feat(local-llm): add generate utilities and reactive Svelte status

Add generate.ts with streaming chat completions, JSON extraction, and
text classification helpers. Add status.svelte.ts with Svelte 5 runes
reactive wrapper for LLM engine state.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

packages/local-llm/src/generate.ts

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+import { getEngine } from './engine.js';
+
+export interface ChatMessage {
+	role: 'system' | 'user' | 'assistant';
+	content: string;
+}
+
+export interface GenerateOptions {
+	messages: ChatMessage[];
+	temperature?: number;
+	maxTokens?: number;
+	onToken?: (token: string) => void;
+}
+
+export interface GenerateResult {
+	content: string;
+	latencyMs: number;
+	usage: {
+		prompt_tokens: number;
+		completion_tokens: number;
+	};
+}
+
+export async function generate(options: GenerateOptions): Promise<GenerateResult> {
+	const engine = getEngine();
+	if (!engine) throw new Error('No model loaded. Call loadLocalLlm() first.');
+
+	const { messages, temperature = 0.7, maxTokens = 1024, onToken } = options;
+	const start = performance.now();
+
+	const reply = await engine.chat.completions.create({
+		messages,
+		temperature,
+		max_tokens: maxTokens,
+		stream: !!onToken,
+		stream_options: onToken ? { include_usage: true } : undefined,
+	});
+
+	let content = '';
+	let promptTokens = 0;
+	let completionTokens = 0;
+
+	if (Symbol.asyncIterator in Object(reply)) {
+		for await (const chunk of reply as AsyncIterable<any>) {
+			const delta = chunk.choices?.[0]?.delta?.content;
+			if (delta) {
+				content += delta;
+				onToken?.(delta);
+			}
+			if (chunk.usage) {
+				promptTokens = chunk.usage.prompt_tokens ?? 0;
+				completionTokens = chunk.usage.completion_tokens ?? 0;
+			}
+		}
+	} else {
+		const completion = reply as any;
+		content = completion.choices?.[0]?.message?.content ?? '';
+		promptTokens = completion.usage?.prompt_tokens ?? 0;
+		completionTokens = completion.usage?.completion_tokens ?? 0;
+	}
+
+	const latencyMs = Math.round(performance.now() - start);
+
+	return {
+		content,
+		latencyMs,
+		usage: { prompt_tokens: promptTokens, completion_tokens: completionTokens },
+	};
+}
+
+export async function extractJson(text: string, instruction: string): Promise<unknown> {
+	const result = await generate({
+		messages: [
+			{
+				role: 'system',
+				content:
+					'You are a JSON extraction assistant. Respond ONLY with valid JSON, no explanation or markdown.',
+			},
+			{
+				role: 'user',
+				content: `${instruction}\n\nText:\n${text}`,
+			},
+		],
+		temperature: 0.1,
+		maxTokens: 2048,
+	});
+
+	const jsonMatch = result.content.match(/[[{][\s\S]*[}\]]/);
+	if (!jsonMatch) throw new Error('No JSON found in response');
+	return JSON.parse(jsonMatch[0]);
+}
+
+export async function classify(text: string, categories: string[]): Promise<string> {
+	const result = await generate({
+		messages: [
+			{
+				role: 'system',
+				content: `You are a text classifier. Classify the text into exactly one of these categories: ${categories.join(', ')}. Respond with ONLY the category name, nothing else.`,
+			},
+			{
+				role: 'user',
+				content: text,
+			},
+		],
+		temperature: 0.1,
+		maxTokens: 50,
+	});
+
+	const response = result.content.trim().toLowerCase();
+	const match = categories.find((c) => response.includes(c.toLowerCase()));
+	return match ?? result.content.trim();
+}

packages/local-llm/src/status.svelte.ts

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+import { subscribe, type LlmState } from './engine.js';
+
+/**
+ * Reactive status wrapper for use in Svelte 5 components.
+ * Returns an object with a `current` property that updates reactively.
+ */
+export function getLocalLlmStatus(): { current: LlmState } {
+	let state = $state<LlmState>({ state: 'idle' });
+
+	$effect(() => {
+		const unsub = subscribe((s) => {
+			state = s;
+		});
+		return unsub;
+	});
+
+	return {
+		get current() {
+			return state;
+		},
+	};
+}