feat(animal-chat): 로깅 시스템 구축
- SQLite DB: animal_chat_logs.db - 로거 모듈: utils/animal_chat_logger.py - 단계별 로깅: - MSSQL (보유 동물약): 개수, 소요시간 - PostgreSQL (RAG): 개수, 소요시간 - LanceDB (벡터 검색): 상위 N개, 유사도, 소스, 소요시간 - OpenAI: 모델, 토큰(입력/출력), 비용, 소요시간 - Admin 페이지: /admin/animal-chat-logs - API: /api/animal-chat-logs - 통계: 총 대화, 평균 응답시간, 총 토큰, 총 비용
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thug0bin
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backend/utils/animal_chat_logger.py
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277
backend/utils/animal_chat_logger.py
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# -*- coding: utf-8 -*-
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"""
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동물약 챗봇 로깅 모듈
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- SQLite에 대화 로그 저장
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- 각 단계별 소요시간, 토큰, 비용 기록
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"""
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import os
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import json
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import sqlite3
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import logging
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Optional
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from dataclasses import dataclass, field, asdict
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logger = logging.getLogger(__name__)
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# DB 경로
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DB_PATH = Path(__file__).parent.parent / "db" / "animal_chat_logs.db"
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SCHEMA_PATH = Path(__file__).parent.parent / "db" / "animal_chat_logs_schema.sql"
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# GPT-4o-mini 가격 (USD per 1K tokens)
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INPUT_COST_PER_1K = 0.00015 # $0.15 / 1M = $0.00015 / 1K
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OUTPUT_COST_PER_1K = 0.0006 # $0.60 / 1M = $0.0006 / 1K
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@dataclass
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class ChatLogEntry:
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"""챗봇 로그 엔트리"""
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session_id: str = ""
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# 입력
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user_message: str = ""
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history_length: int = 0
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# MSSQL
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mssql_drug_count: int = 0
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mssql_duration_ms: int = 0
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# PostgreSQL
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pgsql_rag_count: int = 0
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pgsql_duration_ms: int = 0
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# LanceDB
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vector_results_count: int = 0
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vector_top_scores: List[float] = field(default_factory=list)
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vector_sources: List[str] = field(default_factory=list)
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vector_duration_ms: int = 0
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# OpenAI
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openai_model: str = ""
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openai_prompt_tokens: int = 0
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openai_completion_tokens: int = 0
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openai_total_tokens: int = 0
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openai_cost_usd: float = 0.0
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openai_duration_ms: int = 0
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# 출력
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assistant_response: str = ""
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products_mentioned: List[str] = field(default_factory=list)
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# 메타
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total_duration_ms: int = 0
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error: str = ""
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def calculate_cost(self):
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"""토큰 기반 비용 계산"""
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self.openai_cost_usd = (
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self.openai_prompt_tokens * INPUT_COST_PER_1K / 1000 +
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self.openai_completion_tokens * OUTPUT_COST_PER_1K / 1000
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)
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def init_db():
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"""DB 초기화 (테이블 생성)"""
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try:
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DB_PATH.parent.mkdir(parents=True, exist_ok=True)
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conn = sqlite3.connect(str(DB_PATH))
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if SCHEMA_PATH.exists():
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schema = SCHEMA_PATH.read_text(encoding='utf-8')
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conn.executescript(schema)
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else:
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# 스키마 파일 없으면 인라인 생성
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conn.executescript("""
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CREATE TABLE IF NOT EXISTS chat_logs (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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session_id TEXT,
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user_message TEXT,
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history_length INTEGER,
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mssql_drug_count INTEGER,
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mssql_duration_ms INTEGER,
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pgsql_rag_count INTEGER,
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pgsql_duration_ms INTEGER,
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vector_results_count INTEGER,
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vector_top_scores TEXT,
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vector_sources TEXT,
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vector_duration_ms INTEGER,
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openai_model TEXT,
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openai_prompt_tokens INTEGER,
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openai_completion_tokens INTEGER,
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openai_total_tokens INTEGER,
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openai_cost_usd REAL,
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openai_duration_ms INTEGER,
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assistant_response TEXT,
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products_mentioned TEXT,
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total_duration_ms INTEGER,
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error TEXT,
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created_at TEXT DEFAULT (datetime('now', 'localtime'))
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);
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CREATE INDEX IF NOT EXISTS idx_chat_created ON chat_logs(created_at);
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""")
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conn.commit()
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conn.close()
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logger.info(f"동물약 챗봇 로그 DB 초기화: {DB_PATH}")
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return True
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except Exception as e:
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logger.error(f"DB 초기화 실패: {e}")
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return False
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def log_chat(entry: ChatLogEntry) -> Optional[int]:
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"""
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챗봇 대화 로그 저장
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Returns:
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저장된 로그 ID (실패시 None)
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"""
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try:
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# 비용 계산
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entry.calculate_cost()
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conn = sqlite3.connect(str(DB_PATH))
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cursor = conn.cursor()
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cursor.execute("""
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INSERT INTO chat_logs (
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session_id, user_message, history_length,
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mssql_drug_count, mssql_duration_ms,
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pgsql_rag_count, pgsql_duration_ms,
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vector_results_count, vector_top_scores, vector_sources, vector_duration_ms,
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openai_model, openai_prompt_tokens, openai_completion_tokens,
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openai_total_tokens, openai_cost_usd, openai_duration_ms,
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assistant_response, products_mentioned,
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total_duration_ms, error
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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entry.session_id,
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entry.user_message,
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entry.history_length,
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entry.mssql_drug_count,
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entry.mssql_duration_ms,
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entry.pgsql_rag_count,
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entry.pgsql_duration_ms,
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entry.vector_results_count,
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json.dumps(entry.vector_top_scores),
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json.dumps(entry.vector_sources),
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entry.vector_duration_ms,
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entry.openai_model,
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entry.openai_prompt_tokens,
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entry.openai_completion_tokens,
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entry.openai_total_tokens,
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entry.openai_cost_usd,
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entry.openai_duration_ms,
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entry.assistant_response,
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json.dumps(entry.products_mentioned),
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entry.total_duration_ms,
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entry.error
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))
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log_id = cursor.lastrowid
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conn.commit()
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conn.close()
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logger.debug(f"챗봇 로그 저장: ID={log_id}, tokens={entry.openai_total_tokens}")
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return log_id
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except Exception as e:
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logger.error(f"로그 저장 실패: {e}")
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return None
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def get_logs(
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limit: int = 100,
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offset: int = 0,
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date_from: str = None,
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date_to: str = None,
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error_only: bool = False
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) -> List[Dict]:
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"""로그 조회"""
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try:
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conn = sqlite3.connect(str(DB_PATH))
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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query = "SELECT * FROM chat_logs WHERE 1=1"
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params = []
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if date_from:
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query += " AND created_at >= ?"
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params.append(date_from)
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if date_to:
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query += " AND created_at <= ?"
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params.append(date_to + " 23:59:59")
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if error_only:
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query += " AND error IS NOT NULL AND error != ''"
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query += " ORDER BY created_at DESC LIMIT ? OFFSET ?"
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params.extend([limit, offset])
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cursor.execute(query, params)
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rows = cursor.fetchall()
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conn.close()
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return [dict(row) for row in rows]
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except Exception as e:
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logger.error(f"로그 조회 실패: {e}")
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return []
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def get_stats(date_from: str = None, date_to: str = None) -> Dict:
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"""통계 조회"""
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try:
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conn = sqlite3.connect(str(DB_PATH))
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cursor = conn.cursor()
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query = """
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SELECT
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COUNT(*) as total_chats,
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AVG(total_duration_ms) as avg_duration_ms,
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SUM(openai_total_tokens) as total_tokens,
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SUM(openai_cost_usd) as total_cost_usd,
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AVG(openai_total_tokens) as avg_tokens,
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SUM(CASE WHEN error IS NOT NULL AND error != '' THEN 1 ELSE 0 END) as error_count,
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AVG(vector_duration_ms) as avg_vector_ms,
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AVG(openai_duration_ms) as avg_openai_ms
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FROM chat_logs
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WHERE 1=1
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"""
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params = []
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if date_from:
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query += " AND created_at >= ?"
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params.append(date_from)
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if date_to:
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query += " AND created_at <= ?"
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params.append(date_to + " 23:59:59")
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cursor.execute(query, params)
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row = cursor.fetchone()
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conn.close()
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if row:
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return {
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'total_chats': row[0] or 0,
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'avg_duration_ms': round(row[1] or 0),
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'total_tokens': row[2] or 0,
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'total_cost_usd': round(row[3] or 0, 4),
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'avg_tokens': round(row[4] or 0),
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'error_count': row[5] or 0,
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'avg_vector_ms': round(row[6] or 0),
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'avg_openai_ms': round(row[7] or 0)
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}
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return {}
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except Exception as e:
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logger.error(f"통계 조회 실패: {e}")
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return {}
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# 모듈 로드 시 DB 초기화
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init_db()
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