotlOnionTiers';
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export const DEFAULT_AI_LOCAL_ENDPOINT = 'http://127.0.0.1:11434/v1';
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export const DEFAULT_AI_INTERVAL_SEC = 60;
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function readEndpoint(server: ServerSettings): string {
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return server.ai_endpoint?.trim() || server.ai_local_endpoint?.trim() || DEFAULT_AI_LOCAL_ENDPOINT;
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}
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function readIntervalSec(server: ServerSettings): number {
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return server.ai_decision_interval_sec ?? server.ai_interval_sec ?? DEFAULT_AI_INTERVAL_SEC;
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}
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interface Props {
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server: ServerSettings;
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onUpdate: (path: string, value: unknown) => void;
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}
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export default function CalibrationAIControl({ server, onUpdate }: Props) {
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const aiControl = server.ai_control_enabled ?? false;
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const endpoint = readEndpoint(server);
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const model = server.ai_model?.trim() || '';
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const intervalSec = readIntervalSec(server);
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const [models, setModels] = useState<string[]>([]);
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const [refreshing, setRefreshing] = useState(false);
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const [modelsMsg, setModelsMsg] = useState('');
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const handleRefreshModels = async () => {
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setRefreshing(true);
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setModelsMsg('');
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try {
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const res = await api.getAIModels(endpoint);
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setModels(res.models ?? []);
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if (!res.models?.length) {
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setModelsMsg(res.error || 'No models returned — is Ollama running?');
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} else {
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setModelsMsg(`${res.models.length} model(s) loaded`);
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if (!model && res.models[0]) {
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onUpdate('server.ai_model', res.models[0]);
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}
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}
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} catch (e) {
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setModels([]);
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setModelsMsg(e instanceof Error ? e.message : 'Failed to refresh models');
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} finally {
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setRefreshing(false);
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}
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};
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return (
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<div className="calibration-ai-control">
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<div
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className="calibration-mode-toggle"
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role="group"
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aria-label="Calibration control mode"
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>
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<button
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type="button"
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className={`calibration-mode-btn ${!aiControl ? 'calibration-mode-btn--active' : ''}`}
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aria-pressed={!aiControl}
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onClick={() => onUpdate('server.ai_control_enabled', false)}
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>
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<span className="calibration-mode-label">Logic gates</span>
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<span className="calibration-mode-sub">Adaptive strategy & tier chains</span>
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</button>
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<button
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type="button"
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className={`calibration-mode-btn ${aiControl ? 'calibration-mode-btn--active' : ''}`}
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aria-pressed={aiControl}
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onClick={() => onUpdate('server.ai_control_enabled', true)}
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>
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<span className="calibration-mode-label">AI Control</span>
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<span className="calibration-mode-sub">Local LLM fleet decisions</span>
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</button>
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</div>
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{aiControl ? (
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<div className="calibration-ai-panel">
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<p className="section-desc calibration-ai-blurb">
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Fleet AI issues <strong>stateless</strong> decisions every {intervalSec}s per agent — no memory
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between cycles. The control server calls your local LLM; agents execute tool calls on{' '}
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<em>your</em> machines only. Complete fleet control stays on your LAN.
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</p>
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<div className="form-group">
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<label htmlFor="cal-ai-endpoint" className="label">
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Local API URL <HelpTip field="ai_local_endpoint" />
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</label>
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<input
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id="cal-ai-endpoint"
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type="url"
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className="input mono"
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value={endpoint}
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placeholder={DEFAULT_AI_LOCAL_ENDPOINT}
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onChange={(e) => onUpdate('server.ai_endpoint', e.target.value)}
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/>
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<FieldHint field="ai_local_endpoint" />
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</div>
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<div className="form-row calibration-ai-model-row">
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<button
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type="button"
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className="btn btn-outline btn-sm"
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disabled={refreshing}
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onClick={handleRefreshModels}
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>
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{refreshing ? 'Refreshing…' : 'Refresh models'}
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</button>
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<div className="form-group" style={{ flex: 1, margin: 0 }}>
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<label htmlFor="cal-ai-model" className="label">
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Model <HelpTip field="calibration_ai_model" />
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</label>
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<select
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id="cal-ai-model"
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className="input"
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value={model}
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onChange={(e) => onUpdate('server.ai_model', e.target.value)}
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>
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<option value="">{models.length ? 'Select a model…' : 'Refresh models first'}</option>
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{model && !models.includes(model) && (
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<option value={model}>{model}</option>
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)}
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{models.map((m) => (
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<option key={m} value={m}>{m}</option>
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))}
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</select>
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</div>
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</div>
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{modelsMsg && (
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<p className="form-hint calibration-ai-models-msg">{modelsMsg}</p>
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)}
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<div className="calibration-ai-meta">
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<div className="calibration-ai-info-chip">
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<span className="font-tech">ai_no_context</span>
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<span className="calibration-ai-info-value">always on</span>
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<HelpTip field="ai_no_context" />
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</div>
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<div className="calibration-ai-info-chip">
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<span className="font-tech">Interval</span>
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<span className="calibration-ai-info-value">{intervalSec}s per agent</span>
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<HelpTip field="ai_interval_sec" />
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</div>
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</div>
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</div>
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) : (
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<div className="calibration-logic-panel">
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<p className="section-desc">{ADAPTIVE_STRATEGY_HELP}</p>
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<FieldHint field="adaptive_strategy" />
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<FieldHint field="lotl_onion_tiers" />
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<div className="form-group checkbox-group" style={{ marginTop: '1rem' }}>
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<label className="checkbox-label">
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<input
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type="checkbox"
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className="checkbox"
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checked={server.adaptive_strategy_enabled !== false}
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onChange={(e) => onUpdate('server.adaptive_strategy_enabled', e.target.checked)}
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/>
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<span>Enable adaptive strategy engine <HelpTip field="adaptive_strategy" /></span>
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</label>
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</div>
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{server.lotl_onion_tiers?.length ? (
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<p className="form-hint" style={{ marginTop: '0.75rem' }}>
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Spread tier order: <code className="mono-sm">{server.lotl_onion_tiers.join(' → ')}</code>
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</p>
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) : null}
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</div>
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)}
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</div>
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);
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}
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