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Logging module for XENO Bot
Handles CSV logging for responses and timing data
"""
import csv
import os
from datetime import datetime
from typing import Dict, List, Optional, Tuple
CHAT_LOG_DIR = os.environ.get("CHAT_LOG_DIR", "chats")
RESPONSE_LOG_PATH = os.path.join(CHAT_LOG_DIR, "responses.csv")
TIMING_LOG_PATH = os.path.join(CHAT_LOG_DIR, "timing.csv")
RESPONSE_HEADERS = [
"Timestamp",
"Session_ID",
"Question",
"Answer",
"Source_IDs",
"Knowledge_Q1",
"Knowledge_A1",
"Knowledge_Q2",
"Knowledge_A2",
]
TIMING_HEADERS = [
"Timestamp",
"Session_ID",
"Question",
"Total_Time_MS",
"Intent_Classification_MS",
"Memory_Retrieval_MS",
"RAG_Retrieval_MS",
"Embedding_Generation_MS",
"Similarity_Calculation_MS",
"Context_Processing_MS",
"LLM_Generation_MS",
"Memory_Update_MS",
"Logging_MS",
"Error_Step",
"Notes",
]
def _append_csv_row(path: str, headers: List[str], row: List):
"""Create CSV with headers if needed, then append a row."""
try:
os.makedirs(CHAT_LOG_DIR, exist_ok=True)
if not os.path.exists(path):
with open(path, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(headers)
with open(path, "a", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(row)
except Exception as e:
print(f"Failed to append CSV row to {path}: {e}")
def log_response(
question: str,
answer: str,
source_ids: str,
knowledge_pairs: List[Tuple[str, str]],
session_id: str,
timer=None,
):
"""
Log response to CSV file
Args:
question: User's question
answer: Generated answer
source_ids: Source IDs used
knowledge_pairs: Knowledge base Q&A pairs used
session_id: Session identifier
timer: Optional timer object for tracking
"""
if timer:
with timer.time_step("response_logging"):
_log_response_impl(
question, answer, source_ids, knowledge_pairs, session_id
)
else:
_log_response_impl(question, answer, source_ids, knowledge_pairs, session_id)
def _log_response_impl(
question: str,
answer: str,
source_ids: str,
knowledge_pairs: List[Tuple[str, str]],
session_id: str,
):
"""Internal implementation of response logging"""
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# Extract knowledge pairs
knowledge_question_1 = knowledge_pairs[0][0] if len(knowledge_pairs) > 0 else "N/A"
knowledge_answer_1 = knowledge_pairs[0][1] if len(knowledge_pairs) > 0 else "N/A"
knowledge_question_2 = knowledge_pairs[1][0] if len(knowledge_pairs) > 1 else "N/A"
knowledge_answer_2 = knowledge_pairs[1][1] if len(knowledge_pairs) > 1 else "N/A"
row = [
timestamp,
session_id,
question,
answer,
source_ids,
knowledge_question_1,
knowledge_answer_1,
knowledge_question_2,
knowledge_answer_2,
]
try:
_append_csv_row(RESPONSE_LOG_PATH, RESPONSE_HEADERS, row)
print(f"Logged response: {question} | Source IDs: {source_ids}")
except Exception as e:
print(f"Failed to log response: {e}")
def log_timing_data(
question: str,
session_id: str,
timing_summary: Dict,
error_step: Optional[str] = None,
notes: Optional[str] = None,
):
"""
Log timing data to CSV file
Args:
question: User's question
session_id: Session identifier
timing_summary: Timing summary dictionary
error_step: Step where error occurred (if any)
notes: Additional notes
"""
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
step_times = timing_summary["step_times"]
# Truncate long questions
truncated_question = question[:100] + "..." if len(question) > 100 else question
row = [
timestamp,
session_id,
truncated_question,
timing_summary["total_time_ms"],
step_times.get("intent_classification", 0),
step_times.get("memory_retrieval", 0),
step_times.get("rag_retrieval", 0),
step_times.get("embedding_generation", 0),
step_times.get("similarity_calculation", 0),
step_times.get("context_processing", 0),
step_times.get("llm_generation", 0),
step_times.get("memory_update", 0),
step_times.get("response_logging", 0),
error_step or "",
notes or "",
]
try:
_append_csv_row(TIMING_LOG_PATH, TIMING_HEADERS, row)
print(f"Logged timing data: Total {timing_summary['total_time_ms']}ms")
except Exception as e:
print(f"Failed to log timing data: {e}")
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