Text2SQL 에이전틱 접근법 소개
이 자료는 Text2SQL 구현 시 에이전틱(Agentic) 접근법을 활용하는 방법을 소개합니다. 기존의 Text2SQL 방식과 달리, 에이전틱 접근법은 LLM이 단순히 SQL 쿼리를 생성하는 것을 넘어 데이터베이스를 분석하고 상호작용하는 에이전트로서 작동합니다.
- 데이터베이스 설정 및 연결
- 에이전틱 프레임워크(langgraph-swarm) 설정
- SQL 데이터베이스 도구 활용
- 자동 평가 및 개선 메커니즘
- 실제 사용자 쿼리 처리 예시
다음 페이지에서 각 구성 요소의 구현 방법에 대해 자세히 알아보겠습니다.
이 섹션에서는 SQL 데이터베이스를 설정하고 Text2SQL 에이전트가 상호작용할 수 있도록 연결하는 방법을 설명합니다.
%pip install langchain
%pip install langchain_community
%pip install langchain_openai
%pip install sqlalchemy우리의 예제에서는 파리 2024 올림픽 데이터셋을 사용합니다. 이 데이터는 Kaggle에서 다운로드할 수 있습니다.
from google.colab import files
files.upload()%pip install -q kaggle
%kaggle datasets download -d piterfm/paris-2024-olympic-summer-games
%unzip paris-2024-olympic-summer-games.zipCSV 파일을 SQLite 데이터베이스로 변환합니다.
from sqlalchemy import create_engine
import pandas as pd
from glob import glob
def create_sqlite_engine(db_path):
engine = create_engine(f"sqlite:///{db_path}")
return engine
def create_sqlite_db(csv_files, engine):
for csv_file in csv_files:
df = pd.read_csv(csv_file)
table_name = csv_file.split(".")[0]
df.to_sql(table_name, engine, if_exists="replace", index=False)
csv_files = glob("*.csv")
db_path = "olympics.db"
engine = create_sqlite_engine(db_path)
create_sqlite_db(csv_files, engine)import sqlalchemy
db_path = "../data/sqlite_db_olympics"
def load_db(db_path):
engine = sqlalchemy.create_engine(f"sqlite:///{db_path}")
return engine
engine = load_db(db_path)LangChain과 함께 데이터베이스를 사용할 수 있도록 설정합니다.
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain_openai.chat_models import ChatOpenAI
# import os
# os.environ["OPENAI_API_KEY"] = "your-api-key-here" # API 키 설정
db = SQLDatabase(engine=engine)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
toolkit = SQLDatabaseToolkit(llm=llm, db=db)이렇게 설정된 데이터베이스와 도구는 에이전트가 사용자 질문에 대해 SQL 쿼리를 생성하고 실행할 수 있는 기반이 됩니다.
이 섹션에서는 Text2SQL 기능을 위한 에이전트를 설정하는 방법을 알아봅니다. 우리는 langgraph-swarm을 사용하여 에이전트를 구축합니다.
%pip install langgraph-swarmfrom langchain_core.tools import tool
@tool
def get_db_description():
"""get description about database"""
return """
<About-Database>
The Paris 2024 Olympic Summer Games database provides comprehensive information about the Summer Olympics held in 2024. It covers various aspects of the event, including participating countries, athletes, sports disciplines, medal standings, and key event details. More about the Olympic Games on the official site Olympics Paris 2024 and Wiki.
</About-Database>
<Table-inform>
table_name : description
athletes : personal information about all athletes
coaches : personal information about all coaches
events : all events that had a place
medals : all medal holders
medals_total : all medals (grouped by country)
medalists : all medalists
nocs : all nocs (code, country, country_long )
schedule : day-by-day schedule of all events
schedule_preliminary : preliminary schedule of all events
teams : all teams
technical_officials : all technical_officials (referees, judges, jury members)
</Table-inform>
"""| Table | Description |
|---|---|
| athletes | personal information about all athletes |
| coaches | personal information about all coaches |
| events | all events that had a place |
| medals | all medal holders |
| medals_total | all medals (grouped by country) |
| medalists | all medalists |
| nocs | all nocs (code, country, country_long ) |
| schedule | day-by-day schedule of all events |
| schedule_preliminary | preliminary schedule of all events |
| teams | all teams |
| technical_officials | all technical_officials (referees, judges, jury members) |
from langgraph.prebuilt import create_react_agent
from langgraph_swarm import create_swarm
# 전문가 에이전트 생성
bob = create_react_agent(
llm,
toolkit.get_tools() + [get_db_description], # database 정보를 얻기 위한 도구 추가
prompt="You are a professional Database Administrator. You should answer as data driven and this database only",
name="Bob",
)
# 에이전트 스웜 생성
workflow = create_swarm(agents=[bob], default_active_agent="Bob")app = workflow.compile()
response = app.invoke(
{"messages": [{"role": "user", "content": "데이터베이스에 어떤 테이블이 있나요?"}]}
)for message in response["messages"]:
message.pretty_print()================================[1m Human Message [0m=================================
데이터베이스에 어떤 테이블이 있나요?
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_list_tables (call_w5zrj3fFGSiIsdXy4PtLtXOA)
Call ID: call_w5zrj3fFGSiIsdXy4PtLtXOA
Args:
=================================[1m Tool Message [0m=================================
Name: sql_db_list_tables
athletes, coaches, events, medallists, medals, medals_total, nocs, schedules, schedules_preliminary, teams, technical_officials, torch_route, venues
==================================[1m Ai Message [0m==================================
Name: Bob
데이터베이스에는 다음과 같은 테이블이 있습니다:
1. athletes
2. coaches
3. events
4. medallists
5. medals
6. medals_total
7. nocs
8. schedules
9. schedules_preliminary
10. teams
11. technical_officials
12. torch_route
13. venues
에이전트의 상태를 저장하고 관리하기 위한 체크포인트와 스토어를 설정합니다.
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.store.memory import InMemoryStore
# 메모리 기반 저장소 설정
checkpoint = InMemorySaver()
store = InMemoryStore()
# 애플리케이션 컴파일
app = workflow.compile(checkpointer=checkpoint, store=store)langgraph-swarm을 사용하면 다음과 같은 이점이 있습니다:
- 다중 에이전트 협업: 여러 전문가 에이전트가 협력하여 복잡한 작업을 수행할 수 있습니다.
- 상태 관리: 에이전트의 상태와 대화 흐름을 효과적으로 관리할 수 있습니다.
- 도구 사용: 에이전트는 SQL 툴킷을 활용하여 데이터베이스와 상호작용할 수 있습니다.
- 확장성: 필요에 따라 더 많은 에이전트나 도구를 추가할 수 있습니다.
- 사용자가 자연어로 질문을 입력합니다.
- 에이전트는 질문을 분석하고 필요한 정보를 파악합니다.
- SQL 도구를 사용하여 데이터베이스 스키마를 탐색합니다.
- 적절한 도구를 사용합니다.(Function Calling.
QuerySQLDatabaseTool,InfoSQLDatabaseTool,ListSQLDatabaseTool, ...) - 결과를 분석하고 사용자에게 명확한 답변을 제공합니다.
다음 섹션에서는 이 에이전트에 자동 평가 및 개선 기능을 추가하는 방법을 알아보겠습니다.
이 섹션에서는 Text2SQL 에이전트의 응답을 자동으로 평가하고 개선하는 메커니즘을 구현하는 방법을 설명합니다. 이를 통해 에이전트는 자체적으로 응답 품질을 향상시킬 수 있습니다.
%pip install langgraph-reflection
%pip install openevals응답을 평가하는 판단 에이전트(Judge)를 설정합니다. 이 에이전트는 주 에이전트의 응답을 검토하고 피드백을 제공합니다.
from langgraph.graph import StateGraph, MessagesState, START, END
from openevals.llm import create_llm_as_judge
# 간소화된 평가 프롬프트
critique_prompt = """당신은 AI 응답을 평가하는 전문 판단자입니다. 아래 대화에서 AI 어시스턴트의 최신 응답을 평가하세요.
다음 기준에 따라 응답을 평가하세요:
1. 사용자 질문과의 연관성 - 응답이 사용자의 질문과 관련이 있는가?
2. SQL 사용 적절성 - 적절한 SQL을 사용하였고, SQL 결과값에 기반하여 잘 작성되었는가?(SQL를 요구하는 질문인 경우에서만)
모든 기준을 충족하면 True를, 그렇지 않으면 False를 반환하세요.
<response>
{outputs}
</response>"""
def judge_response(state):
"""별도의 판단 모델을 사용하여 어시스턴트의 응답을 평가합니다."""
evaluator = create_llm_as_judge(
prompt=critique_prompt,
model="gpt-4o-mini",
feedback_key="pass",
)
eval_result = evaluator(outputs=state["messages"][-1].content, inputs=None)
if eval_result["score"]:
print("응답이 승인되었습니다")
return
else:
print("개선이 필요합니다")
return {"messages": [{"role": "user", "content": eval_result["comment"]}]}
# 판단 그래프 생성
judge_graph = (
StateGraph(MessagesState)
.add_node(judge_response)
.add_edge(START, "judge_response")
.add_edge("judge_response", END)
.compile()
)주 에이전트와 판단 에이전트를 연결하는 Reflection 그래프를 구현합니다.
from langgraph_reflection import create_reflection_graph
# 성찰 그래프 생성 및 컴파일
reflection_graph = create_reflection_graph(app, judge_graph)
reflection_app = reflection_graph.compile()- 사용자가 질문을 입력합니다.
- 에이전트(Bob)가 응답을 생성합니다.
- 판단 에이전트가 응답을 평가합니다.
- 응답이 충분하지 않으면 피드백을 제공합니다.
- 에이전트는 피드백을 받아 응답을 개선합니다.
- 이 과정은 판단 에이전트가 응답을 승인할 때까지 반복됩니다.
response = reflection_app.invoke(
{
"messages": [
{
"role": "user",
"content": "한국 메달 리스트를 종목별로 보여줘",
}
]
}
)응답이 승인되었습니다
from langchain_core.messages import HumanMessage, ToolMessage
from IPython.display import Markdown, display
def display_message(response):
if isinstance(response["messages"][-1], HumanMessage):
display(Markdown(response["messages"][-2].content))
else:
display(Markdown(response["messages"][-1].content))
display_message(response)한국의 메달 리스트를 종목별로 정리한 결과는 다음과 같습니다:
- 양궁: 12개
- 배드민턴: 3개
- 복싱: 1개
- 펜싱: 9개
- 유도: 15개
- 근대5종: 1개
- 사격: 7개
- 수영: 1개
- 탁구: 5개
- 태권도: 3개
- 역도: 1개
이 데이터는 한국이 각 종목에서 획득한 메달 수를 나타냅니다.
for message in response["messages"]:
message.pretty_print()================================[1m Human Message [0m=================================
한국 메달 리스트를 종목별로 보여줘
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_list_tables (call_qZ9CdBQb9FX1l6U2lcPI1Nia)
Call ID: call_qZ9CdBQb9FX1l6U2lcPI1Nia
Args:
=================================[1m Tool Message [0m=================================
Name: sql_db_list_tables
athletes, coaches, events, medallists, medals, medals_total, nocs, schedules, schedules_preliminary, teams, technical_officials, torch_route, venues
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_schema (call_TI3aPJXXP8Vm0ttQHpUH7AvX)
Call ID: call_TI3aPJXXP8Vm0ttQHpUH7AvX
Args:
table_names: medallists
=================================[1m Tool Message [0m=================================
Name: sql_db_schema
CREATE TABLE medallists (
medal_date TEXT,
medal_type TEXT,
medal_code REAL,
name TEXT,
gender TEXT,
country_code TEXT,
country TEXT,
country_long TEXT,
nationality_code TEXT,
nationality TEXT,
nationality_long TEXT,
team TEXT,
team_gender TEXT,
discipline TEXT,
event TEXT,
event_type TEXT,
url_event TEXT,
birth_date TEXT,
code_athlete INTEGER,
code_team TEXT,
is_medallist INTEGER
)
/*
3 rows from medallists table:
medal_date medal_type medal_code name gender country_code country country_long nationality_code nationality nationality_long team team_gender discipline event event_type url_event birth_date code_athlete code_team is_medallist
2024-07-27 Gold Medal 1.0 EVENEPOEL Remco Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 2000-01-25 1903136 None 1
2024-07-27 Silver Medal 2.0 GANNA Filippo Male ITA Italy Italy ITA Italy Italy None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1996-07-25 1923520 None 1
2024-07-27 Bronze Medal 3.0 van AERT Wout Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1994-09-15 1903147 None 1
*/
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query (call_IDyEi8PqiW80zswyE2hSA44w)
Call ID: call_IDyEi8PqiW80zswyE2hSA44w
Args:
query: SELECT discipline, COUNT(*) as medal_count FROM medallists WHERE country_code = 'KOR' GROUP BY discipline
=================================[1m Tool Message [0m=================================
Name: sql_db_query
[('Archery', 12), ('Badminton', 3), ('Boxing', 1), ('Fencing', 9), ('Judo', 15), ('Modern Pentathlon', 1), ('Shooting', 7), ('Swimming', 1), ('Table Tennis', 5), ('Taekwondo', 3), ('Weightlifting', 1)]
==================================[1m Ai Message [0m==================================
Name: Bob
한국의 메달 리스트를 종목별로 정리한 결과는 다음과 같습니다:
- 양궁: 12개
- 배드민턴: 3개
- 복싱: 1개
- 펜싱: 9개
- 유도: 15개
- 근대5종: 1개
- 사격: 7개
- 수영: 1개
- 탁구: 5개
- 태권도: 3개
- 역도: 1개
이 데이터는 한국이 각 종목에서 획득한 메달 수를 나타냅니다.
이 섹션에서는 에이전틱 접근법으로 구현된 Text2SQL 에이전트의 실제 사용 예시를 보여드립니다.
response = reflection_app.invoke(
{
"messages": [
{
"role": "user",
"content": "금메달을 가장 많이 획득한 상위 5개국을 보여주세요.",
}
]
}
)
print(response["messages"][-1].content)응답이 승인되었습니다
금메달을 가장 많이 획득한 상위 5개국은 다음과 같습니다:
1. **USA** - 134개
2. **CHN** - 71개
3. **NED** - 67개
4. **FRA** - 53개
5. **GBR** - 40개
display_message(response)금메달을 가장 많이 획득한 상위 5개국은 다음과 같습니다:
- USA - 134개
- CHN - 71개
- NED - 67개
- FRA - 53개
- GBR - 40개
for message in response["messages"]:
message.pretty_print()================================[1m Human Message [0m=================================
금메달을 가장 많이 획득한 상위 5개국을 보여주세요.
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_list_tables (call_ux6yF3nO45eET2P57IrlQEwi)
Call ID: call_ux6yF3nO45eET2P57IrlQEwi
Args:
=================================[1m Tool Message [0m=================================
Name: sql_db_list_tables
athletes, coaches, events, medallists, medals, medals_total, nocs, schedules, schedules_preliminary, teams, technical_officials, torch_route, venues
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query_checker (call_eH4hRETJyRrtbTrXvtqexcCz)
Call ID: call_eH4hRETJyRrtbTrXvtqexcCz
Args:
query: SELECT noc, COUNT(medal) AS gold_count FROM medallists WHERE medal = 'Gold' GROUP BY noc ORDER BY gold_count DESC LIMIT 5;
=================================[1m Tool Message [0m=================================
Name: sql_db_query_checker
```sql
SELECT noc, COUNT(medal) AS gold_count FROM medallists WHERE medal = 'Gold' GROUP BY noc ORDER BY gold_count DESC LIMIT 5;
```
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query (call_Vi1sjvNYSGbFAIq7lkUPSSar)
Call ID: call_Vi1sjvNYSGbFAIq7lkUPSSar
Args:
query: SELECT noc, COUNT(medal) AS gold_count FROM medallists WHERE medal = 'Gold' GROUP BY noc ORDER BY gold_count DESC LIMIT 5;
=================================[1m Tool Message [0m=================================
Name: sql_db_query
Error: (sqlite3.OperationalError) no such column: noc
[SQL: SELECT noc, COUNT(medal) AS gold_count FROM medallists WHERE medal = 'Gold' GROUP BY noc ORDER BY gold_count DESC LIMIT 5;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_schema (call_W4zkYZ4t1u0kmuAhpzVTQYsD)
Call ID: call_W4zkYZ4t1u0kmuAhpzVTQYsD
Args:
table_names: medallists
=================================[1m Tool Message [0m=================================
Name: sql_db_schema
CREATE TABLE medallists (
medal_date TEXT,
medal_type TEXT,
medal_code REAL,
name TEXT,
gender TEXT,
country_code TEXT,
country TEXT,
country_long TEXT,
nationality_code TEXT,
nationality TEXT,
nationality_long TEXT,
team TEXT,
team_gender TEXT,
discipline TEXT,
event TEXT,
event_type TEXT,
url_event TEXT,
birth_date TEXT,
code_athlete INTEGER,
code_team TEXT,
is_medallist INTEGER
)
/*
3 rows from medallists table:
medal_date medal_type medal_code name gender country_code country country_long nationality_code nationality nationality_long team team_gender discipline event event_type url_event birth_date code_athlete code_team is_medallist
2024-07-27 Gold Medal 1.0 EVENEPOEL Remco Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 2000-01-25 1903136 None 1
2024-07-27 Silver Medal 2.0 GANNA Filippo Male ITA Italy Italy ITA Italy Italy None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1996-07-25 1923520 None 1
2024-07-27 Bronze Medal 3.0 van AERT Wout Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1994-09-15 1903147 None 1
*/
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query_checker (call_O2mDO90GruiNPaAVBl0epamc)
Call ID: call_O2mDO90GruiNPaAVBl0epamc
Args:
query: SELECT country_code, COUNT(medal_type) AS gold_count FROM medallists WHERE medal_type = 'Gold Medal' GROUP BY country_code ORDER BY gold_count DESC LIMIT 5;
=================================[1m Tool Message [0m=================================
Name: sql_db_query_checker
```sql
SELECT country_code, COUNT(medal_type) AS gold_count FROM medallists WHERE medal_type = 'Gold Medal' GROUP BY country_code ORDER BY gold_count DESC LIMIT 5;
```
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query (call_YHZcR5AeJyQbg6S6GRAOtMOo)
Call ID: call_YHZcR5AeJyQbg6S6GRAOtMOo
Args:
query: SELECT country_code, COUNT(medal_type) AS gold_count FROM medallists WHERE medal_type = 'Gold Medal' GROUP BY country_code ORDER BY gold_count DESC LIMIT 5;
=================================[1m Tool Message [0m=================================
Name: sql_db_query
[('USA', 134), ('CHN', 71), ('NED', 67), ('FRA', 53), ('GBR', 40)]
==================================[1m Ai Message [0m==================================
Name: Bob
금메달을 가장 많이 획득한 상위 5개국은 다음과 같습니다:
1. **USA** - 134개
2. **CHN** - 71개
3. **NED** - 67개
4. **FRA** - 53개
5. **GBR** - 40개
response = reflection_app.invoke(
{
"messages": [
{
"role": "user",
"content": "여자 선수들이 획득한 메달 수가 남자 선수들보다 많은 국가들을 보여주세요.",
}
]
}
)
print(response["messages"][-1].content)응답이 승인되었습니다
여자 선수들이 남자 선수들보다 더 많은 메달을 획득한 국가들은 다음과 같습니다:
| 국가 | 여자 메달 수 | 남자 메달 수 |
|------------------|--------------|--------------|
| Algeria | 2 | 1 |
| Argentina | 17 | 2 |
| Australia | 76 | 47 |
| Brazil | 54 | 13 |
| Canada | 41 | 16 |
| China | 112 | 56 |
| DPR Korea | 6 | 2 |
| Dominica | 1 | 0 |
| EOR | 1 | 0 |
| Ecuador | 4 | 2 |
| Germany | 58 | 55 |
| Hong Kong, China | 3 | 1 |
| Israel | 8 | 3 |
| Italy | 45 | 43 |
| Kenya | 7 | 4 |
| Korea | 34 | 24 |
| Kosovo | 2 | 0 |
| Mongolia | 1 | 0 |
| Netherlands | 69 | 49 |
| New Zealand | 41 | 10 |
| Norway | 18 | 5 |
| Panama | 1 | 0 |
| Romania | 17 | 4 |
| Saint Lucia | 2 | 0 |
| Slovenia | 2 | 1 |
| Switzerland | 6 | 4 |
| Türkiye | 6 | 5 |
| Ukraine | 10 | 6 |
| United States | 187 | 143 |
이 표는 여자 선수들이 남자 선수들보다 더 많은 메달을 획득한 국가들을 보여줍니다.
display_message(response)여자 선수들이 남자 선수들보다 더 많은 메달을 획득한 국가들은 다음과 같습니다:
| 국가 | 여자 메달 수 | 남자 메달 수 |
|---|---|---|
| Algeria | 2 | 1 |
| Argentina | 17 | 2 |
| Australia | 76 | 47 |
| Brazil | 54 | 13 |
| Canada | 41 | 16 |
| China | 112 | 56 |
| DPR Korea | 6 | 2 |
| Dominica | 1 | 0 |
| EOR | 1 | 0 |
| Ecuador | 4 | 2 |
| Germany | 58 | 55 |
| Hong Kong, China | 3 | 1 |
| Israel | 8 | 3 |
| Italy | 45 | 43 |
| Kenya | 7 | 4 |
| Korea | 34 | 24 |
| Kosovo | 2 | 0 |
| Mongolia | 1 | 0 |
| Netherlands | 69 | 49 |
| New Zealand | 41 | 10 |
| Norway | 18 | 5 |
| Panama | 1 | 0 |
| Romania | 17 | 4 |
| Saint Lucia | 2 | 0 |
| Slovenia | 2 | 1 |
| Switzerland | 6 | 4 |
| Türkiye | 6 | 5 |
| Ukraine | 10 | 6 |
| United States | 187 | 143 |
이 표는 여자 선수들이 남자 선수들보다 더 많은 메달을 획득한 국가들을 보여줍니다.
for message in response["messages"]:
message.pretty_print()================================[1m Human Message [0m=================================
여자 선수들이 획득한 메달 수가 남자 선수들보다 많은 국가들을 보여주세요.
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_list_tables (call_GOP38UIWMNlvCE48jTA1pMJQ)
Call ID: call_GOP38UIWMNlvCE48jTA1pMJQ
Args:
=================================[1m Tool Message [0m=================================
Name: sql_db_list_tables
athletes, coaches, events, medallists, medals, medals_total, nocs, schedules, schedules_preliminary, teams, technical_officials, torch_route, venues
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_schema (call_E6mqCAVuaa0g1rIUmlh3zkdi)
Call ID: call_E6mqCAVuaa0g1rIUmlh3zkdi
Args:
table_names: medallists
sql_db_schema (call_XuYSC0xNxoCTIM26uhUHPmnD)
Call ID: call_XuYSC0xNxoCTIM26uhUHPmnD
Args:
table_names: nocs
=================================[1m Tool Message [0m=================================
Name: sql_db_schema
CREATE TABLE medallists (
medal_date TEXT,
medal_type TEXT,
medal_code REAL,
name TEXT,
gender TEXT,
country_code TEXT,
country TEXT,
country_long TEXT,
nationality_code TEXT,
nationality TEXT,
nationality_long TEXT,
team TEXT,
team_gender TEXT,
discipline TEXT,
event TEXT,
event_type TEXT,
url_event TEXT,
birth_date TEXT,
code_athlete INTEGER,
code_team TEXT,
is_medallist INTEGER
)
/*
3 rows from medallists table:
medal_date medal_type medal_code name gender country_code country country_long nationality_code nationality nationality_long team team_gender discipline event event_type url_event birth_date code_athlete code_team is_medallist
2024-07-27 Gold Medal 1.0 EVENEPOEL Remco Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 2000-01-25 1903136 None 1
2024-07-27 Silver Medal 2.0 GANNA Filippo Male ITA Italy Italy ITA Italy Italy None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1996-07-25 1923520 None 1
2024-07-27 Bronze Medal 3.0 van AERT Wout Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1994-09-15 1903147 None 1
*/
=================================[1m Tool Message [0m=================================
Name: sql_db_schema
CREATE TABLE nocs (
code TEXT,
country TEXT,
country_long TEXT,
tag TEXT,
note TEXT
)
/*
3 rows from nocs table:
code country country_long tag note
AFG Afghanistan Afghanistan afghanistan P
AHO Netherlands Antilles Netherlands Antilles netherlands-antilles H
AIN AIN AIN ain P
*/
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query_checker (call_51knQSSW8acOgPq3dfD6x6NV)
Call ID: call_51knQSSW8acOgPq3dfD6x6NV
Args:
query: SELECT n.country, COUNT(CASE WHEN m.gender = 'Female' THEN 1 END) AS female_medals, COUNT(CASE WHEN m.gender = 'Male' THEN 1 END) AS male_medals
FROM medallists m
JOIN nocs n ON m.country_code = n.code
GROUP BY n.country
HAVING female_medals > male_medals;
=================================[1m Tool Message [0m=================================
Name: sql_db_query_checker
```sql
SELECT n.country, COUNT(CASE WHEN m.gender = 'Female' THEN 1 END) AS female_medals, COUNT(CASE WHEN m.gender = 'Male' THEN 1 END) AS male_medals
FROM medallists m
JOIN nocs n ON m.country_code = n.code
GROUP BY n.country
HAVING female_medals > male_medals;
```
==================================[1m Ai Message [0m==================================
Name: Bob
Tool Calls:
sql_db_query (call_5CZMTgRIvEnLkRkAF0MSCQx0)
Call ID: call_5CZMTgRIvEnLkRkAF0MSCQx0
Args:
query: SELECT n.country, COUNT(CASE WHEN m.gender = 'Female' THEN 1 END) AS female_medals, COUNT(CASE WHEN m.gender = 'Male' THEN 1 END) AS male_medals
FROM medallists m
JOIN nocs n ON m.country_code = n.code
GROUP BY n.country
HAVING female_medals > male_medals;
=================================[1m Tool Message [0m=================================
Name: sql_db_query
[('Algeria', 2, 1), ('Argentina', 17, 2), ('Australia', 76, 47), ('Brazil', 54, 13), ('Canada', 41, 16), ('China', 112, 56), ('DPR Korea', 6, 2), ('Dominica', 1, 0), ('EOR', 1, 0), ('Ecuador', 4, 2), ('Germany', 58, 55), ('Hong Kong, China', 3, 1), ('Israel', 8, 3), ('Italy', 45, 43), ('Kenya', 7, 4), ('Korea', 34, 24), ('Kosovo', 2, 0), ('Mongolia', 1, 0), ('Netherlands', 69, 49), ('New Zealand', 41, 10), ('Norway', 18, 5), ('Panama', 1, 0), ('Romania', 17, 4), ('Saint Lucia', 2, 0), ('Slovenia', 2, 1), ('Switzerland', 6, 4), ('Türkiye', 6, 5), ('Ukraine', 10, 6), ('United States', 187, 143)]
==================================[1m Ai Message [0m==================================
Name: Bob
여자 선수들이 남자 선수들보다 더 많은 메달을 획득한 국가들은 다음과 같습니다:
| 국가 | 여자 메달 수 | 남자 메달 수 |
|------------------|--------------|--------------|
| Algeria | 2 | 1 |
| Argentina | 17 | 2 |
| Australia | 76 | 47 |
| Brazil | 54 | 13 |
| Canada | 41 | 16 |
| China | 112 | 56 |
| DPR Korea | 6 | 2 |
| Dominica | 1 | 0 |
| EOR | 1 | 0 |
| Ecuador | 4 | 2 |
| Germany | 58 | 55 |
| Hong Kong, China | 3 | 1 |
| Israel | 8 | 3 |
| Italy | 45 | 43 |
| Kenya | 7 | 4 |
| Korea | 34 | 24 |
| Kosovo | 2 | 0 |
| Mongolia | 1 | 0 |
| Netherlands | 69 | 49 |
| New Zealand | 41 | 10 |
| Norway | 18 | 5 |
| Panama | 1 | 0 |
| Romania | 17 | 4 |
| Saint Lucia | 2 | 0 |
| Slovenia | 2 | 1 |
| Switzerland | 6 | 4 |
| Türkiye | 6 | 5 |
| Ukraine | 10 | 6 |
| United States | 187 | 143 |
이 표는 여자 선수들이 남자 선수들보다 더 많은 메달을 획득한 국가들을 보여줍니다.
에이전트가 응답을 생성하고 자체 개선하는 과정을 관찰할 수 있습니다:
response = reflection_app.invoke(
{
"messages": [
{
"role": "user",
"content": "한국과 일본의 메달 획득 패턴을 비교해서 분석해줘",
}
]
}
)개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
개선이 필요합니다
응답이 승인되었습니다
response["messages"][2]ToolMessage(content='athletes, coaches, events, medallists, medals, medals_total, nocs, schedules, schedules_preliminary, teams, technical_officials, torch_route, venues', name='sql_db_list_tables', id='b8a592ca-6e2f-4646-8f6e-25d0ec00ef50', tool_call_id='call_Qax8Ckd7DOdNu8cV6Y5rMgqN')
for message in response["messages"]:
if isinstance(message, HumanMessage):
print("역할: Human")
elif isinstance(message, ToolMessage):
print("역할: Tool")
else:
print("역할: AI")
print(f"내용: {message.content}")
print("-" * 50)역할: Human
내용: 한국과 일본의 메달 획득 패턴을 비교해서 분석해줘
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: athletes, coaches, events, medallists, medals, medals_total, nocs, schedules, schedules_preliminary, teams, technical_officials, torch_route, venues
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: Error: (sqlite3.OperationalError) no such column: noc
[SQL: SELECT COUNT(*) AS total_medals, noc FROM medallists WHERE noc IN ('KOR', 'JPN') GROUP BY noc;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
--------------------------------------------------
역할: Tool
내용: Error: (sqlite3.OperationalError) no such column: noc
[SQL: SELECT event, COUNT(*) AS medals FROM medallists WHERE noc = 'KOR' GROUP BY event;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
--------------------------------------------------
역할: Tool
내용: Error: (sqlite3.OperationalError) no such column: noc
[SQL: SELECT event, COUNT(*) AS medals FROM medallists WHERE noc = 'JPN' GROUP BY event;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용:
CREATE TABLE medallists (
medal_date TEXT,
medal_type TEXT,
medal_code REAL,
name TEXT,
gender TEXT,
country_code TEXT,
country TEXT,
country_long TEXT,
nationality_code TEXT,
nationality TEXT,
nationality_long TEXT,
team TEXT,
team_gender TEXT,
discipline TEXT,
event TEXT,
event_type TEXT,
url_event TEXT,
birth_date TEXT,
code_athlete INTEGER,
code_team TEXT,
is_medallist INTEGER
)
/*
3 rows from medallists table:
medal_date medal_type medal_code name gender country_code country country_long nationality_code nationality nationality_long team team_gender discipline event event_type url_event birth_date code_athlete code_team is_medallist
2024-07-27 Gold Medal 1.0 EVENEPOEL Remco Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 2000-01-25 1903136 None 1
2024-07-27 Silver Medal 2.0 GANNA Filippo Male ITA Italy Italy ITA Italy Italy None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1996-07-25 1923520 None 1
2024-07-27 Bronze Medal 3.0 van AERT Wout Male BEL Belgium Belgium BEL Belgium Belgium None None Cycling Road Men's Individual Time Trial ATH /en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100-- 1994-09-15 1903147 None 1
*/
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: [(82, 'JPN'), (58, 'KOR')]
--------------------------------------------------
역할: Tool
내용: [('10m Air Pistol Women', 2), ('10m Air Rifle Mixed Team', 2), ('10m Air Rifle Women', 1), ('25m Pistol Women', 1), ('25m Rapid Fire Pistol Men', 1), ('Men +100 kg', 1), ('Men -58kg', 1), ('Men -81 kg', 1), ("Men's 400m Freestyle", 1), ("Men's Individual", 2), ("Men's Sabre Individual", 1), ("Men's Sabre Team", 4), ("Men's Team", 3), ('Mixed Doubles', 4), ('Mixed Team', 13), ('Women +67kg', 1), ('Women +78 kg', 1), ('Women -57 kg', 1), ('Women -57kg', 1), ("Women's +81kg", 1), ("Women's 54kg", 1), ("Women's Individual", 3), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Team", 6)]
--------------------------------------------------
역할: Tool
내용: [('B-Girls', 1), ('Eventing Team', 4), ('Men -60 kg', 1), ('Men -66 kg', 1), ('Men -73 kg', 1), ('Men -81 kg', 1), ('Men -90 kg', 1), ("Men's 10m Platform", 1), ("Men's 400m Individual Medley", 1), ("Men's All-Around", 1), ("Men's Boulder & Lead", 1), ("Men's Foil Team", 4), ("Men's Freestyle 57kg", 1), ("Men's Freestyle 65kg", 1), ("Men's Freestyle 74kg", 1), ("Men's Greco-Roman 60kg", 1), ("Men's Greco-Roman 77kg", 1), ("Men's Horizontal Bar", 1), ("Men's Individual", 1), ("Men's Individual Stroke Play", 1), ("Men's Parallel Bars", 1), ("Men's Street", 1), ("Men's Team", 5), ("Men's Épée Individual", 1), ("Men's Épée Team", 4), ('Mixed Dinghy', 2), ('Mixed Doubles', 2), ('Mixed Team', 14), ('Women -48 kg', 1), ('Women -57 kg', 1), ("Women's Doubles", 2), ("Women's Foil Team", 4), ("Women's Freestyle 50kg", 1), ("Women's Freestyle 53kg", 1), ("Women's Freestyle 57kg", 1), ("Women's Freestyle 62kg", 1), ("Women's Freestyle 68kg", 1), ("Women's Freestyle 76kg", 1), ("Women's Javelin Throw", 1), ("Women's Park", 1), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Street", 2), ("Women's Team", 3)]
--------------------------------------------------
역할: AI
내용: ### 메달 획득 패턴 분석: 한국(KOR) vs 일본(JPN)
#### 1. 총 메달 수
- **일본 (JPN)**: 82개
- **한국 (KOR)**: 58개
일본이 한국보다 총 메달 수에서 우세한 것으로 나타났습니다.
#### 2. 이벤트별 메달 수
**한국 (KOR)**
- 10m Air Pistol Women: 2
- 10m Air Rifle Mixed Team: 2
- 10m Air Rifle Women: 1
- 25m Pistol Women: 1
- 25m Rapid Fire Pistol Men: 1
- Men +100 kg: 1
- Men -58kg: 1
- Men -81 kg: 1
- Men's 400m Freestyle: 1
- Men's Individual: 2
- Men's Sabre Individual: 1
- Men's Sabre Team: 4
- Men's Team: 3
- Mixed Doubles: 4
- Mixed Team: 13
- Women +67kg: 1
- Women +78 kg: 1
- Women -57 kg: 1
- Women -57kg: 1
- Women's +81kg: 1
- Women's 54kg: 1
- Women's Individual: 3
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Team: 6
**일본 (JPN)**
- B-Girls: 1
- Eventing Team: 4
- Men -60 kg: 1
- Men -66 kg: 1
- Men -73 kg: 1
- Men -81 kg: 1
- Men -90 kg: 1
- Men's 10m Platform: 1
- Men's 400m Individual Medley: 1
- Men's All-Around: 1
- Men's Boulder & Lead: 1
- Men's Foil Team: 4
- Men's Freestyle 57kg: 1
- Men's Freestyle 65kg: 1
- Men's Freestyle 74kg: 1
- Men's Greco-Roman 60kg: 1
- Men's Greco-Roman 77kg: 1
- Men's Horizontal Bar: 1
- Men's Individual: 1
- Men's Individual Stroke Play: 1
- Men's Parallel Bars: 1
- Men's Street: 1
- Men's Team: 5
- Men's Épée Individual: 1
- Men's Épée Team: 4
- Mixed Dinghy: 2
- Mixed Doubles: 2
- Mixed Team: 14
- Women -48 kg: 1
- Women -57 kg: 1
- Women's Doubles: 2
- Women's Foil Team: 4
- Women's Freestyle 50kg: 1
- Women's Freestyle 53kg: 1
- Women's Freestyle 57kg: 1
- Women's Freestyle 62kg: 1
- Women's Freestyle 68kg: 1
- Women's Freestyle 76kg: 1
- Women's Javelin Throw: 1
- Women's Park: 1
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Street: 2
- Women's Team: 3
### 결론
일본은 총 메달 수에서 한국을 초과하며, 다양한 종목에서 메달을 획득하고 있습니다. 한국은 특정 종목에서 강세를 보이고 있지만, 전체적인 메달 수에서는 일본에 비해 부족한 상황입니다.
--------------------------------------------------
역할: Human
내용: The AI response is relevant to the topic of medal counts between South Korea and Japan, providing a detailed comparison along with a conclusion. However, the user's question regarding SQL usage is not applicable here, as no SQL statements or queries were presented in the response. Thus, the SQL usage criterion cannot be evaluated positively. Therefore, the response fails to meet all the criteria. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용: Thank you for your feedback. If you have any specific SQL queries or further questions regarding the medal counts or any other data analysis, please let me know, and I'll be happy to assist you!
--------------------------------------------------
역할: Human
내용: The response expresses gratitude for feedback and invites further questions, which maintains a conversational tone. However, it does not directly address a specific SQL query or data analysis, as it lacks any SQL elements despite being a potential continuation of a conversation. Therefore, while the approach may be seen as friendly and open, it doesn't fulfill the SQL usage requirement and is not closely tied to an existing question. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용: I appreciate your assessment. If you would like to explore specific SQL queries or need further analysis on the medal counts or any other data, please provide the details, and I will ensure to include the necessary SQL elements in my response. Thank you for your understanding!
--------------------------------------------------
역할: Human
내용: The AI assistant's response does not directly address a specific user question and instead offers further assistance if needed, which may not be directly relevant to a posed question about SQL. There is no specific SQL query provided or discussed, making the response not fully pertinent to the criteria for SQL usage. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용: I understand your evaluation. If you have a specific SQL query or analysis in mind regarding the medal counts or any other aspect of the data, please share it, and I will provide a detailed response that includes the necessary SQL elements. Thank you for your patience!
--------------------------------------------------
역할: Human
내용: The AI response acknowledges the user's evaluation and invites them to share a specific SQL query or analysis connected to medal counts or data aspects. This shows relevance to a possible user request related to SQL. However, the response does not include any SQL itself nor does it explicitly convey results based on SQL, which might be necessary if the initial query involved a specific SQL request. Therefore, it partially meets the criteria but fails to provide SQL usage. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용: I appreciate your feedback and understand the importance of including SQL elements in my responses. If you have a specific SQL query or analysis you would like me to perform regarding the medal counts or any other data, please let me know, and I will ensure to provide the relevant SQL details and results. Thank you for your understanding!
--------------------------------------------------
역할: Human
내용: The AI response acknowledges the importance of SQL and invites the user to specify a query, showing an openness to relating to the user's needs. However, the response lacks a direct answer to a specific question. Therefore, it does not fully address the criterion of relevance to a user's potential SQL query. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: ```sql
SELECT COUNT(*) AS total_medals, country_code FROM medallists WHERE country_code IN ('KOR', 'JPN') GROUP BY country_code;
```
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: [(82, 'JPN'), (58, 'KOR')]
--------------------------------------------------
역할: Tool
내용: [('10m Air Pistol Women', 2), ('10m Air Rifle Mixed Team', 2), ('10m Air Rifle Women', 1), ('25m Pistol Women', 1), ('25m Rapid Fire Pistol Men', 1), ('Men +100 kg', 1), ('Men -58kg', 1), ('Men -81 kg', 1), ("Men's 400m Freestyle", 1), ("Men's Individual", 2), ("Men's Sabre Individual", 1), ("Men's Sabre Team", 4), ("Men's Team", 3), ('Mixed Doubles', 4), ('Mixed Team', 13), ('Women +67kg', 1), ('Women +78 kg', 1), ('Women -57 kg', 1), ('Women -57kg', 1), ("Women's +81kg", 1), ("Women's 54kg", 1), ("Women's Individual", 3), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Team", 6)]
--------------------------------------------------
역할: Tool
내용: [('B-Girls', 1), ('Eventing Team', 4), ('Men -60 kg', 1), ('Men -66 kg', 1), ('Men -73 kg', 1), ('Men -81 kg', 1), ('Men -90 kg', 1), ("Men's 10m Platform", 1), ("Men's 400m Individual Medley", 1), ("Men's All-Around", 1), ("Men's Boulder & Lead", 1), ("Men's Foil Team", 4), ("Men's Freestyle 57kg", 1), ("Men's Freestyle 65kg", 1), ("Men's Freestyle 74kg", 1), ("Men's Greco-Roman 60kg", 1), ("Men's Greco-Roman 77kg", 1), ("Men's Horizontal Bar", 1), ("Men's Individual", 1), ("Men's Individual Stroke Play", 1), ("Men's Parallel Bars", 1), ("Men's Street", 1), ("Men's Team", 5), ("Men's Épée Individual", 1), ("Men's Épée Team", 4), ('Mixed Dinghy', 2), ('Mixed Doubles', 2), ('Mixed Team', 14), ('Women -48 kg', 1), ('Women -57 kg', 1), ("Women's Doubles", 2), ("Women's Foil Team", 4), ("Women's Freestyle 50kg", 1), ("Women's Freestyle 53kg", 1), ("Women's Freestyle 57kg", 1), ("Women's Freestyle 62kg", 1), ("Women's Freestyle 68kg", 1), ("Women's Freestyle 76kg", 1), ("Women's Javelin Throw", 1), ("Women's Park", 1), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Street", 2), ("Women's Team", 3)]
--------------------------------------------------
역할: AI
내용: ### 메달 획득 패턴 분석: 한국(KOR) vs 일본(JPN)
#### 1. 총 메달 수
- **일본 (JPN)**: 82개
- **한국 (KOR)**: 58개
일본이 한국보다 총 메달 수에서 우세한 것으로 나타났습니다.
#### 2. 이벤트별 메달 수
**한국 (KOR)**
- 10m Air Pistol Women: 2
- 10m Air Rifle Mixed Team: 2
- 10m Air Rifle Women: 1
- 25m Pistol Women: 1
- 25m Rapid Fire Pistol Men: 1
- Men +100 kg: 1
- Men -58kg: 1
- Men -81 kg: 1
- Men's 400m Freestyle: 1
- Men's Individual: 2
- Men's Sabre Individual: 1
- Men's Sabre Team: 4
- Men's Team: 3
- Mixed Doubles: 4
- Mixed Team: 13
- Women +67kg: 1
- Women +78 kg: 1
- Women -57 kg: 1
- Women -57kg: 1
- Women's +81kg: 1
- Women's 54kg: 1
- Women's Individual: 3
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Team: 6
**일본 (JPN)**
- B-Girls: 1
- Eventing Team: 4
- Men -60 kg: 1
- Men -66 kg: 1
- Men -73 kg: 1
- Men -81 kg: 1
- Men -90 kg: 1
- Men's 10m Platform: 1
- Men's 400m Individual Medley: 1
- Men's All-Around: 1
- Men's Boulder & Lead: 1
- Men's Foil Team: 4
- Men's Freestyle 57kg: 1
- Men's Freestyle 65kg: 1
- Men's Freestyle 74kg: 1
- Men's Greco-Roman 60kg: 1
- Men's Greco-Roman 77kg: 1
- Men's Horizontal Bar: 1
- Men's Individual: 1
- Men's Individual Stroke Play: 1
- Men's Parallel Bars: 1
- Men's Street: 1
- Men's Team: 5
- Men's Épée Individual: 1
- Men's Épée Team: 4
- Mixed Dinghy: 2
- Mixed Doubles: 2
- Mixed Team: 14
- Women -48 kg: 1
- Women -57 kg: 1
- Women's Doubles: 2
- Women's Foil Team: 4
- Women's Freestyle 50kg: 1
- Women's Freestyle 53kg: 1
- Women's Freestyle 57kg: 1
- Women's Freestyle 62kg: 1
- Women's Freestyle 68kg: 1
- Women's Freestyle 76kg: 1
- Women's Javelin Throw: 1
- Women's Park: 1
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Street: 2
- Women's Team: 3
### 결론
일본은 총 메달 수에서 한국을 초과하며, 다양한 종목에서 메달을 획득하고 있습니다. 한국은 특정 종목에서 강세를 보이고 있지만, 전체적인 메달 수에서는 일본에 비해 부족한 상황입니다.
이 분석에 대해 더 알고 싶거나 추가적인 SQL 쿼리가 필요하시면 말씀해 주세요!
--------------------------------------------------
역할: Human
내용: 응답은 사용자의 질문과 밀접하게 관련이 있으며 메달 획득 패턴 분석에 관하여 상세한 정보를 제공합니다. 그러나 SQL 사용과 관련된 부분은 아예 언급되지 않았으므로, 이 기준은 충족되지 않습니다. 따라서 SQL 사용 적절성 기준을 만족하지 못하므로, 최종적으로는 모든 기준을 충족하지 않습니다. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용: I appreciate your feedback and understand the importance of including SQL elements in my responses. If you have any specific SQL queries or further questions regarding the medal counts or any other data analysis, please let me know, and I'll ensure to provide the relevant SQL details and results. Thank you for your understanding!
--------------------------------------------------
역할: Human
내용: The AI's response acknowledges the user's feedback and offers to provide specific SQL queries if needed, but it does not specifically address the user's original question or include any SQL elements. Therefore, it is relevant in context but lacks the necessary SQL component if the user had requested it. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: ```sql
SELECT COUNT(*) AS total_medals, country_code FROM medallists WHERE country_code IN ('KOR', 'JPN') GROUP BY country_code;
```
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: [(82, 'JPN'), (58, 'KOR')]
--------------------------------------------------
역할: Tool
내용: [('10m Air Pistol Women', 2), ('10m Air Rifle Mixed Team', 2), ('10m Air Rifle Women', 1), ('25m Pistol Women', 1), ('25m Rapid Fire Pistol Men', 1), ('Men +100 kg', 1), ('Men -58kg', 1), ('Men -81 kg', 1), ("Men's 400m Freestyle", 1), ("Men's Individual", 2), ("Men's Sabre Individual", 1), ("Men's Sabre Team", 4), ("Men's Team", 3), ('Mixed Doubles', 4), ('Mixed Team', 13), ('Women +67kg', 1), ('Women +78 kg', 1), ('Women -57 kg', 1), ('Women -57kg', 1), ("Women's +81kg", 1), ("Women's 54kg", 1), ("Women's Individual", 3), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Team", 6)]
--------------------------------------------------
역할: Tool
내용: [('B-Girls', 1), ('Eventing Team', 4), ('Men -60 kg', 1), ('Men -66 kg', 1), ('Men -73 kg', 1), ('Men -81 kg', 1), ('Men -90 kg', 1), ("Men's 10m Platform", 1), ("Men's 400m Individual Medley", 1), ("Men's All-Around", 1), ("Men's Boulder & Lead", 1), ("Men's Foil Team", 4), ("Men's Freestyle 57kg", 1), ("Men's Freestyle 65kg", 1), ("Men's Freestyle 74kg", 1), ("Men's Greco-Roman 60kg", 1), ("Men's Greco-Roman 77kg", 1), ("Men's Horizontal Bar", 1), ("Men's Individual", 1), ("Men's Individual Stroke Play", 1), ("Men's Parallel Bars", 1), ("Men's Street", 1), ("Men's Team", 5), ("Men's Épée Individual", 1), ("Men's Épée Team", 4), ('Mixed Dinghy', 2), ('Mixed Doubles', 2), ('Mixed Team', 14), ('Women -48 kg', 1), ('Women -57 kg', 1), ("Women's Doubles", 2), ("Women's Foil Team", 4), ("Women's Freestyle 50kg", 1), ("Women's Freestyle 53kg", 1), ("Women's Freestyle 57kg", 1), ("Women's Freestyle 62kg", 1), ("Women's Freestyle 68kg", 1), ("Women's Freestyle 76kg", 1), ("Women's Javelin Throw", 1), ("Women's Park", 1), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Street", 2), ("Women's Team", 3)]
--------------------------------------------------
역할: AI
내용: ### 메달 획득 패턴 분석: 한국(KOR) vs 일본(JPN)
#### 1. 총 메달 수
- **일본 (JPN)**: 82개
- **한국 (KOR)**: 58개
일본이 한국보다 총 메달 수에서 우세한 것으로 나타났습니다.
#### 2. 이벤트별 메달 수
**한국 (KOR)**
- 10m Air Pistol Women: 2
- 10m Air Rifle Mixed Team: 2
- 10m Air Rifle Women: 1
- 25m Pistol Women: 1
- 25m Rapid Fire Pistol Men: 1
- Men +100 kg: 1
- Men -58kg: 1
- Men -81 kg: 1
- Men's 400m Freestyle: 1
- Men's Individual: 2
- Men's Sabre Individual: 1
- Men's Sabre Team: 4
- Men's Team: 3
- Mixed Doubles: 4
- Mixed Team: 13
- Women +67kg: 1
- Women +78 kg: 1
- Women -57 kg: 1
- Women -57kg: 1
- Women's +81kg: 1
- Women's 54kg: 1
- Women's Individual: 3
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Team: 6
**일본 (JPN)**
- B-Girls: 1
- Eventing Team: 4
- Men -60 kg: 1
- Men -66 kg: 1
- Men -73 kg: 1
- Men -81 kg: 1
- Men -90 kg: 1
- Men's 10m Platform: 1
- Men's 400m Individual Medley: 1
- Men's All-Around: 1
- Men's Boulder & Lead: 1
- Men's Foil Team: 4
- Men's Freestyle 57kg: 1
- Men's Freestyle 65kg: 1
- Men's Freestyle 74kg: 1
- Men's Greco-Roman 60kg: 1
- Men's Greco-Roman 77kg: 1
- Men's Horizontal Bar: 1
- Men's Individual: 1
- Men's Individual Stroke Play: 1
- Men's Parallel Bars: 1
- Men's Street: 1
- Men's Team: 5
- Men's Épée Individual: 1
- Men's Épée Team: 4
- Mixed Dinghy: 2
- Mixed Doubles: 2
- Mixed Team: 14
- Women -48 kg: 1
- Women -57 kg: 1
- Women's Doubles: 2
- Women's Foil Team: 4
- Women's Freestyle 50kg: 1
- Women's Freestyle 53kg: 1
- Women's Freestyle 57kg: 1
- Women's Freestyle 62kg: 1
- Women's Freestyle 68kg: 1
- Women's Freestyle 76kg: 1
- Women's Javelin Throw: 1
- Women's Park: 1
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Street: 2
- Women's Team: 3
### 결론
일본은 총 메달 수에서 한국을 초과하며, 다양한 종목에서 메달을 획득하고 있습니다. 한국은 특정 종목에서 강세를 보이고 있지만, 전체적인 메달 수에서는 일본에 비해 부족한 상황입니다.
추가적인 SQL 쿼리나 데이터 분석이 필요하시면 언제든지 말씀해 주세요!
--------------------------------------------------
역할: Human
내용: AI의 응답은 사용자가 요청한 메달 획득 패턴 분석에 대한 정보를 잘 제공하고 있습니다. 구체적인 데이터와 함께 일본과 한국의 메달 수 차이와 세부 사항을 설명하였으므로 질문의 연관성을 잘 충족하고 있습니다. 그러나 SQL 사용에 대한 요구사항은 명시되지 않았고, AI는 SQL 쿼리나 분석을 제공하지 않았기에 SQL 사용의 적절성을 평가할 수 없습니다. 그런 점에서 AI의 응답은 전체적으로 유용하지만 SQL 사용 여부를 평가하는 기준은 충족하지 못합니다. 따라서 최종 점수는 False입니다. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: ```sql
SELECT COUNT(*) AS total_medals, country_code FROM medallists WHERE country_code IN ('KOR', 'JPN') GROUP BY country_code;
```
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: [(82, 'JPN'), (58, 'KOR')]
--------------------------------------------------
역할: Tool
내용: [('10m Air Pistol Women', 2), ('10m Air Rifle Mixed Team', 2), ('10m Air Rifle Women', 1), ('25m Pistol Women', 1), ('25m Rapid Fire Pistol Men', 1), ('Men +100 kg', 1), ('Men -58kg', 1), ('Men -81 kg', 1), ("Men's 400m Freestyle", 1), ("Men's Individual", 2), ("Men's Sabre Individual", 1), ("Men's Sabre Team", 4), ("Men's Team", 3), ('Mixed Doubles', 4), ('Mixed Team', 13), ('Women +67kg', 1), ('Women +78 kg', 1), ('Women -57 kg', 1), ('Women -57kg', 1), ("Women's +81kg", 1), ("Women's 54kg", 1), ("Women's Individual", 3), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Team", 6)]
--------------------------------------------------
역할: Tool
내용: [('B-Girls', 1), ('Eventing Team', 4), ('Men -60 kg', 1), ('Men -66 kg', 1), ('Men -73 kg', 1), ('Men -81 kg', 1), ('Men -90 kg', 1), ("Men's 10m Platform", 1), ("Men's 400m Individual Medley", 1), ("Men's All-Around", 1), ("Men's Boulder & Lead", 1), ("Men's Foil Team", 4), ("Men's Freestyle 57kg", 1), ("Men's Freestyle 65kg", 1), ("Men's Freestyle 74kg", 1), ("Men's Greco-Roman 60kg", 1), ("Men's Greco-Roman 77kg", 1), ("Men's Horizontal Bar", 1), ("Men's Individual", 1), ("Men's Individual Stroke Play", 1), ("Men's Parallel Bars", 1), ("Men's Street", 1), ("Men's Team", 5), ("Men's Épée Individual", 1), ("Men's Épée Team", 4), ('Mixed Dinghy', 2), ('Mixed Doubles', 2), ('Mixed Team', 14), ('Women -48 kg', 1), ('Women -57 kg', 1), ("Women's Doubles", 2), ("Women's Foil Team", 4), ("Women's Freestyle 50kg", 1), ("Women's Freestyle 53kg", 1), ("Women's Freestyle 57kg", 1), ("Women's Freestyle 62kg", 1), ("Women's Freestyle 68kg", 1), ("Women's Freestyle 76kg", 1), ("Women's Javelin Throw", 1), ("Women's Park", 1), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Street", 2), ("Women's Team", 3)]
--------------------------------------------------
역할: AI
내용: ### 메달 획득 패턴 분석: 한국(KOR) vs 일본(JPN)
#### 1. 총 메달 수
- **일본 (JPN)**: 82개
- **한국 (KOR)**: 58개
일본이 한국보다 총 메달 수에서 우세한 것으로 나타났습니다.
#### 2. 이벤트별 메달 수
**한국 (KOR)**
- 10m Air Pistol Women: 2
- 10m Air Rifle Mixed Team: 2
- 10m Air Rifle Women: 1
- 25m Pistol Women: 1
- 25m Rapid Fire Pistol Men: 1
- Men +100 kg: 1
- Men -58kg: 1
- Men -81 kg: 1
- Men's 400m Freestyle: 1
- Men's Individual: 2
- Men's Sabre Individual: 1
- Men's Sabre Team: 4
- Men's Team: 3
- Mixed Doubles: 4
- Mixed Team: 13
- Women +67kg: 1
- Women +78 kg: 1
- Women -57 kg: 1
- Women -57kg: 1
- Women's +81kg: 1
- Women's 54kg: 1
- Women's Individual: 3
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Team: 6
**일본 (JPN)**
- B-Girls: 1
- Eventing Team: 4
- Men -60 kg: 1
- Men -66 kg: 1
- Men -73 kg: 1
- Men -81 kg: 1
- Men -90 kg: 1
- Men's 10m Platform: 1
- Men's 400m Individual Medley: 1
- Men's All-Around: 1
- Men's Boulder & Lead: 1
- Men's Foil Team: 4
- Men's Freestyle 57kg: 1
- Men's Freestyle 65kg: 1
- Men's Freestyle 74kg: 1
- Men's Greco-Roman 60kg: 1
- Men's Greco-Roman 77kg: 1
- Men's Horizontal Bar: 1
- Men's Individual: 1
- Men's Individual Stroke Play: 1
- Men's Parallel Bars: 1
- Men's Street: 1
- Men's Team: 5
- Men's Épée Individual: 1
- Men's Épée Team: 4
- Mixed Dinghy: 2
- Mixed Doubles: 2
- Mixed Team: 14
- Women -48 kg: 1
- Women -57 kg: 1
- Women's Doubles: 2
- Women's Foil Team: 4
- Women's Freestyle 50kg: 1
- Women's Freestyle 53kg: 1
- Women's Freestyle 57kg: 1
- Women's Freestyle 62kg: 1
- Women's Freestyle 68kg: 1
- Women's Freestyle 76kg: 1
- Women's Javelin Throw: 1
- Women's Park: 1
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Street: 2
- Women's Team: 3
### 결론
일본은 총 메달 수에서 한국을 초과하며, 다양한 종목에서 메달을 획득하고 있습니다. 한국은 특정 종목에서 강세를 보이고 있지만, 전체적인 메달 수에서는 일본에 비해 부족한 상황입니다.
추가적인 SQL 쿼리나 데이터 분석이 필요하시면 언제든지 말씀해 주세요!
--------------------------------------------------
역할: Human
내용: 응답은 한국과 일본의 메달 획득 패턴을 분석하는 내용으로, 사용자의 질문이 메달 획득 패턴에 대한 분석을 기대했을 것으로 보입니다. 따라서 사용자의 질문과 연관성이 높습니다. 그러나 질문에서 SQL 사용을 요구하는 경우가 없으므로 SQL 사용 적절성 평가 기준은 적용되지 않았습니다. 이 경우, 모든 기준을 충족하지 않았으므로 False가 되어야 합니다. Thus, the score should be: False.
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: ```sql
SELECT COUNT(*) AS total_medals, country_code FROM medallists WHERE country_code IN ('KOR', 'JPN') GROUP BY country_code;
```
--------------------------------------------------
역할: AI
내용:
--------------------------------------------------
역할: Tool
내용: [(82, 'JPN'), (58, 'KOR')]
--------------------------------------------------
역할: Tool
내용: [('10m Air Pistol Women', 2), ('10m Air Rifle Mixed Team', 2), ('10m Air Rifle Women', 1), ('25m Pistol Women', 1), ('25m Rapid Fire Pistol Men', 1), ('Men +100 kg', 1), ('Men -58kg', 1), ('Men -81 kg', 1), ("Men's 400m Freestyle", 1), ("Men's Individual", 2), ("Men's Sabre Individual", 1), ("Men's Sabre Team", 4), ("Men's Team", 3), ('Mixed Doubles', 4), ('Mixed Team', 13), ('Women +67kg', 1), ('Women +78 kg', 1), ('Women -57 kg', 1), ('Women -57kg', 1), ("Women's +81kg", 1), ("Women's 54kg", 1), ("Women's Individual", 3), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Team", 6)]
--------------------------------------------------
역할: Tool
내용: [('B-Girls', 1), ('Eventing Team', 4), ('Men -60 kg', 1), ('Men -66 kg', 1), ('Men -73 kg', 1), ('Men -81 kg', 1), ('Men -90 kg', 1), ("Men's 10m Platform", 1), ("Men's 400m Individual Medley", 1), ("Men's All-Around", 1), ("Men's Boulder & Lead", 1), ("Men's Foil Team", 4), ("Men's Freestyle 57kg", 1), ("Men's Freestyle 65kg", 1), ("Men's Freestyle 74kg", 1), ("Men's Greco-Roman 60kg", 1), ("Men's Greco-Roman 77kg", 1), ("Men's Horizontal Bar", 1), ("Men's Individual", 1), ("Men's Individual Stroke Play", 1), ("Men's Parallel Bars", 1), ("Men's Street", 1), ("Men's Team", 5), ("Men's Épée Individual", 1), ("Men's Épée Team", 4), ('Mixed Dinghy', 2), ('Mixed Doubles', 2), ('Mixed Team', 14), ('Women -48 kg', 1), ('Women -57 kg', 1), ("Women's Doubles", 2), ("Women's Foil Team", 4), ("Women's Freestyle 50kg", 1), ("Women's Freestyle 53kg", 1), ("Women's Freestyle 57kg", 1), ("Women's Freestyle 62kg", 1), ("Women's Freestyle 68kg", 1), ("Women's Freestyle 76kg", 1), ("Women's Javelin Throw", 1), ("Women's Park", 1), ("Women's Sabre Team", 4), ("Women's Singles", 1), ("Women's Street", 2), ("Women's Team", 3)]
--------------------------------------------------
역할: AI
내용: ### 메달 획득 패턴 분석: 한국(KOR) vs 일본(JPN)
#### 1. 총 메달 수
- **일본 (JPN)**: 82개
- **한국 (KOR)**: 58개
일본이 한국보다 총 메달 수에서 우세한 것으로 나타났습니다.
#### 2. 이벤트별 메달 수
**한국 (KOR)**
- 10m Air Pistol Women: 2
- 10m Air Rifle Mixed Team: 2
- 10m Air Rifle Women: 1
- 25m Pistol Women: 1
- 25m Rapid Fire Pistol Men: 1
- Men +100 kg: 1
- Men -58kg: 1
- Men -81 kg: 1
- Men's 400m Freestyle: 1
- Men's Individual: 2
- Men's Sabre Individual: 1
- Men's Sabre Team: 4
- Men's Team: 3
- Mixed Doubles: 4
- Mixed Team: 13
- Women +67kg: 1
- Women +78 kg: 1
- Women -57 kg: 1
- Women -57kg: 1
- Women's +81kg: 1
- Women's 54kg: 1
- Women's Individual: 3
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Team: 6
**일본 (JPN)**
- B-Girls: 1
- Eventing Team: 4
- Men -60 kg: 1
- Men -66 kg: 1
- Men -73 kg: 1
- Men -81 kg: 1
- Men -90 kg: 1
- Men's 10m Platform: 1
- Men's 400m Individual Medley: 1
- Men's All-Around: 1
- Men's Boulder & Lead: 1
- Men's Foil Team: 4
- Men's Freestyle 57kg: 1
- Men's Freestyle 65kg: 1
- Men's Freestyle 74kg: 1
- Men's Greco-Roman 60kg: 1
- Men's Greco-Roman 77kg: 1
- Men's Horizontal Bar: 1
- Men's Individual: 1
- Men's Individual Stroke Play: 1
- Men's Parallel Bars: 1
- Men's Street: 1
- Men's Team: 5
- Men's Épée Individual: 1
- Men's Épée Team: 4
- Mixed Dinghy: 2
- Mixed Doubles: 2
- Mixed Team: 14
- Women -48 kg: 1
- Women -57 kg: 1
- Women's Doubles: 2
- Women's Foil Team: 4
- Women's Freestyle 50kg: 1
- Women's Freestyle 53kg: 1
- Women's Freestyle 57kg: 1
- Women's Freestyle 62kg: 1
- Women's Freestyle 68kg: 1
- Women's Freestyle 76kg: 1
- Women's Javelin Throw: 1
- Women's Park: 1
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Street: 2
- Women's Team: 3
### 결론
일본은 총 메달 수에서 한국을 초과하며, 다양한 종목에서 메달을 획득하고 있습니다. 한국은 특정 종목에서 강세를 보이고 있지만, 전체적인 메달 수에서는 일본에 비해 부족한 상황입니다.
추가적인 SQL 쿼리나 데이터 분석이 필요하시면 언제든지 말씀해 주세요!
--------------------------------------------------
display_message(response)- 일본 (JPN): 82개
- 한국 (KOR): 58개
일본이 한국보다 총 메달 수에서 우세한 것으로 나타났습니다.
한국 (KOR)
- 10m Air Pistol Women: 2
- 10m Air Rifle Mixed Team: 2
- 10m Air Rifle Women: 1
- 25m Pistol Women: 1
- 25m Rapid Fire Pistol Men: 1
- Men +100 kg: 1
- Men -58kg: 1
- Men -81 kg: 1
- Men's 400m Freestyle: 1
- Men's Individual: 2
- Men's Sabre Individual: 1
- Men's Sabre Team: 4
- Men's Team: 3
- Mixed Doubles: 4
- Mixed Team: 13
- Women +67kg: 1
- Women +78 kg: 1
- Women -57 kg: 1
- Women -57kg: 1
- Women's +81kg: 1
- Women's 54kg: 1
- Women's Individual: 3
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Team: 6
일본 (JPN)
- B-Girls: 1
- Eventing Team: 4
- Men -60 kg: 1
- Men -66 kg: 1
- Men -73 kg: 1
- Men -81 kg: 1
- Men -90 kg: 1
- Men's 10m Platform: 1
- Men's 400m Individual Medley: 1
- Men's All-Around: 1
- Men's Boulder & Lead: 1
- Men's Foil Team: 4
- Men's Freestyle 57kg: 1
- Men's Freestyle 65kg: 1
- Men's Freestyle 74kg: 1
- Men's Greco-Roman 60kg: 1
- Men's Greco-Roman 77kg: 1
- Men's Horizontal Bar: 1
- Men's Individual: 1
- Men's Individual Stroke Play: 1
- Men's Parallel Bars: 1
- Men's Street: 1
- Men's Team: 5
- Men's Épée Individual: 1
- Men's Épée Team: 4
- Mixed Dinghy: 2
- Mixed Doubles: 2
- Mixed Team: 14
- Women -48 kg: 1
- Women -57 kg: 1
- Women's Doubles: 2
- Women's Foil Team: 4
- Women's Freestyle 50kg: 1
- Women's Freestyle 53kg: 1
- Women's Freestyle 57kg: 1
- Women's Freestyle 62kg: 1
- Women's Freestyle 68kg: 1
- Women's Freestyle 76kg: 1
- Women's Javelin Throw: 1
- Women's Park: 1
- Women's Sabre Team: 4
- Women's Singles: 1
- Women's Street: 2
- Women's Team: 3
일본은 총 메달 수에서 한국을 초과하며, 다양한 종목에서 메달을 획득하고 있습니다. 한국은 특정 종목에서 강세를 보이고 있지만, 전체적인 메달 수에서는 일본에 비해 부족한 상황입니다.
추가적인 SQL 쿼리나 데이터 분석이 필요하시면 언제든지 말씀해 주세요!
이 예시에서는 에이전트가 처음에 불완전한 응답을 생성한 후, 자체 평가를 통해 개선하는 과정을 볼 수 있습니다.
이러한 예시를 통해 에이전틱 접근법을 사용한 Text2SQL 구현이 복잡한 데이터베이스 질의를 자연어로 처리하는 데 얼마나 효과적인지 확인할 수 있습니다.
Tool을 활용할 수 있는 Agentic 방식으로 Text2SQL 에이전트를 구현하였습니다. Agent가 잘 수행했는지 확인하는 Reflection 그래프를 추가하여 에이전트의 성능을 향상시켰습니다. 이 방법은 Multi-Agent System 방법 중 Supervisor 방식과 유사합니다. supervisor langgraph 구현을 위한 별도의 repo가 있으니 참고하시면 좋을 것 같습니다.
