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Interview Context module for managing interview state and conversation history.
This module provides the InterviewContext class which maintains the accumulated state
of an interview including previous questions, answers, performance metrics, and
knowledge gaps. It supports efficient querying and persistence to the database.
"""
import time
from typing import List, Dict, Set, Optional
from sqlalchemy.orm import Session
from models.conversation_entry import ConversationEntry
from models.interview import Interview
from modules.monitoring import get_monitor, ErrorType
class InterviewContext:
"""
Manages the state and conversation history of an interview.
The InterviewContext maintains all relevant information about an ongoing or
completed interview, including questions asked, answers given, scores, topics
covered, and identified knowledge gaps. It provides methods for updating the
context and querying performance metrics.
Attributes:
interview_id: Unique identifier for the interview
db_session: SQLAlchemy database session for persistence
questions: List of question dictionaries asked in the interview
answers: List of answer dictionaries given by the candidate
scores: List of scores for each question-answer pair
topics_covered: Set of unique topics covered in the interview
knowledge_gaps: List of topics where candidate showed weakness
current_difficulty: Current difficulty level (easy, medium, hard)
followup_depth: Current depth of consecutive follow-up questions
"""
def __init__(self, interview_id: int, db_session: Session):
"""
Initialize interview context with interview ID and database session.
Args:
interview_id: Unique identifier for the interview
db_session: SQLAlchemy database session for persistence
"""
self.interview_id = interview_id
self.db_session = db_session
self.questions: List[Dict] = []
self.answers: List[Dict] = []
self.scores: List[float] = []
self.topics_covered: Set[str] = set()
self.knowledge_gaps: List[str] = []
self.current_difficulty: str = "medium"
self.followup_depth: int = 0
self.monitor = get_monitor()
def add_qa_pair(
self,
question: Dict,
answer: str,
score: float,
is_followup: bool = False
) -> None:
"""
Add a question-answer pair to the context.
Updates the context with a new question-answer pair, including the score.
Also updates topics_covered, scores list, and followup_depth based on
whether this is a follow-up question.
Args:
question: Dictionary containing question data with keys:
- id: Question ID (optional, may be None for follow-ups)
- text: Question text
- topic: Question topic (optional)
- difficulty: Difficulty level (optional)
- question_type: Type of question (optional)
answer: The candidate's answer text
score: Score for the answer (0.0 to 1.0)
is_followup: Whether this is a follow-up question
"""
# Add question to questions list
self.questions.append(question)
# Add answer to answers list
answer_dict = {
'text': answer,
'score': score,
'is_followup': is_followup
}
self.answers.append(answer_dict)
# Add score to scores list
self.scores.append(score)
# Update topics_covered if topic is provided
if 'topic' in question and question['topic']:
self.topics_covered.add(question['topic'])
# Update followup_depth
if is_followup:
self.followup_depth += 1
else:
self.followup_depth = 0
def get_recent_questions(self, n: int = 5) -> List[Dict]:
"""
Get the last n questions asked in the interview.
Args:
n: Number of recent questions to retrieve (default: 5)
Returns:
List of question dictionaries, most recent last
"""
return self.questions[-n:] if len(self.questions) >= n else self.questions
def get_average_score(self) -> float:
"""
Calculate the average score across all questions.
Returns:
Average score (0.0 to 1.0), or 0.0 if no scores recorded
"""
if not self.scores:
return 0.0
return sum(self.scores) / len(self.scores)
def get_topic_performance(self) -> Dict[str, float]:
"""
Get average score by topic.
Calculates the average score for each topic that has been covered
in the interview.
Returns:
Dictionary mapping topic names to average scores
"""
topic_scores: Dict[str, List[float]] = {}
# Group scores by topic
for i, question in enumerate(self.questions):
if 'topic' in question and question['topic']:
topic = question['topic']
if topic not in topic_scores:
topic_scores[topic] = []
if i < len(self.scores):
topic_scores[topic].append(self.scores[i])
# Calculate average for each topic
topic_performance = {}
for topic, scores in topic_scores.items():
if scores:
topic_performance[topic] = sum(scores) / len(scores)
return topic_performance
def load_from_db(self) -> None:
"""
Load existing interview context from database.
Restores the interview context by querying conversation entries from
the database. Handles missing or corrupted data gracefully by logging
warnings and continuing with partial data.
Raises:
No exceptions - handles errors gracefully
"""
start_time = time.time()
try:
# Query conversation entries for this interview, ordered by sequence
entries = self.db_session.query(ConversationEntry).filter(
ConversationEntry.interview_id == self.interview_id
).order_by(ConversationEntry.sequence_number).all()
# Reset context state
self.questions = []
self.answers = []
self.scores = []
self.topics_covered = set()
self.followup_depth = 0
# Rebuild context from conversation entries
for entry in entries:
# Build question dictionary
question = {
'id': entry.question_id,
'text': entry.question_text,
'topic': entry.topic,
'difficulty': entry.difficulty_level,
'question_type': entry.question_type
}
self.questions.append(question)
# Build answer dictionary
answer = {
'text': entry.answer_text or '',
'score': entry.score or 0.0,
'is_followup': entry.is_followup
}
self.answers.append(answer)
# Add score
if entry.score is not None:
self.scores.append(entry.score)
# Add topic to topics_covered
if entry.topic:
self.topics_covered.add(entry.topic)
# Update followup_depth (track consecutive follow-ups)
if entry.is_followup:
self.followup_depth = entry.followup_depth
else:
self.followup_depth = 0
# Load interview metadata
interview = self.db_session.query(Interview).filter(
Interview.id == self.interview_id
).first()
if interview:
# Load knowledge gaps if available
if interview.knowledge_gaps:
self.knowledge_gaps = interview.knowledge_gaps
# Infer current difficulty from last question or use default
if entries and entries[-1].difficulty_level:
self.current_difficulty = entries[-1].difficulty_level
else:
self.current_difficulty = "medium"
# Calculate latency and log success
latency_ms = (time.time() - start_time) * 1000
self.monitor.log_context_loaded(
interview_id=self.interview_id,
questions_count=len(self.questions),
latency_ms=latency_ms
)
except Exception as e:
# Log error but don't raise - allow context to be used with empty state
print(f"Warning: Error loading interview context for interview {self.interview_id}: {e}")
# Log error to monitoring
self.monitor.log_error(
error_type=ErrorType.CONTEXT_LOADING_FAILURE,
error_message=str(e),
context={'interview_id': self.interview_id}
)
# Initialize with empty state
self.questions = []
self.answers = []
self.scores = []
self.topics_covered = set()
self.knowledge_gaps = []
self.current_difficulty = "medium"
self.followup_depth = 0
def save_to_db(self) -> None:
"""
Persist current context to database.
Saves the current interview context by creating or updating conversation
entries in the database. Also updates the interview record with topics
covered and knowledge gaps. Handles database errors gracefully.
Raises:
No exceptions - handles errors gracefully
"""
start_time = time.time()
try:
# Get existing conversation entries count to determine sequence numbers
existing_count = self.db_session.query(ConversationEntry).filter(
ConversationEntry.interview_id == self.interview_id
).count()
# Save only new entries (those not yet persisted)
for i in range(existing_count, len(self.questions)):
question = self.questions[i]
answer = self.answers[i] if i < len(self.answers) else None
score = self.scores[i] if i < len(self.scores) else None
# Create conversation entry
entry = ConversationEntry(
interview_id=self.interview_id,
sequence_number=i + 1, # 1-indexed
question_id=question.get('id'),
question_text=question.get('text', ''),
answer_text=answer.get('text', '') if answer else '',
score=score,
is_followup=answer.get('is_followup', False) if answer else False,
followup_depth=self.followup_depth if answer and answer.get('is_followup') else 0,
difficulty_level=question.get('difficulty'),
topic=question.get('topic'),
question_type=question.get('question_type'),
time_to_answer=None # TODO: Track time to answer
)
self.db_session.add(entry)
# Update interview record with context metadata
interview = self.db_session.query(Interview).filter(
Interview.id == self.interview_id
).first()
if interview:
# Update topics covered
interview.topics_covered = list(self.topics_covered)
# Update knowledge gaps
interview.knowledge_gaps = self.knowledge_gaps
# Update final score if interview is complete
if self.scores:
interview.final_score = self.get_average_score()
# Commit changes
self.db_session.commit()
# Calculate latency and log success
latency_ms = (time.time() - start_time) * 1000
self.monitor.log_context_saved(
interview_id=self.interview_id,
latency_ms=latency_ms
)
except Exception as e:
# Log error and rollback
print(f"Error saving interview context for interview {self.interview_id}: {e}")
self.db_session.rollback()
# Log error to monitoring
self.monitor.log_error(
error_type=ErrorType.DATABASE_FAILURE,
error_message=str(e),
context={
'interview_id': self.interview_id,
'operation': 'save_context'
}
)
# Don't raise - allow interview to continue even if persistence fails
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