Llm generate question service

docker-compose.yml

@@ -43,6 +43,8 @@ services:
     image: ghcr.io/arquisoft/wichat_es6a/llmservice:latest
     profiles: ["dev", "prod"]
     build: ./llmservice
+    env_file:
+      - ./llmservice/.env
     ports:
       - "8003:8003"
     networks:

llmservice/llm-service.js

@@ -3,37 +3,41 @@ const express = require('express');
 
 const app = express();
 const port = 8003;
+let moderation = "You are a quiz game assistant.";
+require('dotenv').config();  // Cargar las variables de entorno desde .env
 
-// Middleware to parse JSON in request body
-app.use(express.json());
+// Middleware para parsear JSON
+app.use(express.json()); 
+
+// Agregar apiKey automáticamente en la solicitud si no está presente
+app.use((req, res, next) => {
+  // Verificar si no se incluye apiKey en el cuerpo de la solicitud
+  if (!req.body.apiKey) {
+    req.body.apiKey = process.env.LLM_API_KEY;  // Usar la API Key desde las variables de entorno
+  }
+  next();
+});
 
-// Define configurations for different LLM APIs
 const llmConfigs = {
-  gemini: {
-    url: (apiKey) => `https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=${apiKey}`,
-    transformRequest: (question) => ({
-      contents: [{ parts: [{ text: question }] }]
-    }),
-    transformResponse: (response) => response.data.candidates[0]?.content?.parts[0]?.text
-  },
   empathy: {
-    url: () => 'https://empathyai.staging.empathy.co/v1/chat/completions',
-    transformRequest: (question) => ({
+    url: () => 'https://ai-challenge.empathy.ai/v1/chat/completions',
+    transformRequest: (question, moderation) => ({
       model: "qwen/Qwen2.5-Coder-7B-Instruct",
+      stream: false, // No soporta stream=true con axios directamente
       messages: [
-        { role: "system", content: "You are a helpful assistant." },
+        { role: "system", content: moderation },
         { role: "user", content: question }
       ]
     }),
-    transformResponse: (response) => response.data.choices[0]?.message?.content,
+    transformResponse: (response) => response.data.choices?.[0]?.message?.content || "No response",
     headers: (apiKey) => ({
       Authorization: `Bearer ${apiKey}`,
       'Content-Type': 'application/json'
     })
   }
 };
 
-// Function to validate required fields in the request body
+// Validar campos requeridos
 function validateRequiredFields(req, requiredFields) {
   for (const field of requiredFields) {
     if (!(field in req.body)) {
@@ -42,50 +46,92 @@ function validateRequiredFields(req, requiredFields) {
   }
 }
 
-// Generic function to send questions to LLM
-async function sendQuestionToLLM(question, apiKey, model = 'gemini') {
+// Función genérica para enviar preguntas al LLM
+async function sendQuestionToLLM(question, apiKey, moderation) {
   try {
-    const config = llmConfigs[model];
+    const config = llmConfigs["empathy"];
     if (!config) {
-      throw new Error(`Model "${model}" is not supported.`);
+      throw new Error(`Model is not supported.`);
     }
 
-    const url = config.url(apiKey);
-    const requestData = config.transformRequest(question);
-
-    const headers = {
-      'Content-Type': 'application/json',
-      ...(config.headers ? config.headers(apiKey) : {})
-    };
+    const url = config.url();
+    const requestData = config.transformRequest(question, moderation);
+    const headers = config.headers(apiKey);
 
     const response = await axios.post(url, requestData, { headers });
 
     return config.transformResponse(response);
 
   } catch (error) {
-    console.error(`Error sending question to ${model}:`, error.message || error);
-    return null;
+    console.error(`Error sending question:`, error.message || error);
+    return "Error processing request.";
   }
 }
 
+// Ruta para configurar el prompt del asistente
+app.post('/configureAssistant', async (req, res) => {
+  if (!req.body.moderation) {
+    return res.status(400).json({ error: "Missing moderation prompt" });
+  }
+  moderation = req.body.moderation;
+  res.json({ message: "Moderation prompt updated" });
+});
+
+// Ruta para enviar una pregunta
 app.post('/ask', async (req, res) => {
   try {
-    // Check if required fields are present in the request body
-    validateRequiredFields(req, ['question', 'model', 'apiKey']);
+    validateRequiredFields(req, ['question']);
+
+    const { question, apiKey } = req.body;  // La apiKey ya ha sido añadida automáticamente
+    const answer = await sendQuestionToLLM(question, apiKey, moderation);
 
-    const { question, model, apiKey } = req.body;
-    const answer = await sendQuestionToLLM(question, apiKey, model);
     res.json({ answer });
 
   } catch (error) {
     res.status(400).json({ error: error.message });
   }
 });
 
+// Servicio 1: Generación de preguntas y respuestas a partir del contexto
+app.post('/generateQuestions', async (req, res) => {
+  try {
+    if (!req.body.context) {
+      return res.status(400).json({ error: "Missing context" });
+    }
+
+    const context = req.body.context;
+
+    const prompt = `A partir del siguiente texto, genera 4 preguntas de opción múltiple. 
+    Cada pregunta debe tener 4 respuestas, una correcta y tres incorrectas:
+
+    Texto: "${context}"
+
+    Responde en formato JSON, la respuesta debe incluir UNICAMENTE el formato JSON con las preguntas y respuestas:
+    {
+      "questions": [
+        {
+          "question": "Pregunta 1",
+          "answers": [
+            { "text": "Respuesta correcta", "correct": true },
+            { "text": "Respuesta incorrecta 1", "correct": false },
+            { "text": "Respuesta incorrecta 2", "correct": false },
+            { "text": "Respuesta incorrecta 3", "correct": false }
+          ]
+        },
+        ...
+      ]
+    }`;
+
+    const response = await sendQuestionToLLM(prompt, req.body.apiKey, moderation);
+    console.log("Response:", response);
+    res.json(response);
+  } catch (error) {
+    res.status(500).json({ error: "Failed to generate questions" });
+  }
+});
+
 const server = app.listen(port, () => {
   console.log(`LLM Service listening at http://localhost:${port}`);
 });
 
-module.exports = server
-
-
+module.exports = server;

llmservice/package.json

@@ -9,12 +9,13 @@
   "license": "ISC",
   "description": "",
   "homepage": "https://github.com/arquisoft/wichat_es6a#readme",
-    "dependencies": {
-        "axios": "^1.7.9",
-        "express": "^4.21.2"
-    },
-    "devDependencies": {
-        "jest": "^29.7.0",
-        "supertest": "^7.0.0"
-    }
+  "dependencies": {
+    "axios": "^1.7.9",
+    "dotenv": "^16.4.7",
+    "express": "^4.21.2"
+  },
+  "devDependencies": {
+    "jest": "^29.7.0",
+    "supertest": "^7.0.0"
+  }
 }

package.json

@@ -0,0 +1,5 @@
+{
+  "dependencies": {
+    "dotenv": "^16.4.7"
+  }
+}