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# FILE: src/logging_utils.py
# ============================================================
# PURPOSE:
# Save structured logs for RAG runs.
#
# WHY LOGGING MATTERS:
# Production AI systems need observability.
#
# Useful fields:
# - user question
# - generated answer
# - retrieved sources
# - model name
# - prompt version
# - latency
# - errors
#
# AI ENGINEER PRODUCTION TIP:
# Be careful with sensitive data.
# Do not log private user data unless necessary and protected.
# ============================================================
import json
import time
from pathlib import Path
from typing import Any, Dict
def write_jsonl_event(logs_folder: Path, event: Dict[str, Any]) -> Path:
"""
Append one JSON event to a JSONL file.
JSONL format:
- one JSON object per line
- easy to append
- easy to load later
"""
logs_folder.mkdir(parents=True, exist_ok=True)
log_path = logs_folder / "rag_events.jsonl"
event_with_timestamp = {
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
**event,
}
with log_path.open("a", encoding="utf-8") as file:
file.write(json.dumps(event_with_timestamp, ensure_ascii=False) + "\n")
return log_path
def save_json_output(outputs_folder: Path, data: Dict[str, Any], file_name: str) -> Path:
"""
Save a full RAG result as a JSON file.
"""
outputs_folder.mkdir(parents=True, exist_ok=True)
output_path = outputs_folder / file_name
with output_path.open("w", encoding="utf-8") as file:
json.dump(data, file, indent=2, ensure_ascii=False)
return output_path |