feat: Phase 2 - LLM Integration for Canvas
- Add canvas.ts MCP tool with validate_canvas_intent, execute_canvas_intent, interpret_canvas_intent - Add useCanvasChat.ts bridge hook connecting canvas to chat system - Update context_builder.py with canvas tool instructions - Add ExecuteDialog for study name input - Add ChatPanel for canvas-integrated Claude responses - Connect AtomizerCanvas to Claude via useCanvasChat Canvas workflow now: 1. Build graph visually 2. Click Validate/Analyze/Execute 3. Claude processes intent via MCP tools 4. Response shown in integrated chat panel Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
@@ -235,4 +235,12 @@ Available tools:
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- `get_trial_data`, `analyze_convergence`, `compare_trials`, `get_best_design`
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- `generate_report`, `export_data`
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- `explain_physics`, `recommend_method`, `query_extractors`
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**Canvas Tools (for visual workflow builder):**
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- `validate_canvas_intent` - Validate a canvas-generated optimization intent
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- `execute_canvas_intent` - Create a study from a canvas intent
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- `interpret_canvas_intent` - Analyze intent and provide recommendations
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When you receive a message containing "INTENT:" followed by JSON, this is from the Canvas UI.
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Parse the intent and use the appropriate canvas tool to process it.
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"""
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@@ -1,4 +1,4 @@
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import { useCallback, useRef, DragEvent } from 'react';
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import { useCallback, useRef, useState, DragEvent } from 'react';
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import ReactFlow, {
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Background,
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Controls,
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@@ -12,12 +12,17 @@ import { nodeTypes } from './nodes';
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import { NodePalette } from './palette/NodePalette';
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import { NodeConfigPanel } from './panels/NodeConfigPanel';
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import { ValidationPanel } from './panels/ValidationPanel';
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import { ExecuteDialog } from './panels/ExecuteDialog';
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import { useCanvasStore } from '../../hooks/useCanvasStore';
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import { useCanvasChat } from '../../hooks/useCanvasChat';
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import { NodeType } from '../../lib/canvas/schema';
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import { ChatPanel } from './panels/ChatPanel';
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function CanvasFlow() {
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const reactFlowWrapper = useRef<HTMLDivElement>(null);
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const reactFlowInstance = useRef<ReactFlowInstance | null>(null);
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const [showExecuteDialog, setShowExecuteDialog] = useState(false);
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const [showChat, setShowChat] = useState(false);
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const {
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nodes,
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@@ -33,6 +38,18 @@ function CanvasFlow() {
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toIntent,
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} = useCanvasStore();
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const {
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messages,
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isThinking,
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isExecuting,
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isConnected,
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executeIntent,
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validateIntent,
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analyzeIntent,
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} = useCanvasChat({
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onError: (error) => console.error('Canvas chat error:', error),
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});
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const onDragOver = useCallback((event: DragEvent) => {
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event.preventDefault();
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event.dataTransfer.dropEffect = 'move';
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@@ -67,17 +84,39 @@ function CanvasFlow() {
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selectNode(null);
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}, [selectNode]);
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const handleExecute = () => {
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const handleValidate = () => {
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const result = validate();
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if (result.valid) {
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// Also send to Claude for intelligent feedback
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const intent = toIntent();
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validateIntent(intent);
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setShowChat(true);
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}
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};
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const handleAnalyze = () => {
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const result = validate();
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if (result.valid) {
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const intent = toIntent();
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// Send to chat
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console.log('Executing intent:', JSON.stringify(intent, null, 2));
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// TODO: Connect to useChat hook
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alert('Intent generated! Check console for JSON output.\n\nIn Phase 2, this will be sent to Claude.');
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analyzeIntent(intent);
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setShowChat(true);
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}
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};
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const handleExecuteClick = () => {
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const result = validate();
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if (result.valid) {
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setShowExecuteDialog(true);
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}
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};
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const handleExecute = async (studyName: string, autoRun: boolean) => {
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const intent = toIntent();
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await executeIntent(intent, studyName, autoRun);
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setShowExecuteDialog(false);
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setShowChat(true);
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};
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return (
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<div className="flex h-full">
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{/* Left: Node Palette */}
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@@ -104,16 +143,38 @@ function CanvasFlow() {
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<MiniMap />
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</ReactFlow>
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{/* Execute Button */}
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{/* Action Buttons */}
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<div className="absolute bottom-4 right-4 flex gap-2 z-10">
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<button
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onClick={validate}
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onClick={() => setShowChat(!showChat)}
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className={`px-3 py-2 rounded-lg transition-colors ${
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showChat
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? 'bg-blue-100 text-blue-700'
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: 'bg-gray-100 text-gray-600 hover:bg-gray-200'
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}`}
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title="Toggle Chat"
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>
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{isConnected ? '💬' : '🔌'}
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</button>
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<button
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onClick={handleValidate}
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className="px-4 py-2 bg-gray-600 text-white rounded-lg hover:bg-gray-700 transition-colors"
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>
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Validate
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</button>
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<button
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onClick={handleExecute}
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onClick={handleAnalyze}
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disabled={!validation.valid}
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className={`px-4 py-2 rounded-lg transition-colors ${
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validation.valid
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? 'bg-purple-600 text-white hover:bg-purple-700'
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: 'bg-gray-300 text-gray-500 cursor-not-allowed'
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}`}
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>
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Analyze
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</button>
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<button
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onClick={handleExecuteClick}
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disabled={!validation.valid}
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className={`px-4 py-2 rounded-lg transition-colors ${
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validation.valid
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@@ -131,8 +192,34 @@ function CanvasFlow() {
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)}
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</div>
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{/* Right: Config Panel */}
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{selectedNode && <NodeConfigPanel nodeId={selectedNode} />}
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{/* Right: Config Panel or Chat */}
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{showChat ? (
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<div className="w-96 border-l border-gray-200 flex flex-col bg-white">
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<div className="p-3 border-b border-gray-200 flex justify-between items-center">
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<h3 className="font-semibold text-gray-800">Claude Assistant</h3>
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<button
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onClick={() => setShowChat(false)}
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className="text-gray-500 hover:text-gray-700"
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>
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✕
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</button>
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</div>
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<ChatPanel
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messages={messages}
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isThinking={isThinking || isExecuting}
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/>
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</div>
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) : selectedNode ? (
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<NodeConfigPanel nodeId={selectedNode} />
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) : null}
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{/* Execute Dialog */}
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<ExecuteDialog
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isOpen={showExecuteDialog}
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onClose={() => setShowExecuteDialog(false)}
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onExecute={handleExecute}
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isExecuting={isExecuting}
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/>
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</div>
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);
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}
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@@ -2,4 +2,6 @@ export { AtomizerCanvas } from './AtomizerCanvas';
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export { NodePalette } from './palette/NodePalette';
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export { NodeConfigPanel } from './panels/NodeConfigPanel';
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export { ValidationPanel } from './panels/ValidationPanel';
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export { ExecuteDialog } from './panels/ExecuteDialog';
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export { ChatPanel } from './panels/ChatPanel';
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export * from './nodes';
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@@ -0,0 +1,48 @@
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/**
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* Chat Panel for Canvas - Displays messages from Claude
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*/
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import { useRef, useEffect } from 'react';
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import { Message, ChatMessage } from '../../chat/ChatMessage';
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import { ThinkingIndicator } from '../../chat/ThinkingIndicator';
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interface ChatPanelProps {
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messages: Message[];
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isThinking: boolean;
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}
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export function ChatPanel({ messages, isThinking }: ChatPanelProps) {
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const messagesEndRef = useRef<HTMLDivElement>(null);
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// Auto-scroll to bottom when new messages arrive
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useEffect(() => {
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messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
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}, [messages, isThinking]);
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return (
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<div className="flex-1 overflow-y-auto p-4 space-y-4 bg-gray-50">
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{/* Welcome message if no messages */}
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{messages.length === 0 && !isThinking && (
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<div className="text-center py-8">
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<div className="w-12 h-12 rounded-xl bg-blue-100 flex items-center justify-center mx-auto mb-4">
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<span className="text-2xl">🧠</span>
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</div>
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<p className="text-gray-500 text-sm max-w-xs mx-auto">
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Use <strong>Validate</strong>, <strong>Analyze</strong>, or <strong>Execute</strong> to interact with Claude about your optimization workflow.
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</p>
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</div>
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)}
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{/* Messages */}
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{messages.map((message) => (
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<ChatMessage key={message.id} message={message} />
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))}
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{/* Thinking indicator */}
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{isThinking && <ThinkingIndicator />}
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{/* Scroll anchor */}
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<div ref={messagesEndRef} />
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</div>
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);
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}
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@@ -0,0 +1,131 @@
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/**
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* Execute Dialog - Prompts for study name before executing canvas intent
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*/
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import { useState } from 'react';
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interface ExecuteDialogProps {
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isOpen: boolean;
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onClose: () => void;
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onExecute: (studyName: string, autoRun: boolean) => void;
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isExecuting: boolean;
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}
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export function ExecuteDialog({
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isOpen,
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onClose,
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onExecute,
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isExecuting,
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}: ExecuteDialogProps) {
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const [studyName, setStudyName] = useState('');
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const [autoRun, setAutoRun] = useState(false);
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const [error, setError] = useState<string | null>(null);
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if (!isOpen) return null;
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const handleSubmit = (e: React.FormEvent) => {
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e.preventDefault();
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// Validate study name
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const trimmed = studyName.trim();
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if (!trimmed) {
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setError('Study name is required');
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return;
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}
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// Check for valid snake_case
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if (!/^[a-z][a-z0-9_]*$/.test(trimmed)) {
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setError('Study name must be snake_case (lowercase letters, numbers, underscores)');
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return;
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}
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setError(null);
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onExecute(trimmed, autoRun);
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};
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const handleClose = () => {
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setStudyName('');
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setAutoRun(false);
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setError(null);
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onClose();
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};
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return (
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<div className="fixed inset-0 bg-black bg-opacity-50 flex items-center justify-center z-50">
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<div className="bg-white rounded-lg shadow-xl w-full max-w-md p-6">
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<h2 className="text-xl font-semibold text-gray-800 mb-4">
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Execute with Claude
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</h2>
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<form onSubmit={handleSubmit}>
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<div className="mb-4">
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<label
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htmlFor="study-name"
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className="block text-sm font-medium text-gray-600 mb-1"
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>
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Study Name
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</label>
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<input
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id="study-name"
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type="text"
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value={studyName}
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onChange={(e) => setStudyName(e.target.value.toLowerCase().replace(/\s+/g, '_'))}
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placeholder="my_optimization_study"
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className="w-full px-3 py-2 border rounded-lg font-mono focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
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disabled={isExecuting}
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autoFocus
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/>
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{error && (
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<p className="mt-1 text-sm text-red-600">{error}</p>
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)}
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<p className="mt-1 text-xs text-gray-500">
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Use snake_case (e.g., bracket_mass_v1, mirror_wfe_optimization)
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</p>
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</div>
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<div className="mb-6">
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<label className="flex items-center gap-2">
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<input
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type="checkbox"
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checked={autoRun}
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onChange={(e) => setAutoRun(e.target.checked)}
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disabled={isExecuting}
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className="w-4 h-4"
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/>
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<span className="text-sm text-gray-700">
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Start optimization immediately after creation
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</span>
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</label>
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</div>
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<div className="flex gap-3 justify-end">
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<button
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type="button"
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onClick={handleClose}
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disabled={isExecuting}
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className="px-4 py-2 text-gray-600 hover:text-gray-800 disabled:opacity-50"
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>
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Cancel
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</button>
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<button
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type="submit"
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disabled={isExecuting}
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className="px-4 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700 disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
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>
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{isExecuting ? (
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<>
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<span className="animate-spin">⏳</span>
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Executing...
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</>
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) : (
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<>
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Send to Claude
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</>
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)}
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</button>
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</div>
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</form>
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</div>
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</div>
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);
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}
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184
atomizer-dashboard/frontend/src/hooks/useCanvasChat.ts
Normal file
184
atomizer-dashboard/frontend/src/hooks/useCanvasChat.ts
Normal file
@@ -0,0 +1,184 @@
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/**
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* Canvas-Chat Bridge Hook
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*
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* Bridges the Canvas UI with the Chat system, allowing canvas intents
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* to be sent to Claude for intelligent execution.
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*/
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import { useCallback, useState } from 'react';
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import { useChat, ChatMode } from './useChat';
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import { OptimizationIntent, formatIntentForChat } from '../lib/canvas/intent';
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interface UseCanvasChatOptions {
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mode?: ChatMode;
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onError?: (error: string) => void;
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}
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interface CanvasChatState {
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isExecuting: boolean;
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lastIntent: OptimizationIntent | null;
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executionResult: ExecutionResult | null;
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}
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interface ExecutionResult {
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success: boolean;
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action: string;
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studyName?: string;
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path?: string;
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error?: string;
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message?: string;
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}
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export function useCanvasChat({
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mode = 'user',
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onError,
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}: UseCanvasChatOptions = {}) {
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const chat = useChat({ mode, onError });
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const [state, setState] = useState<CanvasChatState>({
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isExecuting: false,
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lastIntent: null,
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executionResult: null,
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});
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/**
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* Submit an intent for validation only (no execution)
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*/
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const validateIntent = useCallback(
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async (intent: OptimizationIntent): Promise<void> => {
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setState((prev) => ({
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...prev,
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isExecuting: true,
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lastIntent: intent,
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executionResult: null,
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}));
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// Format intent for chat and ask Claude to validate
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const message = `Please validate this canvas optimization intent:
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${formatIntentForChat(intent)}
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|
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Use the validate_canvas_intent tool to check for errors and provide feedback.`;
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|
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await chat.sendMessage(message);
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setState((prev) => ({
|
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...prev,
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isExecuting: false,
|
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}));
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},
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[chat]
|
||||
);
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|
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/**
|
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* Execute an intent (create study and optionally run)
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*/
|
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const executeIntent = useCallback(
|
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async (
|
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intent: OptimizationIntent,
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studyName: string,
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autoRun: boolean = false
|
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): Promise<void> => {
|
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setState((prev) => ({
|
||||
...prev,
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||||
isExecuting: true,
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||||
lastIntent: intent,
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executionResult: null,
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||||
}));
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||||
|
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// Format intent for chat and ask Claude to execute
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const message = `Please execute this canvas optimization intent to create study "${studyName}"${autoRun ? ' and start the optimization' : ''}:
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||||
|
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${formatIntentForChat(intent)}
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|
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Use the execute_canvas_intent tool with:
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- study_name: "${studyName}"
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||||
- auto_run: ${autoRun}
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|
||||
After execution, provide a summary of what was created.`;
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|
||||
await chat.sendMessage(message);
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|
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setState((prev) => ({
|
||||
...prev,
|
||||
isExecuting: false,
|
||||
}));
|
||||
},
|
||||
[chat]
|
||||
);
|
||||
|
||||
/**
|
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* Get recommendations for an intent without executing
|
||||
*/
|
||||
const analyzeIntent = useCallback(
|
||||
async (intent: OptimizationIntent): Promise<void> => {
|
||||
setState((prev) => ({
|
||||
...prev,
|
||||
isExecuting: true,
|
||||
lastIntent: intent,
|
||||
}));
|
||||
|
||||
const message = `Please analyze this canvas optimization intent and provide recommendations:
|
||||
|
||||
${formatIntentForChat(intent)}
|
||||
|
||||
Use the interpret_canvas_intent tool to:
|
||||
1. Analyze the problem characteristics
|
||||
2. Suggest the best optimization method
|
||||
3. Recommend trial budget
|
||||
4. Identify any potential issues
|
||||
|
||||
Provide your recommendations in a clear, actionable format.`;
|
||||
|
||||
await chat.sendMessage(message);
|
||||
|
||||
setState((prev) => ({
|
||||
...prev,
|
||||
isExecuting: false,
|
||||
}));
|
||||
},
|
||||
[chat]
|
||||
);
|
||||
|
||||
/**
|
||||
* Send a free-form message about the current canvas state
|
||||
*/
|
||||
const askAboutCanvas = useCallback(
|
||||
async (intent: OptimizationIntent, question: string): Promise<void> => {
|
||||
const message = `Given this canvas optimization intent:
|
||||
|
||||
${formatIntentForChat(intent)}
|
||||
|
||||
${question}`;
|
||||
|
||||
await chat.sendMessage(message);
|
||||
},
|
||||
[chat]
|
||||
);
|
||||
|
||||
return {
|
||||
// Chat state
|
||||
messages: chat.messages,
|
||||
isThinking: chat.isThinking || state.isExecuting,
|
||||
isConnected: chat.isConnected,
|
||||
error: chat.error,
|
||||
sessionId: chat.sessionId,
|
||||
mode: chat.mode,
|
||||
|
||||
// Canvas-specific state
|
||||
isExecuting: state.isExecuting,
|
||||
lastIntent: state.lastIntent,
|
||||
executionResult: state.executionResult,
|
||||
|
||||
// Actions
|
||||
validateIntent,
|
||||
executeIntent,
|
||||
analyzeIntent,
|
||||
askAboutCanvas,
|
||||
|
||||
// Base chat actions
|
||||
sendMessage: chat.sendMessage,
|
||||
clearMessages: chat.clearMessages,
|
||||
switchMode: chat.switchMode,
|
||||
};
|
||||
}
|
||||
@@ -21,6 +21,7 @@ import { optimizationTools } from "./tools/optimization.js";
|
||||
import { analysisTools } from "./tools/analysis.js";
|
||||
import { reportingTools } from "./tools/reporting.js";
|
||||
import { physicsTools } from "./tools/physics.js";
|
||||
import { canvasTools } from "./tools/canvas.js";
|
||||
import { adminTools } from "./tools/admin.js";
|
||||
import { ATOMIZER_MODE } from "./utils/paths.js";
|
||||
|
||||
@@ -50,6 +51,7 @@ const userTools: AtomizerTool[] = [
|
||||
...analysisTools,
|
||||
...reportingTools,
|
||||
...physicsTools,
|
||||
...canvasTools,
|
||||
];
|
||||
|
||||
const powerTools: AtomizerTool[] = [
|
||||
|
||||
578
mcp-server/atomizer-tools/src/tools/canvas.ts
Normal file
578
mcp-server/atomizer-tools/src/tools/canvas.ts
Normal file
@@ -0,0 +1,578 @@
|
||||
/**
|
||||
* Canvas Intent Processing Tools
|
||||
*
|
||||
* Tools for processing optimization workflow intents from the Canvas UI.
|
||||
* The canvas serializes node graphs to Intent JSON, which Claude interprets
|
||||
* using protocols and LAC to execute the optimization.
|
||||
*/
|
||||
|
||||
import { execSync } from "child_process";
|
||||
import { AtomizerTool } from "../index.js";
|
||||
import { PYTHON_PATH, STUDIES_DIR } from "../utils/paths.js";
|
||||
|
||||
// Intent type definitions matching frontend schema
|
||||
interface CanvasIntent {
|
||||
version: string;
|
||||
source: "canvas";
|
||||
timestamp: string;
|
||||
model: {
|
||||
path?: string;
|
||||
type?: string;
|
||||
};
|
||||
solver: {
|
||||
type?: string;
|
||||
};
|
||||
design_variables: Array<{
|
||||
name: string;
|
||||
min: number;
|
||||
max: number;
|
||||
unit?: string;
|
||||
}>;
|
||||
extractors: Array<{
|
||||
id: string;
|
||||
name: string;
|
||||
config?: Record<string, unknown>;
|
||||
}>;
|
||||
objectives: Array<{
|
||||
name: string;
|
||||
direction: "minimize" | "maximize";
|
||||
weight: number;
|
||||
extractor: string;
|
||||
}>;
|
||||
constraints: Array<{
|
||||
name: string;
|
||||
operator: string;
|
||||
value: number;
|
||||
extractor: string;
|
||||
}>;
|
||||
optimization: {
|
||||
method?: string;
|
||||
max_trials?: number;
|
||||
};
|
||||
surrogate?: {
|
||||
enabled: boolean;
|
||||
type?: string;
|
||||
min_trials?: number;
|
||||
};
|
||||
}
|
||||
|
||||
interface ValidationError {
|
||||
field: string;
|
||||
message: string;
|
||||
severity: "error" | "warning";
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate a canvas intent and return detailed feedback
|
||||
*/
|
||||
function validateIntent(intent: CanvasIntent): ValidationError[] {
|
||||
const errors: ValidationError[] = [];
|
||||
|
||||
// Model validation
|
||||
if (!intent.model?.path) {
|
||||
errors.push({
|
||||
field: "model.path",
|
||||
message: "Model file path is required",
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
|
||||
// Solver validation
|
||||
if (!intent.solver?.type) {
|
||||
errors.push({
|
||||
field: "solver.type",
|
||||
message: "Solver type is required (e.g., SOL101)",
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
|
||||
// Design variables validation
|
||||
if (!intent.design_variables || intent.design_variables.length === 0) {
|
||||
errors.push({
|
||||
field: "design_variables",
|
||||
message: "At least one design variable is required",
|
||||
severity: "error",
|
||||
});
|
||||
} else {
|
||||
intent.design_variables.forEach((dv, i) => {
|
||||
if (!dv.name) {
|
||||
errors.push({
|
||||
field: `design_variables[${i}].name`,
|
||||
message: "Design variable name is required",
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
if (dv.min >= dv.max) {
|
||||
errors.push({
|
||||
field: `design_variables[${i}]`,
|
||||
message: `Invalid bounds: min (${dv.min}) must be less than max (${dv.max})`,
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// Objectives validation
|
||||
if (!intent.objectives || intent.objectives.length === 0) {
|
||||
errors.push({
|
||||
field: "objectives",
|
||||
message: "At least one objective is required",
|
||||
severity: "error",
|
||||
});
|
||||
} else {
|
||||
intent.objectives.forEach((obj, i) => {
|
||||
if (!obj.name) {
|
||||
errors.push({
|
||||
field: `objectives[${i}].name`,
|
||||
message: "Objective name is required",
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
if (!obj.extractor) {
|
||||
errors.push({
|
||||
field: `objectives[${i}].extractor`,
|
||||
message: "Objective must be connected to an extractor",
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// Extractors validation
|
||||
if (!intent.extractors || intent.extractors.length === 0) {
|
||||
errors.push({
|
||||
field: "extractors",
|
||||
message: "At least one physics extractor is required",
|
||||
severity: "error",
|
||||
});
|
||||
}
|
||||
|
||||
// Optimization settings
|
||||
if (!intent.optimization?.method) {
|
||||
errors.push({
|
||||
field: "optimization.method",
|
||||
message: "Optimization method not specified, will default to TPE",
|
||||
severity: "warning",
|
||||
});
|
||||
}
|
||||
|
||||
if (!intent.optimization?.max_trials) {
|
||||
errors.push({
|
||||
field: "optimization.max_trials",
|
||||
message: "Max trials not specified, will default to 100",
|
||||
severity: "warning",
|
||||
});
|
||||
}
|
||||
|
||||
// Multi-objective check
|
||||
if (intent.objectives && intent.objectives.length > 1) {
|
||||
if (intent.optimization?.method && intent.optimization.method !== "NSGA-II") {
|
||||
errors.push({
|
||||
field: "optimization.method",
|
||||
message: `Multiple objectives detected. Consider using NSGA-II instead of ${intent.optimization.method}`,
|
||||
severity: "warning",
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert canvas intent to optimization_config.json format
|
||||
*/
|
||||
function intentToConfig(intent: CanvasIntent, studyName: string): Record<string, unknown> {
|
||||
// Map extractor IDs to physics names
|
||||
const extractorPhysicsMap: Record<string, string> = {
|
||||
E1: "displacement",
|
||||
E2: "frequency",
|
||||
E3: "stress",
|
||||
E4: "mass_bdf",
|
||||
E5: "mass_cad",
|
||||
E8: "zernike_op2",
|
||||
E9: "zernike_csv",
|
||||
E10: "zernike_rms",
|
||||
};
|
||||
|
||||
return {
|
||||
study_name: studyName,
|
||||
model: {
|
||||
path: intent.model.path,
|
||||
type: intent.model.type || "sim",
|
||||
},
|
||||
solver: {
|
||||
type: "nastran",
|
||||
solution: parseInt(intent.solver.type?.replace("SOL", "") || "101"),
|
||||
},
|
||||
design_variables: intent.design_variables.map((dv) => ({
|
||||
name: dv.name,
|
||||
expression_name: dv.name,
|
||||
lower: dv.min,
|
||||
upper: dv.max,
|
||||
type: "continuous",
|
||||
})),
|
||||
objectives: intent.objectives.map((obj) => ({
|
||||
name: obj.name,
|
||||
direction: obj.direction,
|
||||
weight: obj.weight || 1.0,
|
||||
extractor: obj.extractor,
|
||||
physics: extractorPhysicsMap[obj.extractor] || "custom",
|
||||
})),
|
||||
constraints: intent.constraints.map((c) => ({
|
||||
name: c.name,
|
||||
type: c.operator === "<=" || c.operator === "<" ? "upper" : "lower",
|
||||
value: c.value,
|
||||
extractor: c.extractor,
|
||||
})),
|
||||
method: intent.optimization.method || "TPE",
|
||||
max_trials: intent.optimization.max_trials || 100,
|
||||
surrogate: intent.surrogate?.enabled
|
||||
? {
|
||||
type: intent.surrogate.type || "MLP",
|
||||
min_trials: intent.surrogate.min_trials || 20,
|
||||
}
|
||||
: null,
|
||||
};
|
||||
}
|
||||
|
||||
export const canvasTools: AtomizerTool[] = [
|
||||
{
|
||||
definition: {
|
||||
name: "validate_canvas_intent",
|
||||
description:
|
||||
"Validate a canvas-generated optimization intent. Returns validation errors and warnings without creating a study.",
|
||||
inputSchema: {
|
||||
type: "object" as const,
|
||||
properties: {
|
||||
intent: {
|
||||
type: "object",
|
||||
description: "The optimization intent JSON from the canvas",
|
||||
},
|
||||
},
|
||||
required: ["intent"],
|
||||
},
|
||||
},
|
||||
handler: async (args) => {
|
||||
const intent = args.intent as CanvasIntent;
|
||||
|
||||
if (!intent) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
valid: false,
|
||||
errors: [{ field: "intent", message: "Intent is required", severity: "error" }],
|
||||
}),
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
const errors = validateIntent(intent);
|
||||
const hasErrors = errors.some((e) => e.severity === "error");
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify(
|
||||
{
|
||||
valid: !hasErrors,
|
||||
errors: errors.filter((e) => e.severity === "error"),
|
||||
warnings: errors.filter((e) => e.severity === "warning"),
|
||||
summary: hasErrors
|
||||
? `Found ${errors.filter((e) => e.severity === "error").length} error(s) that must be fixed`
|
||||
: `Intent is valid with ${errors.filter((e) => e.severity === "warning").length} warning(s)`,
|
||||
},
|
||||
null,
|
||||
2
|
||||
),
|
||||
},
|
||||
],
|
||||
};
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
definition: {
|
||||
name: "execute_canvas_intent",
|
||||
description:
|
||||
"Execute a canvas-generated optimization intent. Creates a study from the intent and optionally starts the optimization.",
|
||||
inputSchema: {
|
||||
type: "object" as const,
|
||||
properties: {
|
||||
intent: {
|
||||
type: "object",
|
||||
description: "The optimization intent JSON from the canvas",
|
||||
},
|
||||
study_name: {
|
||||
type: "string",
|
||||
description: "Name for the study (snake_case)",
|
||||
},
|
||||
auto_run: {
|
||||
type: "boolean",
|
||||
description: "Whether to automatically start the optimization after creating the study",
|
||||
},
|
||||
},
|
||||
required: ["intent", "study_name"],
|
||||
},
|
||||
},
|
||||
handler: async (args) => {
|
||||
const intent = args.intent as CanvasIntent;
|
||||
const studyName = args.study_name as string;
|
||||
const autoRun = args.auto_run as boolean || false;
|
||||
|
||||
// First validate
|
||||
const errors = validateIntent(intent);
|
||||
const hasErrors = errors.some((e) => e.severity === "error");
|
||||
|
||||
if (hasErrors) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
success: false,
|
||||
action: "validation_failed",
|
||||
errors: errors.filter((e) => e.severity === "error"),
|
||||
message: "Cannot execute intent - validation errors must be fixed first",
|
||||
}, null, 2),
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
// Convert intent to config
|
||||
const config = intentToConfig(intent, studyName);
|
||||
const configJson = JSON.stringify(config).replace(/"/g, '\\"');
|
||||
|
||||
// Python script to create study from config
|
||||
const script = `
|
||||
import sys
|
||||
import json
|
||||
sys.path.insert(0, r"C:/Users/antoi/Atomizer")
|
||||
from pathlib import Path
|
||||
from optimization_engine.study.creator import StudyCreator
|
||||
|
||||
config = json.loads("""${configJson}""")
|
||||
study_name = "${studyName}"
|
||||
|
||||
try:
|
||||
creator = StudyCreator()
|
||||
result = creator.create_from_config(study_name, config)
|
||||
print(json.dumps({"success": True, "study_name": study_name, "path": str(result)}))
|
||||
except Exception as e:
|
||||
print(json.dumps({"success": False, "error": str(e)}))
|
||||
sys.exit(1)
|
||||
`;
|
||||
|
||||
try {
|
||||
const output = execSync(`"${PYTHON_PATH}" -c "${script}"`, {
|
||||
encoding: "utf-8",
|
||||
cwd: STUDIES_DIR,
|
||||
timeout: 60000,
|
||||
});
|
||||
|
||||
const result = JSON.parse(output.trim());
|
||||
|
||||
if (!result.success) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
success: false,
|
||||
action: "creation_failed",
|
||||
error: result.error,
|
||||
}, null, 2),
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
|
||||
// If auto_run, start the optimization
|
||||
if (autoRun) {
|
||||
const runScript = `
|
||||
import sys
|
||||
sys.path.insert(0, r"C:/Users/antoi/Atomizer")
|
||||
from optimization_engine.core.runner import OptimizationRunner
|
||||
|
||||
try:
|
||||
runner = OptimizationRunner("${studyName}")
|
||||
runner.start_async()
|
||||
print("STARTED")
|
||||
except Exception as e:
|
||||
print(f"RUN_ERROR: {e}")
|
||||
`;
|
||||
try {
|
||||
const runOutput = execSync(`"${PYTHON_PATH}" -c "${runScript}"`, {
|
||||
encoding: "utf-8",
|
||||
cwd: STUDIES_DIR,
|
||||
timeout: 30000,
|
||||
});
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
success: true,
|
||||
action: "created_and_started",
|
||||
study_name: studyName,
|
||||
path: result.path,
|
||||
message: `Study "${studyName}" created and optimization started!`,
|
||||
config_summary: {
|
||||
design_variables: intent.design_variables.length,
|
||||
objectives: intent.objectives.length,
|
||||
constraints: intent.constraints.length,
|
||||
method: intent.optimization.method || "TPE",
|
||||
max_trials: intent.optimization.max_trials || 100,
|
||||
},
|
||||
}, null, 2),
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (runError) {
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
success: true,
|
||||
action: "created_but_run_failed",
|
||||
study_name: studyName,
|
||||
path: result.path,
|
||||
run_error: runError instanceof Error ? runError.message : String(runError),
|
||||
message: `Study created but failed to start optimization. You can start it manually.`,
|
||||
}, null, 2),
|
||||
},
|
||||
],
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
success: true,
|
||||
action: "created",
|
||||
study_name: studyName,
|
||||
path: result.path,
|
||||
message: `Study "${studyName}" created successfully! Use run_optimization to start.`,
|
||||
config_summary: {
|
||||
design_variables: intent.design_variables.length,
|
||||
objectives: intent.objectives.length,
|
||||
constraints: intent.constraints.length,
|
||||
method: intent.optimization.method || "TPE",
|
||||
max_trials: intent.optimization.max_trials || 100,
|
||||
},
|
||||
}, null, 2),
|
||||
},
|
||||
],
|
||||
};
|
||||
} catch (error) {
|
||||
const message = error instanceof Error ? error.message : String(error);
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify({
|
||||
success: false,
|
||||
action: "error",
|
||||
error: message,
|
||||
}, null, 2),
|
||||
},
|
||||
],
|
||||
isError: true,
|
||||
};
|
||||
}
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
definition: {
|
||||
name: "interpret_canvas_intent",
|
||||
description:
|
||||
"Interpret a canvas intent and provide recommendations. Does not create anything - just analyzes and suggests improvements.",
|
||||
inputSchema: {
|
||||
type: "object" as const,
|
||||
properties: {
|
||||
intent: {
|
||||
type: "object",
|
||||
description: "The optimization intent JSON from the canvas",
|
||||
},
|
||||
},
|
||||
required: ["intent"],
|
||||
},
|
||||
},
|
||||
handler: async (args) => {
|
||||
const intent = args.intent as CanvasIntent;
|
||||
|
||||
const analysis: Record<string, unknown> = {
|
||||
source: intent.source,
|
||||
timestamp: intent.timestamp,
|
||||
};
|
||||
|
||||
// Analyze problem characteristics
|
||||
const numObjectives = intent.objectives?.length || 0;
|
||||
const numDesignVars = intent.design_variables?.length || 0;
|
||||
const numConstraints = intent.constraints?.length || 0;
|
||||
|
||||
analysis.problem_type = numObjectives > 1 ? "multi-objective" : "single-objective";
|
||||
analysis.complexity = numDesignVars > 5 ? "high" : numDesignVars > 2 ? "medium" : "low";
|
||||
|
||||
// Method recommendation based on problem characteristics
|
||||
const recommendations: string[] = [];
|
||||
|
||||
if (numObjectives > 1 && intent.optimization?.method !== "NSGA-II") {
|
||||
recommendations.push(
|
||||
`Consider using NSGA-II for multi-objective optimization (${numObjectives} objectives detected)`
|
||||
);
|
||||
}
|
||||
|
||||
if (numDesignVars > 10 && intent.optimization?.method === "CMA-ES") {
|
||||
recommendations.push(
|
||||
"CMA-ES may struggle with high-dimensional problems. Consider TPE or GP-BO."
|
||||
);
|
||||
}
|
||||
|
||||
if ((intent.optimization?.max_trials || 100) < 50 && numDesignVars > 5) {
|
||||
recommendations.push(
|
||||
`Trial budget (${intent.optimization?.max_trials || 100}) may be insufficient for ${numDesignVars} design variables. Consider 100+ trials.`
|
||||
);
|
||||
}
|
||||
|
||||
if (!intent.surrogate?.enabled && (intent.optimization?.max_trials || 100) > 100) {
|
||||
recommendations.push(
|
||||
"Consider enabling neural surrogate for faster optimization with high trial counts."
|
||||
);
|
||||
}
|
||||
|
||||
analysis.recommendations = recommendations;
|
||||
analysis.suggested_method =
|
||||
numObjectives > 1
|
||||
? "NSGA-II"
|
||||
: numDesignVars > 10
|
||||
? "TPE"
|
||||
: "TPE"; // Default to TPE for most cases
|
||||
|
||||
analysis.suggested_trials =
|
||||
numDesignVars <= 3 ? 50 : numDesignVars <= 6 ? 100 : numDesignVars <= 10 ? 200 : 500;
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: JSON.stringify(analysis, null, 2),
|
||||
},
|
||||
],
|
||||
};
|
||||
},
|
||||
},
|
||||
];
|
||||
Reference in New Issue
Block a user