Add language assistant settings and related features: Introduce new routes and controller methods for managing language assistant settings, including retrieval and saving of LLM configurations. Update navigation structure to include language assistant options. Enhance vocab course model to support additional learning attributes such as learning goals and core patterns. Update SQL scripts to reflect new database schema changes for vocab courses. Improve localization for language assistant settings in German and English.

mcp/language-course-server/src/llm.mjs

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+/**
+ * OpenAI-kompatible Chat-API (OpenAI, Azure OpenAI, Ollama, LM Studio, vLLM, …).
+ *
+ * Umgebung:
+ *   LANGUAGE_COURSE_LLM_API_KEY  oder OPENAI_API_KEY
+ *   LANGUAGE_COURSE_LLM_BASE_URL (optional, z. B. http://127.0.0.1:11434/v1 für Ollama)
+ *   LANGUAGE_COURSE_LLM_MODEL      (optional, Default: gpt-4o-mini)
+ */
+import OpenAI from 'openai';
+
+export function getLlmEnv() {
+    const apiKey =
+        process.env.LANGUAGE_COURSE_LLM_API_KEY ||
+        process.env.OPENAI_API_KEY ||
+        '';
+    const baseURL = process.env.LANGUAGE_COURSE_LLM_BASE_URL || undefined;
+    const model = process.env.LANGUAGE_COURSE_LLM_MODEL || 'gpt-4o-mini';
+    return { apiKey, baseURL, model };
+}
+
+export function isLlmConfigured() {
+    return Boolean(getLlmEnv().apiKey);
+}
+
+export function getOpenAiClient() {
+    const { apiKey, baseURL } = getLlmEnv();
+    if (!apiKey) return null;
+    return new OpenAI({
+        apiKey,
+        baseURL,
+    });
+}
+
+/**
+ * @param {import('openai').ChatCompletionMessageParam[]} messages
+ * @param {{ model?: string; temperature?: number; maxTokens?: number }} [opts]
+ */
+export async function chatComplete(messages, opts = {}) {
+    const client = getOpenAiClient();
+    if (!client) {
+        throw new Error(
+            'Kein LLM konfiguriert: LANGUAGE_COURSE_LLM_API_KEY oder OPENAI_API_KEY setzen (siehe README).'
+        );
+    }
+    const { model, temperature = 0.6, maxTokens = 2048 } = opts;
+    const m = model || getLlmEnv().model;
+    const res = await client.chat.completions.create({
+        model: m,
+        messages,
+        temperature,
+        max_tokens: maxTokens,
+    });
+    const text = res.choices[0]?.message?.content?.trim() || '';
+    if (!text) {
+        throw new Error('Leere Antwort vom Modell.');
+    }
+    return text;
+}