Coverage for src / local_deep_research / advanced_search_system / questions / followup / llm_followup_question.py: 0%

1""" 

2LLM-based follow-up question generator. 

3 

4This implementation uses an LLM to intelligently reformulate follow-up 

5questions based on the previous research context. 

6""" 

7 

8from typing import Dict, List 

9from loguru import logger 

10from .base_followup_question import BaseFollowUpQuestionGenerator 

11 

12 

13class LLMFollowUpQuestionGenerator(BaseFollowUpQuestionGenerator): 

14 """ 

15 LLM-based follow-up question generator. 

16 

17 This generator uses an LLM to reformulate follow-up questions 

18 based on the previous research context, creating more targeted 

19 and effective search queries. 

20 

21 NOTE: This is a placeholder for future implementation. 

22 Currently falls back to simple concatenation. 

23 """ 

24 

25 def generate_contextualized_query( 

26 self, 

27 follow_up_query: str, 

28 original_query: str, 

29 past_findings: str, 

30 **kwargs, 

31 ) -> str: 

32 """ 

33 Generate a contextualized query using LLM reformulation. 

34 

35 Future implementation will: 

36 1. Analyze the follow-up query in context of past findings 

37 2. Identify information gaps 

38 3. Reformulate for more effective searching 

39 4. Generate multiple targeted search questions 

40 

41 Args: 

42 follow_up_query: The user's follow-up question 

43 original_query: The original research query 

44 past_findings: The findings from previous research 

45 **kwargs: Additional context parameters 

46 

47 Returns: 

48 str: An LLM-reformulated contextualized query 

49 """ 

50 # TODO: Implement LLM-based reformulation 

51 # For now, fall back to simple concatenation 

52 logger.warning( 

53 "LLM-based follow-up question generation not yet implemented, " 

54 "falling back to simple concatenation" 

55 ) 

56 

57 from .simple_followup_question import SimpleFollowUpQuestionGenerator 

58 

59 simple_generator = SimpleFollowUpQuestionGenerator(self.model) 

60 return simple_generator.generate_contextualized_query( 

61 follow_up_query, original_query, past_findings, **kwargs 

62 ) 

63 

64 def generate_questions( 

65 self, 

66 current_knowledge: str, 

67 query: str, 

68 questions_per_iteration: int, 

69 questions_by_iteration: Dict[int, List[str]], 

70 ) -> List[str]: 

71 """ 

72 Generate multiple targeted questions for follow-up research. 

73 

74 Future implementation will generate multiple specific questions 

75 based on the follow-up query and context. 

76 

77 Args: 

78 current_knowledge: The accumulated knowledge so far 

79 query: The research query 

80 questions_per_iteration: Number of questions to generate 

81 questions_by_iteration: Previous questions 

82 

83 Returns: 

84 List[str]: List of targeted follow-up questions 

85 """ 

86 # TODO: Implement multi-question generation 

87 # For now, return single contextualized query 

88 return super().generate_questions( 

89 current_knowledge, 

90 query, 

91 questions_per_iteration, 

92 questions_by_iteration, 

93 )