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Doubt Solver - Resolves student questions using Chain-of-Thought reasoning
"""
from typing import Tuple, Dict
from config import LLM_PROVIDER
from core.llm_engine import LLMEngine
from core.prompt_builder import PromptBuilder
from core.validator import InputValidator, ContentValidator
from core.utils import log_event, truncate_text
import re
class DoubtSolver:
"""Solves student doubts using step-by-step reasoning."""
def __init__(self, llm_provider: str = LLM_PROVIDER):
"""
Initialize doubt solver.
Args:
llm_provider: LLM provider to use
"""
self.engine = LLMEngine(llm_provider)
self.prompt_builder = PromptBuilder()
self.validator = InputValidator()
def solve(
self,
question: str,
context: str = "",
mode: str = "normal",
use_cot: bool = True
) -> Tuple[bool, str]:
"""
Solve a student's doubt with reasoning.
Args:
question: Student's doubt/question
context: Relevant material context
mode: Response mode
use_cot: Use Chain-of-Thought reasoning
Returns:
Tuple of (success, solution)
"""
# Validate input
is_valid, msg = self.validator.validate_input(question)
if not is_valid:
log_event("VALIDATION_ERROR", f"DoubtSolver: {msg}")
return False, msg
# Build prompt
try:
prompt = self.prompt_builder.build_doubt_solver_prompt(
question,
context=truncate_text(context, 3000) if context else "",
mode=mode
)
if use_cot:
# Add Chain-of-Thought emphasis
prompt = f"""{prompt}
IMPORTANT: Use Chain-of-Thought reasoning.
1. Break down the question into parts
2. Think through each part step by step
3. Show your reasoning clearly
4. Verify your answer
5. Provide the final answer
"""
log_event("PROMPT_BUILT", "Doubt solver prompt ready")
except Exception as e:
log_event("PROMPT_ERROR", f"Error building doubt solver: {str(e)}")
return False, f"Error: {str(e)}"
# Generate solution
success, solution = self.engine.generate(prompt, max_tokens=2000)
if not success:
log_event("DOUBT_SOLVER_ERROR", solution)
return False, solution
# Quality check
is_meaningful = ContentValidator.is_meaningful_response(solution, min_words=20)
if not is_meaningful:
log_event("QUALITY_CHECK_FAILED", "Solution too short")
return False, "Response too short. Please try again."
quality_score = ContentValidator.estimate_quality(solution)
log_event("QUALITY_SCORE", f"Solution quality: {quality_score:.2f}")
log_event("DOUBT_SOLVED", f"Solution provided")
return True, solution
def parse_solution(self, solution_text: str) -> Dict:
"""
Parse solution into structured components.
Args:
solution_text: Raw solution text
Returns:
Dictionary with thinking, answer, insights
"""
parsed = {
"thinking": "",
"answer": "",
"insights": "",
"raw_text": solution_text
}
# Extract THINKING section
thinking_match = re.search(
r'(?:THINKING|Step|Reasoning):\s*(.+?)(?=ANSWER|Final|$)',
solution_text,
re.IGNORECASE | re.DOTALL
)
if thinking_match:
parsed["thinking"] = thinking_match.group(1).strip()
# Extract ANSWER section
answer_match = re.search(
r'(?:ANSWER|Final Answer):\s*(.+?)(?=INSIGHTS|Additional|$)',
solution_text,
re.IGNORECASE | re.DOTALL
)
if answer_match:
parsed["answer"] = answer_match.group(1).strip()
else:
# If no explicit answer, use last paragraph
paragraphs = solution_text.split('\n\n')
if paragraphs:
parsed["answer"] = paragraphs[-1].strip()
# Extract INSIGHTS section
insights_match = re.search(
r'(?:INSIGHTS|Additional|Tips):\s*(.+?)$',
solution_text,
re.IGNORECASE | re.DOTALL
)
if insights_match:
parsed["insights"] = insights_match.group(1).strip()
return parsed
def solve_with_context(
self,
question: str,
context: str,
mode: str = "normal"
) -> Tuple[bool, str]:
"""
Solve doubt with full context from notes.
Args:
question: Student's question
context: Full context from notes
mode: Response mode
Returns:
Tuple of (success, solution)
"""
return self.solve(question, context=context, mode=mode)
def solve_step_by_step(self, question: str) -> Tuple[bool, str]:
"""
Solve doubt with emphasis on step-by-step reasoning.
Args:
question: Student's question
Returns:
Tuple of (success, solution)
"""
enhanced_prompt = f"""Solve this doubt step by step.
Student's Question: {question}
Your approach:
1. Clarify what's being asked
2. Identify key concepts
3. Work through it step by step
4. Check your reasoning
5. Provide clear final answer
Use this format:
STEP 1: [First step]
STEP 2: [Second step]
...
FINAL ANSWER: [Clear answer]
Now solve:"""
try:
success, solution = self.engine.generate(enhanced_prompt, max_tokens=2000)
return success, solution
except Exception as e:
return False, f"Error: {str(e)}"
def compare_solutions(
self,
question: str,
modes: list = None
) -> Tuple[bool, Dict]:
"""
Compare solutions in different modes.
Args:
question: Question to solve
modes: List of modes to compare
Returns:
Tuple of (success, dict of solutions)
"""
if modes is None:
modes = ["normal", "detailed", "teacher"]
solutions = {}
for mode in modes:
success, solution = self.solve(question, mode=mode)
solutions[mode] = solution if success else f"Error: {solution}"
return True, solutions
def is_question_valid(self, question: str) -> Tuple[bool, str]:
"""
Check if question is valid and answerable.
Args:
question: Question to validate
Returns:
Tuple of (is_valid, message)
"""
if len(question.strip()) < 5:
return False, "Question too short"
if len(question.strip().split()) < 3:
return False, "Question not detailed enough"
return True, "Valid question"
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