feat(McqGeneration): add MCQ generation

Author: craigtonlian

app/controllers/course/assessment/question/multiple_responses_controller.rb

@@ -14,7 +14,12 @@ def new
 
   def create
     if @multiple_response_question.save
-      render json: { redirectUrl: course_assessment_path(current_course, @assessment) }
+      render json: {
+        redirectUrl: course_assessment_path(current_course, @assessment),
+        redirectEditUrl: edit_course_assessment_question_multiple_response_path(
+          current_course, @assessment, @multiple_response_question
+        )
+      }
     else
       render json: { errors: @multiple_response_question.errors }, status: :bad_request
     end
@@ -32,7 +37,12 @@ def update
     update_skill_ids_if_params_present(multiple_response_question_params[:question_assessment])
 
     if update_multiple_response_question
-      render json: { redirectUrl: course_assessment_path(current_course, @assessment) }
+      render json: {
+        redirectUrl: course_assessment_path(current_course, @assessment),
+        redirectEditUrl: edit_course_assessment_question_multiple_response_path(
+          current_course, @assessment, @multiple_response_question
+        )
+      }
     else
       render json: { errors: @multiple_response_question.errors }, status: :bad_request
     end
@@ -50,93 +60,25 @@ def destroy
   end
 
   def generate
-    # Parse the form data
-    custom_prompt = params[:custom_prompt] || ''
-    number_of_questions = (params[:number_of_questions] || 1).to_i
-    
-    # Parse source_question_data from JSON string
-    source_question_data = {}
-    if params[:source_question_data].present?
-      begin
-        source_question_data = JSON.parse(params[:source_question_data])
-      rescue JSON::ParserError => e
-        Rails.logger.warn "Failed to parse source_question_data: #{e.message}"
-        source_question_data = {}
-      end
-    end
-
-    # Validate parameters
-    if custom_prompt.blank?
-      render json: { success: false, message: 'Custom prompt is required' }, status: :bad_request
-      return
-    end
+    generation_params = parse_generation_params
 
-    if number_of_questions < 1 || number_of_questions > 3
-      render json: { success: false, message: 'Number of questions must be between 1 and 3' }, status: :bad_request
+    unless validate_generation_params(generation_params)
+      render json: { success: false, message: 'Invalid parameters' }, status: :bad_request
       return
     end
 
-    # Create generation service
-    generation_service = Course::Assessment::Question::MrqGenerationService.new(
-      @assessment,
-      {
-        custom_prompt: custom_prompt,
-        number_of_questions: number_of_questions,
-        source_question_data: source_question_data
-      }
-    )
-
-    # Generate questions
+    generation_service = Course::Assessment::Question::MrqGenerationService.new(@assessment, generation_params)
     generated_questions = generation_service.generate_questions
-    # Transform the response to match the expected frontend format
     questions = generated_questions['questions'] || []
 
     if questions.empty?
-      render json: { success: false, message: 'No questions were generated' }, status: :internal_server_error
+      render json: { success: false, message: 'No questions were generated' }, status: :bad_request
       return
     end
-    # Format response for frontend
-    response_data = {
-      success: true,
-      data: {
-        title: questions.first['title'],
-        description: questions.first['description'],
-        options: questions.first['options'].map.with_index do |option, index|
-          {
-            id: index + 1,
-            option: option['option'],
-            correct: option['correct'],
-            weight: index + 1,
-            explanation: option['explanation'] || '',
-            ignoreRandomization: false,
-            toBeDeleted: false
-          }
-        end,
-        allQuestions: questions.map.with_index do |question, q_index|
-          {
-            title: question['title'],
-            description: question['description'],
-            options: question['options'].map.with_index do |option, index|
-              {
-                id: index + 1,
-                option: option['option'],
-                correct: option['correct'],
-                weight: index + 1,
-                explanation: option['explanation'] || '',
-                ignoreRandomization: false,
-                toBeDeleted: false
-              }
-            end
-          }
-        end,
-        numberOfQuestions: questions.length
-      }
-    }
 
-    render json: response_data, status: :ok
+    render json: format_generation_response(questions), status: :ok
   rescue StandardError => e
-    Rails.logger.error "MRQ Generation Error: #{e.message}"
-    Rails.logger.error e.backtrace.join("\n")
+    Rails.logger.error "MCQ/MRQ Generation Error: #{e.message}"
     render json: { success: false, message: 'An error occurred while generating questions' },
            status: :internal_server_error
   end
@@ -177,4 +119,62 @@ def multiple_response_question_params
   def load_question_assessment
     @question_assessment = load_question_assessment_for(@multiple_response_question)
   end
+
+  def parse_generation_params
+    {
+      custom_prompt: params[:custom_prompt] || '',
+      number_of_questions: (params[:number_of_questions] || 1).to_i,
+      question_type: params[:question_type],
+      source_question_data: parse_source_question_data
+    }
+  end
+
+  def parse_source_question_data
+    return {} unless params[:source_question_data].present?
+
+    JSON.parse(params[:source_question_data])
+  rescue JSON::ParserError
+    {}
+  end
+
+  def validate_generation_params(params)
+    params[:custom_prompt].present? &&
+      params[:number_of_questions] >= 1 && params[:number_of_questions] <= 10 &&
+      %w[mrq mcq].include?(params[:question_type])
+  end
+
+  def format_generation_response(questions)
+    {
+      success: true,
+      data: {
+        title: questions.first['title'],
+        description: questions.first['description'],
+        options: format_options(questions.first['options']),
+        allQuestions: questions.map { |question| format_question(question) },
+        numberOfQuestions: questions.length
+      }
+    }
+  end
+
+  def format_options(options)
+    options.map.with_index do |option, index|
+      {
+        id: index + 1,
+        option: option['option'],
+        correct: option['correct'],
+        weight: index + 1,
+        explanation: option['explanation'] || '',
+        ignoreRandomization: false,
+        toBeDeleted: false
+      }
+    end
+  end
+
+  def format_question(question)
+    {
+      title: question['title'],
+      description: question['description'],
+      options: format_options(question['options'])
+    }
+  end
 end

app/services/course/assessment/question/mrq_generation_service.rb

@@ -1,66 +1,97 @@
 # frozen_string_literal: true
 class Course::Assessment::Question::MrqGenerationService
   @output_schema = JSON.parse(
-    File.read('app/services/course/assessment/question/prompts/mrq_generation_output_format.json')
+    File.read('app/services/course/assessment/question/prompts/mcq_mrq_generation_output_format.json')
   )
   @output_parser = Langchain::OutputParsers::StructuredOutputParser.from_json_schema(
     @output_schema
   )
-  @system_prompt = Langchain::Prompt.load_from_path(
+  @mrq_system_prompt = Langchain::Prompt.load_from_path(
     file_path: 'app/services/course/assessment/question/prompts/mrq_generation_system_prompt.json'
   )
-  @user_prompt = Langchain::Prompt.load_from_path(
+  @mrq_user_prompt = Langchain::Prompt.load_from_path(
     file_path: 'app/services/course/assessment/question/prompts/mrq_generation_user_prompt.json'
   )
+  @mcq_system_prompt = Langchain::Prompt.load_from_path(
+    file_path: 'app/services/course/assessment/question/prompts/mcq_generation_system_prompt.json'
+  )
+  @mcq_user_prompt = Langchain::Prompt.load_from_path(
+    file_path: 'app/services/course/assessment/question/prompts/mcq_generation_user_prompt.json'
+  )
   @llm = LANGCHAIN_OPENAI
 
   class << self
-    attr_reader :system_prompt, :user_prompt, :output_schema, :output_parser
+    attr_reader :output_schema, :output_parser,
+                :mrq_system_prompt, :mrq_user_prompt, :mcq_system_prompt, :mcq_user_prompt
     attr_accessor :llm
   end
 
+  # Initializes the MRQ generation service with assessment and parameters.
+  # @param [Course::Assessment] assessment The assessment to generate questions for.
+  # @param [Hash] params Parameters for question generation.
+  # @option params [String] :custom_prompt Custom instructions for the LLM.
+  # @option params [Integer] :number_of_questions Number of questions to generate.
+  # @option params [Hash] :source_question_data Data from an existing question to base new questions on.
+  # @option params [String] :question_type Type of question to generate ('mrq' or 'mcq').
   def initialize(assessment, params)
     @assessment = assessment
     @params = params
     @custom_prompt = params[:custom_prompt].to_s
-    @number_of_questions = params[:number_of_questions].to_i || 1
+    @number_of_questions = (params[:number_of_questions] || 1).to_i
     @source_question_data = params[:source_question_data]
+    @question_type = params[:question_type] || 'mrq'
   end
 
-  # Calls the LLM service to generate MRQ questions.
+  # Calls the LLM service to generate MRQ or MCQ questions.
   # @return [Hash] The LLM's generation response containing multiple questions.
   def generate_questions
-    formatted_system_prompt = self.class.system_prompt.format
-    formatted_user_prompt = self.class.user_prompt.format(
-      custom_prompt: @custom_prompt,
-      number_of_questions: @number_of_questions,
-      source_question_title: @source_question_data&.dig(:title) || '',
-      source_question_description: @source_question_data&.dig(:description) || '',
-      source_question_options: format_source_options(@source_question_data&.dig(:options) || [])
-    )
-
-    messages = [
-      { role: 'system', content: formatted_system_prompt },
-      { role: 'user', content: formatted_user_prompt }
-    ]
+    messages = build_messages
 
     response = self.class.llm.chat(
       messages: messages,
       response_format: {
         type: 'json_schema',
         json_schema: {
-          name: 'mrq_generation_output',
+          name: 'mcq_mrq_generation_output',
           strict: true,
           schema: self.class.output_schema
         }
       }
     ).completion
-
     parse_llm_response(response)
   end
 
   private
 
+  # Builds the messages array from system and user prompt for the LLM chat
+  # @return [Array<Hash>] Array of messages formatted for the LLM chat
+  def build_messages
+    system_prompt, user_prompt = select_prompts
+    formatted_system_prompt = system_prompt.format
+    formatted_user_prompt = user_prompt.format(
+      custom_prompt: @custom_prompt,
+      number_of_questions: @number_of_questions,
+      source_question_title: @source_question_data&.dig(:title) || '',
+      source_question_description: @source_question_data&.dig(:description) || '',
+      source_question_options: format_source_options(@source_question_data&.dig(:options) || [])
+    )
+
+    [
+      { role: 'system', content: formatted_system_prompt },
+      { role: 'user', content: formatted_user_prompt }
+    ]
+  end
+
+  # Selects the appropriate prompts based on the question type
+  # @return [Array] Array containing system and user prompts
+  def select_prompts
+    if @question_type == 'mcq'
+      [self.class.mcq_system_prompt, self.class.mcq_user_prompt]
+    else
+      [self.class.mrq_system_prompt, self.class.mrq_user_prompt]
+    end
+  end
+
   # Formats source question options for inclusion in the LLM prompt
   # @param [Array] options The source question options
   # @return [String] Formatted string representation of options

app/services/course/assessment/question/prompts/mcq_generation_system_prompt.json

@@ -0,0 +1,5 @@
+{
+  "_type": "prompt",
+  "input_variables": ["format_instructions"],
+  "template": "You are an expert educational content creator specializing in multiple choice questions (MCQ).\n\nYour task is to generate high-quality multiple choice questions based on the provided instructions and context.\n\nKey requirements for MCQ generation:\n1. Create questions that have exactly ONE correct answer. Each question should have only one option that is correct.\n2. Ensure all options are plausible and well-written\n3. Try to create 4 options per question. If that is not feasible, ensure at least 2 options are provided.\n4. Questions should be clear, concise, and educational\n5. Options should be mutually exclusive and cover different aspects\n6. Avoid obvious incorrect answers\n7. Use an appropriate difficulty level for the target audience\n8. Make sure distractors (incorrect options) are plausible but clearly wrong\n\nWhen provided with a source question, you may use it as inspiration or reference, but create original questions.\n\n{format_instructions}"
+}

app/services/course/assessment/question/prompts/mcq_generation_user_prompt.json

@@ -0,0 +1,11 @@
+{
+  "_type": "prompt",
+  "input_variables": [
+    "custom_prompt",
+    "number_of_questions",
+    "source_question_title",
+    "source_question_description",
+    "source_question_options"
+  ],
+  "template": "Please generate {number_of_questions} multiple choice question(s) based on the following instructions:\n\nCustom Instructions: {custom_prompt}\n\nSource Question Context (for reference only):\nTitle: {source_question_title}\nDescription: {source_question_description}\nOptions:\n{source_question_options}\n\nGenerate {number_of_questions} high-quality multiple choice question(s) that:\n- Have clear, educational content\n- Include at least 2 options per question\n- Have exactly ONE correct answer per question\n- Are appropriate for educational assessment\n- Follow the custom instructions provided\n\nEach question should be original and well-structured for educational use."
+}

app/services/course/assessment/question/prompts/mrq_generation_output_format.json renamed to app/services/course/assessment/question/prompts/mcq_mrq_generation_output_format.json

@@ -13,7 +13,7 @@
           },
           "description": {
             "type": "string",
-            "description": "The question description"
+            "description": "The description of the question"
           },
           "options": {
             "type": "array",
@@ -30,13 +30,13 @@
                 },
                 "explanation": {
                   "type": "string",
-                  "description": "Explanation for why this option is correct or incorrect"
+                  "description": "Highly detailed explanation for why this option is correct or incorrect"
                 }
               },
               "required": ["option", "correct", "explanation"],
               "additionalProperties": false
             },
-            "description": "Array of 4-6 options for the question"
+            "description": "Array of at least 2 options for the question"
           }
         },
         "required": ["title", "description", "options"],

app/services/course/assessment/question/prompts/mrq_generation_system_prompt.json

@@ -1,5 +1,5 @@
 {
   "_type": "prompt",
   "input_variables": ["format_instructions"],
-  "template": "You are an expert educational content creator specializing in multiple response questions (MRQ).\n\nYour task is to generate high-quality multiple response questions based on the provided instructions and context.\n\nKey requirements for MRQ generation:\n1. Create questions that may have one or more correct answers. It’s acceptable for some questions to have only one correct answer, or for options like \"None of the above\" to be correct.\n2. Ensure all options are plausible and well-written\n3. Include 2-6 options per question\n4. Questions should be clear, concise, and educational\n5. Options should be mutually exclusive when possible\n6. Avoid obvious incorrect answers\n7. Use an appropriate difficulty level for the target audience\n\nWhen provided with a source question, you may use it as inspiration or reference, but create original questions.\n\n{format_instructions}"
+  "template": "You are an expert educational content creator specializing in multiple response questions (MRQ).\n\nYour task is to generate high-quality multiple response questions based on the provided instructions and context.\n\nKey requirements for MRQ generation:\n1. Create questions that may have one or more correct answers. It is acceptable for some questions to have only one correct answer, or for options like \"None of the above\" to be correct.\n2. Ensure all options are plausible and well-written\n3. Try to create 4 options per question. If that is not feasible, ensure at least 2 options are provided.\n4. Questions should be clear, concise, and educational\n5. Options should be mutually exclusive when possible\n6. Avoid obvious incorrect answers\n7. Use an appropriate difficulty level for the target audience\n\nWhen provided with a source question, you may use it as inspiration or reference, but create original questions.\n\n{format_instructions}"
 }

app/services/course/assessment/question/prompts/mrq_generation_user_prompt.json

@@ -7,5 +7,5 @@
     "source_question_description",
     "source_question_options"
   ],
-  "template": "Please generate {number_of_questions} multiple response question(s) based on the following instructions:\n\nCustom Instructions: {custom_prompt}\n\nSource Question Context (for reference only):\nTitle: {source_question_title}\nDescription: {source_question_description}\nOptions:\n{source_question_options}\n\nGenerate {number_of_questions} high-quality multiple response question(s) that:\n- Have clear, educational content\n- Include 4-6 options per question\n- Have multiple correct answers (at least 2)\n- Are appropriate for educational assessment\n- Follow the custom instructions provided\n\nEach question should be original and well-structured for educational use."
+  "template": "Please generate {number_of_questions} multiple response question(s) based on the following instructions:\n\nCustom Instructions: {custom_prompt}\n\nSource Question Context (for reference only):\nTitle: {source_question_title}\nDescription: {source_question_description}\nOptions:\n{source_question_options}\n\nGenerate {number_of_questions} high-quality multiple response question(s) that:\n- Have clear, educational content\n- Include at least 2 options per question\n- Are appropriate for educational assessment\n- Follow the custom instructions provided\n\nEach question should be original and well-structured for educational use."
 }

app/views/course/assessment/assessments/show.json.jbuilder

@@ -174,12 +174,12 @@ if can_observe
     end
 
     json.generateQuestionUrls [
-      # {
-      #   type: 'MultipleChoice',
-      #   url: generate_course_assessment_question_multiple_responses_path(current_course, assessment, {
-      #     multiple_choice: true
-      #   })
-      # },
+      {
+        type: 'MultipleChoice',
+        url: generate_course_assessment_question_multiple_responses_path(current_course, assessment, {
+          multiple_choice: true
+        })
+      },
       {
         type: 'MultipleResponse',
         url: generate_course_assessment_question_multiple_responses_path(current_course, assessment)