feat: add LLM question generation

Made-with: Cursor

src/services/llm/llm.service.ts

@@ -1,4 +1,6 @@
+import { z } from 'zod';
 import { env } from '../../config/env.js';
+import type { Stack, Level, QuestionType } from '../../db/schema/enums.js';
 
 export interface LlmConfig {
   baseUrl: string;
@@ -21,6 +23,37 @@ export interface ChatCompletionResponse {
   }>;
 }
 
+const QUESTION_TYPES: QuestionType[] = ['single_choice', 'multiple_select', 'true_false', 'short_text'];
+
+const optionSchema = z.object({
+  key: z.string().min(1),
+  text: z.string().min(1),
+});
+
+const generatedQuestionSchema = z.object({
+  questionText: z.string().min(1),
+  type: z.enum(QUESTION_TYPES as [string, ...string[]]),
+  options: z.array(optionSchema).optional(),
+  correctAnswer: z.union([z.string(), z.array(z.string())]),
+  explanation: z.string().min(1),
+});
+
+const generateQuestionsResponseSchema = z.object({
+  questions: z.array(generatedQuestionSchema),
+});
+
+export type GeneratedQuestion = z.infer<typeof generatedQuestionSchema> & {
+  stack: Stack;
+  level: Level;
+};
+
+export interface GenerateQuestionsInput {
+  stack: Stack;
+  level: Level;
+  count: number;
+  types?: QuestionType[];
+}
+
 export class LlmService {
   private readonly config: LlmConfig;
 
@@ -84,4 +117,52 @@ export class LlmService {
       throw new Error('LLM request failed');
     }
   }
+
+  async generateQuestions(input: GenerateQuestionsInput): Promise<GeneratedQuestion[]> {
+    const { stack, level, count, types = QUESTION_TYPES } = input;
+
+    const typeList = types.join(', ');
+    const systemPrompt = `You are a technical interview question generator. Generate exactly ${count} programming/tech questions.
+Return ONLY valid JSON in this exact format (no markdown, no code blocks):
+{"questions":[{"questionText":"...","type":"single_choice|multiple_select|true_false|short_text","options":[{"key":"a","text":"..."}],"correctAnswer":"a" or ["a","b"],"explanation":"..."}]}
+Rules: type must be one of: ${typeList}. For single_choice/multiple_select: options array required with key (a,b,c,d). For true_false: options [{"key":"true","text":"True"},{"key":"false","text":"False"}]. For short_text: options omitted, correctAnswer is string.`;
+
+    const userPrompt = `Generate ${count} questions for stack="${stack}", level="${level}". Use types: ${typeList}.`;
+
+    const raw = await this.chat([
+      { role: 'system', content: systemPrompt },
+      { role: 'user', content: userPrompt },
+    ]);
+
+    const jsonStr = extractJson(raw);
+    const parsed = JSON.parse(jsonStr) as unknown;
+
+    const result = generateQuestionsResponseSchema.safeParse(parsed);
+    if (!result.success) {
+      throw new Error(`LLM response validation failed: ${result.error.message}`);
+    }
+
+    const questions: GeneratedQuestion[] = result.data.questions.map((q) => ({
+      ...q,
+      stack,
+      level,
+    }));
+
+    for (const q of questions) {
+      if ((q.type === 'single_choice' || q.type === 'multiple_select') && (!q.options || q.options.length === 0)) {
+        throw new Error(`Question validation failed: ${q.type} requires options`);
+      }
+      if (q.type === 'true_false' && (!q.options || q.options.length < 2)) {
+        throw new Error(`Question validation failed: true_false requires true/false options`);
+      }
+    }
+
+    return questions;
+  }
+}
+
+function extractJson(text: string): string {
+  const trimmed = text.trim();
+  const match = trimmed.match(/\{[\s\S]*\}/);
+  return match ? match[0]! : trimmed;
 }