feat: fix AI for generation questions with RAG

services/cms-service/src/handlers_question_bank.rs

@@ -294,8 +294,8 @@ pub async fn import_from_mysql(
             
             if let Some(question) = mq {
                 // Map MySQL question type to platform question type
-                let question_type = map_mysql_question_type(question.id_tipo_pregunta);
-                
+                let question_type = map_mysql_question_type(question.id_tipo_pregunta, None);
+
                 let options = if question_type == QuestionBankType::MultipleChoice {
                     Some(json!(["Opción A", "Opción B", "Opción C", "Opción D"]))
                 } else if question_type == QuestionBankType::TrueFalse {
@@ -374,7 +374,7 @@ pub async fn import_from_mysql(
         }
 
         // Map MySQL question type to platform question type
-        let question_type = map_mysql_question_type(mq.id_tipo_pregunta);
+        let question_type = map_mysql_question_type(mq.id_tipo_pregunta, None);
 
         // Create options for multiple choice (if applicable)
         let options = if question_type == QuestionBankType::MultipleChoice {
@@ -431,16 +431,110 @@ pub async fn import_from_mysql(
 
 // ==================== Helpers ====================
 
-fn map_mysql_question_type(mysql_type: i32) -> QuestionBankType {
+fn map_mysql_question_type(mysql_type: i32, tipo_nombre: Option<&str>) -> QuestionBankType {
     // Map MySQL question types to platform types
-    // This depends on how tipos are defined in the MySQL database
-    match mysql_type {
-        1 => QuestionBankType::MultipleChoice,
-        2 => QuestionBankType::TrueFalse,
-        3 => QuestionBankType::ShortAnswer,
-        4 => QuestionBankType::Matching,
-        _ => QuestionBankType::MultipleChoice, // Default
+    // First try by name, then by ID as fallback
+    if let Some(nombre) = tipo_nombre {
+        let nombre_lower = nombre.to_lowercase();
+        if nombre_lower.contains("selecc") || nombre_lower.contains("múltiple") || nombre_lower.contains("multiple") || nombre_lower.contains("alternativa") {
+            return QuestionBankType::MultipleChoice;
+        }
+        if nombre_lower.contains("verdadero") || nombre_lower.contains("falso") {
+            return QuestionBankType::TrueFalse;
+        }
+        if nombre_lower.contains("emparej") || nombre_lower.contains("match") {
+            return QuestionBankType::Matching;
+        }
+        if nombre_lower.contains("orden") {
+            return QuestionBankType::Ordering;
+        }
+        if nombre_lower.contains("complet") {
+            return QuestionBankType::FillInTheBlanks;
+        }
+        if nombre_lower.contains("ensayo") || nombre_lower.contains("essay") {
+            return QuestionBankType::Essay;
+        }
+        if nombre_lower.contains("corta") || nombre_lower.contains("short") || nombre_lower.contains("texto") {
+            return QuestionBankType::ShortAnswer;
+        }
+        // Audio type in MySQL is typically listening comprehension with multiple choice answers
+        if nombre_lower.contains("audio") || nombre_lower.contains("listening") {
+            return QuestionBankType::MultipleChoice;
+        }
     }
+
+    // Fallback to ID mapping
+    match mysql_type {
+        1 => QuestionBankType::MultipleChoice, // Alternativa
+        2 => QuestionBankType::MultipleChoice, // Audio (listening comprehension)
+        3 => QuestionBankType::ShortAnswer,    // Texto
+        4 => QuestionBankType::Matching,
+        5 => QuestionBankType::Ordering,
+        6 => QuestionBankType::FillInTheBlanks,
+        7 => QuestionBankType::Essay,
+        _ => QuestionBankType::MultipleChoice,
+    }
+}
+
+/// Parse MySQL answers JSON and extract options and correct answer
+fn parse_mysql_answers(
+    answers_json: Option<&str>,
+    question_type: QuestionBankType,
+) -> (Option<serde_json::Value>, Option<serde_json::Value>) {
+    if let Some(json_str) = answers_json {
+        if let Ok(answers) = serde_json::from_str::<Vec<serde_json::Value>>(json_str) {
+            if !answers.is_empty() {
+                // Extract options (all answer texts)
+                let options: Vec<String> = answers
+                    .iter()
+                    .filter_map(|a| a.get("texto").and_then(|t| t.as_str()).map(String::from))
+                    .collect();
+                
+                // Extract correct answer(s)
+                let correct_indices: Vec<usize> = answers
+                    .iter()
+                    .enumerate()
+                    .filter(|(_, a)| a.get("es_correcta").and_then(|c| c.as_bool()).unwrap_or(false))
+                    .map(|(i, _)| i)
+                    .collect();
+                
+                let options_json = if !options.is_empty() {
+                    Some(serde_json::json!(options))
+                } else {
+                    None
+                };
+                
+                let correct_answer = if question_type == QuestionBankType::TrueFalse || 
+                    question_type == QuestionBankType::MultipleChoice {
+                    // For multiple choice, store index/indices
+                    if correct_indices.len() == 1 {
+                        Some(serde_json::json!(correct_indices[0]))
+                    } else if correct_indices.len() > 1 {
+                        Some(serde_json::json!(correct_indices))
+                    } else {
+                        Some(serde_json::json!(0)) // Default to first
+                    }
+                } else {
+                    // For other types, store the text
+                    correct_indices.first()
+                        .and_then(|&i| answers.get(i))
+                        .and_then(|a| a.get("texto").and_then(|t| t.as_str()))
+                        .map(|t| serde_json::json!(t))
+                };
+                
+                return (options_json, correct_answer);
+            }
+        }
+    }
+    
+    // Default values if no answers provided
+    let default_options = match question_type {
+        QuestionBankType::MultipleChoice => Some(serde_json::json!(["Opción A", "Opción B", "Opción C", "Opción D"])),
+        QuestionBankType::TrueFalse => Some(serde_json::json!(["Verdadero", "Falso"])),
+        _ => None,
+    };
+    
+    (default_options, Some(serde_json::json!(0)))
 }
 
 // ==================== MySQL Integration ====================
@@ -496,24 +590,41 @@ pub async fn import_all_from_mysql(
         .await
         .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Failed to connect to MySQL: {}", e)))?;
     
-    // Fetch ALL questions from MySQL
+    // Fetch ALL questions from MySQL with answers (using JSON aggregation for answers)
     let mysql_questions: Vec<MySqlQuestionFull> = sqlx::query_as(
         r#"
-        SELECT 
-            bp.idPregunta,
-            bp.descripcion,
-            bp.idTipoPregunta,
-            bp.activo,
-            c.idCursos,
-            c.NombreCurso,
-            c.NivelCurso,
-            pe.idPlanDeEstudios,
-            pe.Nombre as PlanNombre
+        SELECT
+            bp.idPregunta AS id_pregunta,
+            bp.descripcion AS descripcion,
+            bp.idTipoPregunta AS id_tipo_pregunta,
+            bp.activo AS activo,
+            c.idCursos AS id_cursos,
+            c.NombreCurso AS nombre_curso,
+            c.NivelCurso AS nivel_curso,
+            pe.idPlanDeEstudios AS id_plan_de_estudios,
+            pe.Nombre AS plan_nombre,
+            tp.descripcion AS tipo_pregunta_nombre,
+            CAST(
+                (
+                    SELECT JSON_ARRAYAGG(
+                        JSON_OBJECT(
+                            'idRespuesta', br.idRespuesta,
+                            'texto', br.descripcion,
+                            'es_correcta', br.resultado = 1
+                        )
+                    )
+                    FROM bancorespuestas br
+                    WHERE br.idPregunta = bp.idPregunta
+                    AND br.activo = 1
+                ) AS CHAR
+            ) AS respuestas_json
         FROM bancopreguntas bp
         JOIN curso c ON bp.idCursos = c.idCursos
         JOIN plandeestudios pe ON bp.idPlanDeEstudios = pe.idPlanDeEstudios
+        JOIN tipopregunta tp ON bp.idTipoPregunta = tp.idTipoPregunta
         WHERE bp.activo = 1
         ORDER BY pe.Nombre, c.NombreCurso, bp.idPregunta
+        LIMIT 500
         "#
     )
     .fetch_all(&mysql_pool)
@@ -566,15 +677,14 @@ pub async fn import_all_from_mysql(
                 }
             }
             None => {
-                // New question - insert
-                let question_type = map_mysql_question_type(mq.id_tipo_pregunta);
-                let options = if question_type == QuestionBankType::MultipleChoice {
-                    Some(serde_json::json!(["Opción A", "Opción B", "Opción C", "Opción D"]))
-                } else if question_type == QuestionBankType::TrueFalse {
-                    Some(serde_json::json!(["Verdadero", "Falso"]))
-                } else {
-                    None
-                };
+                // New question - insert with answers from MySQL
+                let question_type = map_mysql_question_type(mq.id_tipo_pregunta, mq.tipo_pregunta_nombre.as_deref());
+                
+                // Parse answers from MySQL
+                let (options, correct_answer) = parse_mysql_answers(mq.respuestas_json.as_deref(), question_type);
+
+                let has_answers = mq.respuestas_json.is_some();
+                let question_type_name = mq.tipo_pregunta_nombre.clone();
                 
                 let source_metadata = serde_json::json!({
                     "mysql_table": "bancopreguntas",
@@ -585,10 +695,12 @@ pub async fn import_all_from_mysql(
                     "idPlanDeEstudios": mq.id_plan_de_estudios,
                     "plan_nombre": mq.plan_nombre,
                     "idTipoPregunta": mq.id_tipo_pregunta,
+                    "tipo_pregunta_nombre": question_type_name,
                     "imported_at": chrono::Utc::now().to_rfc3339(),
                     "import_method": "bulk_import_all",
+                    "has_answers": has_answers,
                 });
-                
+
                 let _ = sqlx::query(
                     r#"
                     INSERT INTO question_bank (
@@ -605,13 +717,13 @@ pub async fn import_all_from_mysql(
                 .bind(&mq.descripcion)
                 .bind(&question_type)
                 .bind(&options)
-                .bind(&serde_json::Value::Null)
+                .bind(&correct_answer)
                 .bind(&source_metadata)
                 .bind(mq.id_pregunta)
                 .bind(mq.id_cursos)
                 .execute(&pool)
                 .await;
-                
+
                 imported_count += 1;
             }
         }
@@ -672,6 +784,8 @@ pub struct MySqlQuestionFull {
     pub nivel_curso: Option<i32>,
     pub id_plan_de_estudios: i32,
     pub plan_nombre: String,
+    pub respuestas_json: Option<String>, // JSON array de respuestas
+    pub tipo_pregunta_nombre: Option<String>, // Nombre del tipo de pregunta
 }
 
 #[derive(Debug, sqlx::FromRow)]
@@ -685,3 +799,101 @@ struct MySqlQuestion {
     id_plan_de_estudios: i32,
     plan_nombre: String,
 }
+
+// ==================== AI Generation ====================
+
+#[derive(Debug, Deserialize)]
+pub struct AIGenerateQuestionPayload {
+    pub question_text: Option<String>,
+    pub question_type: Option<String>,
+    pub difficulty: Option<String>,
+    pub skill: Option<String>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct AIQuestionResponse {
+    pub question_text: String,
+    pub options: Vec<String>,
+    pub correct_answer: serde_json::Value,
+    pub explanation: String,
+}
+
+/// POST /question-bank/ai-generate - Generate question options/answers using AI (Ollama only)
+pub async fn ai_generate_question(
+    _org_ctx: Org,
+    _claims: Claims,
+    Json(payload): Json<AIGenerateQuestionPayload>,
+) -> Result<Json<AIQuestionResponse>, (StatusCode, String)> {
+    use std::env;
+    use std::time::Duration;
+
+    let question_text = payload.question_text.unwrap_or_else(|| "English grammar question".to_string());
+    let difficulty = payload.difficulty.unwrap_or_else(|| "medium".to_string());
+    let skill = payload.skill.unwrap_or_else(|| "grammar".to_string());
+
+    // Build prompt for AI
+    let system_prompt = format!(
+        r#"You are an expert English Teacher creating quiz questions.
+
+        Create a multiple-choice question with the following parameters:
+        - Topic/Context: {}
+        - Difficulty: {}
+        - Skill assessed: {}
+
+        Return ONLY a JSON object with this exact structure:
+        {{
+            "question_text": "The question text here",
+            "options": ["Option A", "Option B", "Option C", "Option D"],
+            "correct_answer": 0,
+            "explanation": "Detailed explanation of why this is correct"
+        }}"#,
+        question_text, difficulty, skill
+    );
+
+    // Call Ollama AI with extended timeout
+    let base_url = env::var("LOCAL_OLLAMA_URL").unwrap_or_else(|_| "http://localhost:11434".to_string());
+    let model = env::var("LOCAL_LLM_MODEL").unwrap_or_else(|_| "llama3.2:3b".to_string());
+    let url = format!("{}/api/chat", base_url);
+    
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(600)) // 10 minutes timeout for slower machines
+        .build()
+        .unwrap_or_else(|_| reqwest::Client::new());
+
+    tracing::info!("Calling Ollama at {} with model {}", url, model);
+
+    let response = client
+        .post(&url)
+        .json(&serde_json::json!({
+            "model": model,
+            "messages": [
+                { "role": "system", "content": system_prompt },
+                { "role": "user", "content": "Generate the question in JSON format" }
+            ],
+            "stream": false,
+            "format": "json"
+        }))
+        .send()
+        .await
+        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("AI request failed: {}", e)))?;
+
+    if !response.status().is_success() {
+        return Err((StatusCode::INTERNAL_SERVER_ERROR, format!("AI API error: {}", response.status())));
+    }
+
+    let result: serde_json::Value = response.json().await
+        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Failed to parse AI response: {}", e)))?;
+
+    // Extract content from Ollama response
+    let content = result
+        .get("message")
+        .and_then(|m| m.get("content"))
+        .and_then(|c| c.as_str())
+        .ok_or((StatusCode::INTERNAL_SERVER_ERROR, "Invalid AI response format".to_string()))?;
+
+    // Parse AI response as JSON
+    let ai_question: AIQuestionResponse = serde_json::from_str(content)
+        .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Failed to parse question JSON: {}", e)))?;
+
+    Ok(Json(ai_question))
+}

services/cms-service/src/handlers_test_templates.rs

@@ -10,6 +10,7 @@ use common::models::{
 use common::{auth::Claims, middleware::Org};
 use serde::Deserialize;
 use sqlx::PgPool;
+use std::time::Duration;
 use uuid::Uuid;
 
 // ==================== Query Parameters ====================
@@ -635,12 +636,16 @@ pub async fn generate_questions_with_rag(
     let mysql_questions: Vec<MySqlQuestion> = if let Some(course_id) = payload.course_id {
         sqlx::query_as(
             r#"
-            SELECT bp.descripcion, bp.idTipoPregunta, c.NombreCurso, pe.Nombre as PlanNombre
+            SELECT 
+                bp.descripcion, 
+                bp.idTipoPregunta AS id_tipo_pregunta, 
+                c.NombreCurso AS nombre_curso, 
+                pe.Nombre as plan_nombre
             FROM bancopreguntas bp
             JOIN curso c ON bp.idCursos = c.idCursos
             JOIN plandeestudios pe ON bp.idPlanDeEstudios = pe.idPlanDeEstudios
             WHERE bp.idCursos = ? AND bp.activo = 1
-            LIMIT 50
+            LIMIT 20
             "#
         )
         .bind(course_id)
@@ -650,114 +655,83 @@ pub async fn generate_questions_with_rag(
     } else {
         sqlx::query_as(
             r#"
-            SELECT bp.descripcion, bp.idTipoPregunta, c.NombreCurso, pe.Nombre as PlanNombre
+            SELECT 
+                bp.descripcion, 
+                bp.idTipoPregunta AS id_tipo_pregunta, 
+                c.NombreCurso AS nombre_curso, 
+                pe.Nombre as plan_nombre
             FROM bancopreguntas bp
             JOIN curso c ON bp.idCursos = c.idCursos
             JOIN plandeestudios pe ON bp.idPlanDeEstudios = pe.idPlanDeEstudios
             WHERE bp.activo = 1
-            LIMIT 50
+            LIMIT 20
             "#
         )
         .fetch_all(&mysql_pool)
         .await
         .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Failed to fetch questions: {}", e)))?
     };
-    
+
     mysql_pool.close().await;
-    
+
     if mysql_questions.is_empty() && payload.course_id.is_some() {
         return Err((StatusCode::NOT_FOUND, "No questions found in MySQL bank for this course".to_string()));
     }
-    
-    // 2. Build RAG context from MySQL questions
+
+    // 2. Build RAG context from MySQL questions (lightweight format)
     let rag_context: String = mysql_questions
         .iter()
-        .map(|q| format!("- {} (Tipo: {}, Curso: {})", q.descripcion, q.id_tipo_pregunta, q.nombre_curso))
+        .take(15) // Use only first 15 for context
+        .map(|q| format!("* {}", q.descripcion))
         .collect::<Vec<_>>()
         .join("\n");
+
+    tracing::info!("RAG context built with {} questions", mysql_questions.len().min(15));
     
-    // 3. Call AI to generate new questions based on RAG context
-    let provider = std::env::var("AI_PROVIDER").unwrap_or_else(|_| "local".to_string());
-    let client = reqwest::Client::new();
-    
-    let (url, auth_header, model) = if provider == "local" {
-        let base_url = std::env::var("LOCAL_OLLAMA_URL").unwrap_or_else(|_| "http://localhost:11434".to_string());
-        let model = std::env::var("LOCAL_LLM_MODEL").unwrap_or_else(|_| "llama3.2:3b".to_string());
-        (
-            format!("{}/v1/chat/completions", base_url),
-            "".to_string(),
-            model,
-        )
-    } else {
-        let api_key = std::env::var("OPENAI_API_KEY")
-            .map_err(|_| (StatusCode::INTERNAL_SERVER_ERROR, "OPENAI_API_KEY not configured".to_string()))?;
-        (
-            "https://api.openai.com/v1/chat/completions".to_string(),
-            format!("Bearer {}", api_key),
-            "gpt-4o".to_string(),
-        )
-    };
+    // 3. Call AI to generate new questions based on RAG context (Ollama only)
+    let base_url = std::env::var("LOCAL_OLLAMA_URL").unwrap_or_else(|_| "http://localhost:11434".to_string());
+    let model = std::env::var("LOCAL_LLM_MODEL").unwrap_or_else(|_| "llama3.2:3b".to_string());
+    let url = format!("{}/api/chat", base_url);
     
+    // Create client with extended timeout for slower Ollama instances
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(600)) // 10 minutes timeout for slower machines
+        .build()
+        .unwrap_or_else(|_| reqwest::Client::new());
+
+    tracing::info!("Calling Ollama at {} with model {}", url, model);
+
+    // Simplified system prompt for better performance on slower machines
     let system_prompt = format!(
-        r#"You are an expert English Teacher creating ORIGINAL quiz questions that assess the FOUR KEY LANGUAGE SKILLS.
-        
-        Below are EXAMPLE questions from our existing question bank. Use them as INSPIRATION ONLY:
-        - Understand the TOPICS, STYLE, and DIFFICULTY LEVEL
-        - Create COMPLETELY NEW questions with different wording, contexts, and answer options
-        - Do NOT copy any question directly
-        - Adapt the concepts to fresh scenarios
-        
-        EXAMPLE QUESTIONS (for inspiration only):
+        r#"You are an English Teacher creating quiz questions.
+
+        Use these examples as inspiration (do NOT copy):
         {}
-        
-        Task: Create {} ORIGINAL multiple-choice questions about: {}
-        
-        IMPORTANT: Each question must assess ONE of these FOUR skills:
-        1. READING - Comprehension, vocabulary in context, text analysis
-        2. LISTENING - Audio comprehension, spoken dialogue understanding
-        3. SPEAKING - Oral production, pronunciation, conversational response
-        4. WRITING - Written production, grammar in writing, composition
-        
-        For EACH question, you MUST:
-        - Specify which skill it assesses (reading/listening/speaking/writing)
-        - Ensure the question actually tests that skill (not just knowledge)
-        - Provide pedagogically sound content for English language learning
-        - Match the difficulty level appropriately
-        
-        Return ONLY a JSON array of questions with this exact structure:
+
+        Create {} ORIGINAL multiple-choice questions about: {}
+
+        Return ONLY a JSON array with this structure:
         [
             {{
-                "question_text": "Your ORIGINAL question text here",
+                "question_text": "Question text",
                 "question_type": "multiple-choice",
-                "options": ["Option A", "Option B", "Option C", "Option D"],
+                "options": ["A", "B", "C", "D"],
                 "correct_answer": 0,
-                "explanation": "Brief explanation of why this is correct. End with: 'Skill assessed: [READING|LISTENING|SPEAKING|WRITING]'",
+                "explanation": "Why this is correct",
                 "points": 1,
                 "skill_assessed": "reading"
             }}
         ]
-        
-        GUIDELINES:
-        - Each question must be UNIQUE - rephrase concepts from examples
-        - Use different vocabulary, scenarios, and contexts
-        - Maintain similar difficulty level to the examples
-        - Ensure correct_answer is the index (0-3) of the correct option
-        - Write clear explanations that teach, not just state the answer
-        - DISTRIBUTE questions across all 4 skills (don't focus on just one)
-        
-        Example transformations by skill:
-        - READING: "Read this passage and answer..." or "What does the word X mean in context?"
-        - LISTENING: "After listening to the dialogue..." or "What would you hear in this situation?"
-        - SPEAKING: "Which response is most appropriate in this conversation?"
-        - WRITING: "Which sentence is grammatically correct?" or "Complete the sentence with..."
-        
-        Be creative while maintaining educational value and ensuring all 4 skills are covered!"#,
+
+        Skills: reading, listening, speaking, writing. Distribute across all 4."#,
         rag_context,
         payload.num_questions.unwrap_or(5),
-        payload.topic.unwrap_or_else(|| "English grammar and vocabulary".to_string())
+        payload.topic.unwrap_or_else(|| "English grammar".to_string())
     );
+
+    tracing::debug!("System prompt length: {} chars", system_prompt.len());
     
-    let mut request = client
+    let request = client
         .post(&url)
         .json(&json!({
             "model": model,
@@ -765,33 +739,38 @@ pub async fn generate_questions_with_rag(
                 {
                     "role": "system",
                     "content": system_prompt
+                },
+                {
+                    "role": "user",
+                    "content": "Generate the questions in valid JSON format."
                 }
             ],
-            "response_format": { "type": "json_object" }
+            "stream": false,
+            "format": "json"  // Ollama native JSON mode
         }));
-    
-    if !auth_header.is_empty() {
-        request = request.header("Authorization", auth_header);
-    }
+
+    tracing::info!("Sending request to Ollama (model: {}, prompt length: {} chars)", model, system_prompt.len());
     
     let response = request.send().await.map_err(|e| {
-        tracing::error!("AI request failed: {}", e);
-        (StatusCode::INTERNAL_SERVER_ERROR, "AI service unavailable".to_string())
+        tracing::error!("AI request failed after timeout: {}", e);
+        (StatusCode::INTERNAL_SERVER_ERROR, format!("Ollama timeout - el equipo t-800 está tardando en responder. Intenta nuevamente: {}", e))
     })?;
-    
+
+    tracing::info!("Ollama response status: {}", response.status());
+
     let response_json: serde_json::Value = response.json().await.map_err(|e| {
         tracing::error!("Failed to parse AI response: {}", e);
         (StatusCode::INTERNAL_SERVER_ERROR, "Invalid AI response".to_string())
     })?;
-    
-    // Parse questions from AI response
+
+    tracing::debug!("Ollama response: {:?}", response_json);
+
+    // Parse questions from Ollama response
     let questions_data = response_json
-        .get("choices")
-        .and_then(|c| c.as_array())
-        .and_then(|choices| choices.first())
-        .and_then(|c| c.get("message"))
+        .get("message")
         .and_then(|m| m.get("content"))
-        .and_then(|content| serde_json::from_str::<serde_json::Value>(content.as_str().unwrap_or("{}")).ok())
+        .and_then(|content| content.as_str())
+        .and_then(|content| serde_json::from_str::<serde_json::Value>(content).ok())
         .and_then(|data| {
             if let Some(questions) = data.get("questions").or(data.get("items")) {
                 questions.as_array().cloned()

services/cms-service/src/main.rs

@@ -351,6 +351,10 @@ async fn main() {
             "/question-bank/import-mysql-all",
             post(handlers_question_bank::import_all_from_mysql),
         )
+        .route(
+            "/question-bank/ai-generate",
+            post(handlers_question_bank::ai_generate_question),
+        )
         // Excel import - pendiente de fix
         // .route(
         //     "/question-bank/import-excel",

shared/common/src/models.rs

@@ -1322,7 +1322,7 @@ pub struct ApplyTemplatePayload {
 
 // ==================== Question Bank ====================
 
-#[derive(Debug, Serialize, Deserialize, sqlx::Type, Clone, PartialEq)]
+#[derive(Debug, Serialize, Deserialize, Clone, Copy, sqlx::Type, PartialEq)]
 #[sqlx(type_name = "question_bank_type")]
 pub enum QuestionBankType {
     #[sqlx(rename = "multiple-choice")]

web/studio/src/components/QuestionBank/QuestionBankEditor.tsx

@@ -147,55 +147,44 @@ export default function QuestionBankEditor({ question, onSuccess, onCancel }: Qu
 
         try {
             setGeneratingAI(true);
-            
-            // AI generation with skill verification
-            const response = await fetch(`${process.env.NEXT_PUBLIC_CMS_API_URL || 'http://localhost:3001'}/lessons/dummy/generate-quiz`, {
+
+            // AI generation con el nuevo endpoint de question bank
+            const token = localStorage.getItem('studio_token');
+            const response = await fetch(`${process.env.NEXT_PUBLIC_CMS_API_URL || 'http://localhost:3001'}/question-bank/ai-generate`, {
                 method: 'POST',
                 headers: {
                     'Content-Type': 'application/json',
-                    'Authorization': `Bearer ${localStorage.getItem('token')}`,
+                    'Authorization': `Bearer ${token}`,
                 },
                 body: JSON.stringify({
-                    context: `Generate a question that assesses ${randomSkill.toUpperCase()} skill. ${skillPrompts[randomSkill as keyof typeof skillPrompts]}
-                    
-                    Base question context: ${formData.question_text}
-                    
-                    IMPORTANT: The question must:
-                    1. Test the ${randomSkill} skill specifically
-                    2. Be pedagogically sound for English language learning
-                    3. Include clear options and a thorough explanation
-                    4. Be appropriate for the difficulty level: ${formData.difficulty}
-                    
-                    Return the question with options and explanation.`,
-                    quiz_type: formData.question_type,
+                    question_text: formData.question_text,
+                    difficulty: formData.difficulty,
+                    skill: randomSkill,
                 }),
             });
 
             if (!response.ok) {
-                throw new Error('Error al generar con IA');
+                const errorText = await response.text();
+                throw new Error(`Error ${response.status}: ${errorText}`);
             }
 
             const data = await response.json();
-            
-            if (data.blocks && data.blocks.length > 0) {
-                const block = data.blocks[0];
-                if (block.quiz_data && block.quiz_data.questions && block.quiz_data.questions.length > 0) {
-                    const aiQuestion = block.quiz_data.questions[0];
-                    setFormData({
-                        ...formData,
-                        options: aiQuestion.options || formData.options,
-                        correct_answer: aiQuestion.correct,
-                        explanation: aiQuestion.explanation 
-                            ? `${aiQuestion.explanation}\n\n📊 Skill assessed: ${randomSkill.toUpperCase()}`
-                            : `This question assesses ${randomSkill.toUpperCase()} skills.`,
-                        tags: [...(formData.tags || []), randomSkill, 'ai-generated'],
-                    });
-                    alert(`IA generó las opciones y explicación enfocadas en la habilidad: ${randomSkill.toUpperCase()}`);
-                }
+
+            if (data.options && data.correct_answer !== undefined) {
+                setFormData({
+                    ...formData,
+                    options: data.options,
+                    correct_answer: data.correct_answer,
+                    explanation: data.explanation
+                        ? `${data.explanation}\n\n📊 Skill assessed: ${randomSkill.toUpperCase()}`
+                        : `This question assesses ${randomSkill.toUpperCase()} skills.`,
+                    tags: [...(formData.tags || []), randomSkill, 'ai-generated'],
+                });
+                alert(`IA generó las opciones y explicación enfocadas en la habilidad: ${randomSkill.toUpperCase()}`);
             }
         } catch (error) {
             console.error('AI generation error:', error);
-            alert('Error al generar con IA. Asegúrate de tener Ollama configurado.');
+            alert(`Error al generar con IA: ${error instanceof Error ? error.message : 'Verifica que Ollama esté configurado'}`);
         } finally {
             setGeneratingAI(false);
         }

web/studio/src/components/TestTemplates/TestTemplateForm.tsx

@@ -163,65 +163,61 @@ export default function TestTemplateForm({ onSuccess, onCancel }: TestTemplateFo
         setExpandedQuestion(newQuestion.id);
     };
 
-    const handleUpdateQuestion = (questionId: string, updates: Partial<Question>) => {
-        setQuestions(questions.map(q => q.id === questionId ? { ...q, ...updates } : q));
-    };
-
-    const handleRemoveQuestion = (questionId: string) => {
-        setQuestions(questions.filter(q => q.id !== questionId));
-    };
-
     const handleGenerateWithAI = async () => {
         if (!aiContext.trim()) {
             alert('Ingresa el contexto para generar las preguntas (ej: tema de la lección, contenido, etc.)');
             return;
         }
 
+        const token = localStorage.getItem('studio_token');
+        if (!token) {
+            alert('No hay sesión activa. Por favor inicia sesión nuevamente.');
+            return;
+        }
+
         try {
             setGeneratingAI(true);
-            
-            // Usar el endpoint de generación de quiz existente
-            const response = await fetch(`${process.env.NEXT_PUBLIC_CMS_API_URL || 'http://localhost:3001'}/lessons/dummy/generate-quiz`, {
+
+            // Usar el endpoint RAG de generación de preguntas desde banco MySQL
+            const response = await fetch(`${process.env.NEXT_PUBLIC_CMS_API_URL || 'http://localhost:3001'}/test-templates/generate-with-rag`, {
                 method: 'POST',
                 headers: {
                     'Content-Type': 'application/json',
-                    'Authorization': `Bearer ${localStorage.getItem('token')}`,
+                    'Authorization': `Bearer ${token}`,
                 },
                 body: JSON.stringify({
-                    context: aiContext,
-                    quiz_type: 'multiple-choice',
+                    topic: aiContext,
+                    num_questions: 5,
                 }),
             });
 
             if (!response.ok) {
-                throw new Error('Error al generar con IA');
+                const errorText = await response.text();
+                throw new Error(`Error ${response.status}: ${errorText}`);
             }
 
-            const data = await response.json();
-            
+            const generatedQuestions = await response.json();
+
             // Parsear las preguntas generadas
-            if (data.blocks && data.blocks.length > 0) {
-                const block = data.blocks[0];
-                if (block.quiz_data && block.quiz_data.questions) {
-                    const generatedQuestions: Question[] = block.quiz_data.questions.map((q: any, idx: number) => ({
-                        id: `q-${Date.now()}-${idx}`,
-                        section_id: undefined,
-                        question_order: idx,
-                        question_type: q.type || 'multiple-choice',
-                        question_text: q.question,
-                        options: q.options,
-                        correct_answer: q.correct,
-                        explanation: q.explanation || '',
-                        points: 1,
-                    }));
-                    
-                    setQuestions([...questions, ...generatedQuestions]);
-                    alert(`Se generaron ${generatedQuestions.length} preguntas con IA`);
-                }
+            if (Array.isArray(generatedQuestions) && generatedQuestions.length > 0) {
+                const questionsToAdd: Question[] = generatedQuestions.map((q: any, idx: number) => ({
+                    id: `q-${Date.now()}-${idx}`,
+                    section_id: undefined,
+                    question_order: questions.length + idx,
+                    question_type: q.question_type || 'multiple-choice',
+                    question_text: q.question_text || q.text,
+                    options: q.options || [],
+                    correct_answer: q.correct_answer || q.correct,
+                    explanation: q.explanation || '',
+                    points: q.points || 1,
+                }));
+
+                setQuestions([...questions, ...questionsToAdd]);
+                alert(`Se generaron ${questionsToAdd.length} preguntas con IA`);
             }
         } catch (error) {
             console.error('AI generation error:', error);
-            alert('Error al generar preguntas con IA. Asegúrate de tener Ollama configurado.');
+            alert(`Error al generar preguntas con IA: ${error instanceof Error ? error.message : 'Verifica que Ollama esté configurado y el banco de preguntas MySQL tenga datos'}`);
         } finally {
             setGeneratingAI(false);
         }