Update prompts.py

prompts.py

@@ -1,229 +1,54 @@
-"""
-Prompt templates for the multi-agent exam generation system
-"""
-
-# Agent 1 - Generator Prompt (Llama 3.1 70B)
-GENERATOR_PROMPT = """
-You are an expert exam paper generator for engineering education. Create a structured question paper based on the following inputs:
-
-SUBJECT: {subject}
-STREAM: {stream}
-SYLLABUS: {syllabus_text}
-REFERENCE CONTEXT: {reference_text}
-REALTIME UPDATES: {realtime_updates}
-
-QUESTION DISTRIBUTION:
-- Part A: {part_a_count} questions × 2 marks each
-- Part B: {part_b_count} questions × 13 marks each (Either/Or pattern)
-- Part C: {part_c_count} questions × 14 marks each (Case studies)
-
-CRITICAL REQUIREMENTS:
-1. Difficulty Index: Maintain between 1.8-2.5
-2. Unit Distribution: Even coverage across all syllabus units
-3. Bloom's Taxonomy: {bloom_mix}
-4. Tags: {tag_requirements}
-
-{stream_specific_template}
+def build_master_prompt(subject, stream, partA, partB, partC, syllabus, refqp, realtime):
 
-OUTPUT FORMAT - MUST BE VALID JSON:
-{{
-  "metadata": {{
-    "subject": "{subject}",
-    "stream": "{stream}",
-    "difficulty_index": 2.1,
-    "total_marks": {total_marks},
-    "units_covered": [1, 2, 3, 4, 5]
-  }},
-  "questions": [
-    {{
-      "part": "A",
-      "question_no": 1,
-      "sub_no": null,
-      "marks": 2,
-      "unit": 1,
-      "bloom_level": "Remember",
-      "tags": ["{tag_example}"],
-      "course_outcome": "CO1",
-      "question_text": "Define key concept from unit 1"
-    }},
-    {{
-      "part": "B",
-      "question_no": 1,
-      "sub_no": "a",
-      "marks": 13,
-      "unit": 2,
-      "bloom_level": "Apply",
-      "tags": ["{tag_example}"],
-      "course_outcome": "CO2",
-      "question_text": "Explain concept with example OR Solve this problem"
-    }},
-    {{
-      "part": "C",
-      "question_no": 1,
-      "sub_no": null,
-      "marks": 14,
-      "unit": 3,
-      "bloom_level": "Evaluate",
-      "tags": ["Case Study", "{tag_example}"],
-      "course_outcome": "CO3",
-      "question_text": "Analyze the given case study and provide solutions"
-    }}
-  ]
-}}
-
-Generate exactly {total_questions} questions following this structure. Ensure even unit distribution and proper bloom level mixing.
+    if stream == "CSE":
+        template = """
+You are an expert in CSE academic content creation aligned with MAANGO BIG15.
+Generate:
+- Part A: {partA} × 2 marks
+- Part B: {partB} × 16 marks (Either/Or)
+- Part C: {partC} × 16 marks (Case-based)
+Tag each question:
+(Bloom's Level: <x> | Unit: <n> | Company Tag: <company>)
 """
-
-# Agent 2 - Verifier Prompt (Gemma 2 27B)
-VERIFIER_PROMPT = """
-You are a quality verification agent for exam papers. Analyze the generated question paper and identify issues:
-
-GENERATED CONTENT:
-{generated_content}
-
-VERIFICATION CHECKLIST:
-1. Bloom's Taxonomy Correctness: {bloom_mix}
-2. Unit Distribution: Even across all syllabus units
-3. Question Count: Part A: {part_a_count}, Part B: {part_b_count}, Part C: {part_c_count}
-4. Tag Completeness: {tag_requirements}
-5. Difficulty Index: Between 1.8-2.5
-6. JSON Validity: Proper structure and formatting
-7. Duplicate Check: No repeated concepts
-8. Ambiguity Check: Clear, unambiguous questions
-
-OUTPUT FORMAT:
-{{
-  "status": "valid|needs_correction",
-  "corrections": [
-    {{
-      "target": "question_1",
-      "issue": "Bloom level incorrect",
-      "fix": "Change from 'Remember' to 'Apply'",
-      "priority": "high|medium|low"
-    }}
-  ],
-  "summary": {{
-    "unit_coverage_score": "X/Y units covered",
-    "bloom_distribution": {{"Remember": "X%", "Understand": "Y%", ...}},
-    "difficulty_estimate": 2.1,
-    "overall_quality": "excellent|good|needs_improvement"
-  }}
-}}
-
-Provide specific, actionable corrections.
+    else:
+        template = """
+You are an expert academic creator for Mechanical/Electrical/Electronics (Non-CSE).
+Generate:
+- Part A: {partA} × 2 marks
+- Part B: {partB} × 13 marks (Either/Or)
+- Part C: {partC} × 14 marks (Case-based)
+Tag each question:
+(Bloom's Level: <x> | Unit: <n> | GATE Reference: <year>)
 """
 
-# Agent 3 - Formatter Prompt (Mixtral-8x7B)
-FORMATTER_PROMPT = """
-You are the final formatting and output agent. Take the verified content and produce the final structured output.
-
-ORIGINAL CONTENT:
-{original_content}
+    return f"""
+{template.format(partA=partA, partB=partB, partC=partC)}
 
-VERIFICATION CORRECTIONS:
-{corrections}
+Subject: {subject}
 
-FINAL OUTPUT REQUIREMENTS:
-1. Apply all corrections from verification
-2. Ensure valid JSON structure
-3. Generate three complete blocks:
-   - Final Question Paper
-   - Answer Key
-   - OBE Summary
+Syllabus:
+{syllabus[:20000]}
 
-ANSWER KEY FORMAT:
-For each question, provide:
-- Model answer
-- Step-by-step solution (where applicable)
-- Marking scheme breakdown
+Reference QP:
+{refqp[:10000]}
 
-OBE SUMMARY FORMAT:
-- Course outcome mapping
-- Bloom's taxonomy distribution
-- Difficulty analysis
-- Unit coverage report
+Real-time context:
+{realtime}
 
-OUTPUT STRUCTURE:
+Generate two outputs:
+1. Printable QP
+2. VALID JSON with key "questions", containing list of items:
 {{
-  "final_qp": {original_content},
-  "answers": [
-    {{
-      "question_ref": "A1",
-      "model_answer": "Detailed answer here...",
-      "marking_scheme": ["Point 1: 1 mark", "Point 2: 1 mark"],
-      "bloom_level": "Remember",
-      "course_outcome": "CO1"
-    }}
-  ],
-  "obe": {{
-    "course_outcomes": {{
-      "CO1": {{"coverage": "excellent", "questions": ["A1", "B1a"]}},
-      "CO2": {{"coverage": "good", "questions": ["A2", "B2a"]}}
-    }},
-    "bloom_distribution": {{
-      "Remember": "30%",
-      "Understand": "25%", 
-      "Apply": "20%",
-      "Analyze": "15%",
-      "Evaluate": "10%"
-    }},
-    "difficulty_index": 2.1,
-    "unit_coverage": "5/5 units covered",
-    "recommendations": "Suggestions for improvement"
-  }}
+"part": "A/B/C",
+"question_no": int,
+"sub_no": str or "",
+"marks": int,
+"unit": int,
+"course_outcome": str,
+"bloom_level": str,
+"tags": str,
+"question_text": str
 }}
-"""
-
-# Stream-specific templates
-CSE_TEMPLATE = """
-CSE-SPECIFIC REQUIREMENTS:
-- Company Tags: MAANGO BIG15 (Microsoft, Amazon, Apple, Netflix, Google, Oracle, Bloomberg, IBM, Goldman Sachs, etc.)
-- Focus: Real-world coding problems, system design, algorithms
-- Part B: Either/Or should include coding problems vs theory questions
-- Part C: Case studies from recent tech industry scenarios
-- Bloom's Mix: 60% Remember/Understand, 40% Apply/Analyze/Evaluate
-"""
 
-NON_CSE_TEMPLATE = """
-NON-CSE SPECIFIC REQUIREMENTS:
-- GATE Reference Tags: All questions must reference GATE patterns
-- Focus: Fundamental concepts, problem-solving, theoretical understanding  
-- Part B: Either/Or should include derivation vs application problems
-- Part C: Engineering case studies with real-world applications
-- Bloom's Mix: 50% Remember/Understand, 50% Apply/Analyze/Evaluate
+JSON MUST appear at the bottom of answer.
 """
-
-def get_generator_prompt(subject, stream, syllabus_text, reference_text, realtime_updates,
-                        part_a_count, part_b_count, part_c_count):
-    """Build the complete generator prompt"""
-    
-    total_marks = (part_a_count * 2) + (part_b_count * 13) + (part_c_count * 14)
-    total_questions = part_a_count + (part_b_count * 2) + part_c_count
-    
-    if stream == "CSE":
-        stream_template = CSE_TEMPLATE
-        bloom_mix = "60% Remember/Understand, 40% Apply/Analyze/Evaluate"
-        tag_requirements = "MAANGO BIG15 company tags required"
-        tag_example = "Amazon"
-    else:
-        stream_template = NON_CSE_TEMPLATE  
-        bloom_mix = "50% Remember/Understand, 50% Apply/Analyze/Evaluate"
-        tag_requirements = "GATE reference tags required"
-        tag_example = "GATE-2024"
-    
-    return GENERATOR_PROMPT.format(
-        subject=subject,
-        stream=stream,
-        syllabus_text=syllabus_text[:2000],  # Limit length
-        reference_text=reference_text[:1500],
-        realtime_updates=realtime_updates,
-        part_a_count=part_a_count,
-        part_b_count=part_b_count,
-        part_c_count=part_c_count,
-        total_marks=total_marks,
-        total_questions=total_questions,
-        bloom_mix=bloom_mix,
-        tag_requirements=tag_requirements,
-        stream_specific_template=stream_template,
-        tag_example=tag_example
-    )