[PYTHON/LANGCHAIN] QuerySQLDataBaseTool 클래스 : 쿼리 생성/실행 체인을 통해 질의 응답하기

■ QuerySQLDataBaseTool 클래스를 사용해 쿼리 생성/실행 체인을 통해 질의 응답하는 방법을 보여준다.

▶ main.py


import os

from operator import itemgetter

from langchain_openai                            import ChatOpenAI
from langchain_community.utilities               import SQLDatabase
from langchain.chains                            import create_sql_query_chain
from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool
from langchain_core.prompts                      import PromptTemplate
from langchain_core.runnables                    import RunnablePassthrough
from langchain_core.output_parsers               import StrOutputParser

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

# OpenAI 채팅 모델을 설정한다.
chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

# SQLITE 데이터베이스를 초기화한다.
sqlDatabase = SQLDatabase.from_uri("sqlite:///chinook.db")

# 쿼리 생성 체인을 생성한다.
generateQueryRunnableSequence = create_sql_query_chain(chatOpenAI, sqlDatabase)

# 쿼리 SQL 데이터베이스 도구를 설정한다.
querySQLDataBaseTool = QuerySQLDataBaseTool(db = sqlDatabase)

# 답변 프롬프트 템플리트를 설정한다.
answerPromptTemplate = PromptTemplate.from_template(
    """Given the following user question, corresponding SQL query, and SQL result, answer the user question.

Question: {question}
SQL Query: {query}
SQL Result: {result}
Answer: """
)

# 답변 체인을 설정한다.
answerRunnableSequence = (RunnablePassthrough.assign(query = generateQueryRunnableSequence).assign(result = itemgetter("query") | querySQLDataBaseTool) | answerPromptTemplate | chatOpenAI | StrOutputParser())

# 답변 체인을 실행한다.
answerString = answerRunnableSequence.invoke({"question" : "How many employees are there"})

print(answerString)

import os

from operator import itemgetter

from langchain_openai import ChatOpenAI

from langchain_community.utilities import SQLDatabase

from langchain.chains import create_sql_query_chain

from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool

from langchain_core.prompts import PromptTemplate

from langchain_core.runnables import RunnablePassthrough

from langchain_core.output_parsers import StrOutputParser

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

# OpenAI 채팅 모델을 설정한다.

chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125")

# SQLITE 데이터베이스를 초기화한다.

sqlDatabase = SQLDatabase.from_uri("sqlite:///chinook.db")

# 쿼리 생성 체인을 생성한다.

generateQueryRunnableSequence = create_sql_query_chain(chatOpenAI, sqlDatabase)

# 쿼리 SQL 데이터베이스 도구를 설정한다.

querySQLDataBaseTool = QuerySQLDataBaseTool(db = sqlDatabase)

# 답변 프롬프트 템플리트를 설정한다.

answerPromptTemplate = PromptTemplate.from_template(

"""Given the following user question, corresponding SQL query, and SQL result, answer the user question.

Question: {question}

SQL Query: {query}

SQL Result: {result}

Answer: """

)

# 답변 체인을 설정한다.

answerRunnableSequence = (RunnablePassthrough.assign(query = generateQueryRunnableSequence).assign(result = itemgetter("query") | querySQLDataBaseTool) | answerPromptTemplate | chatOpenAI | StrOutputParser())

# 답변 체인을 실행한다.

answerString = answerRunnableSequence.invoke({"question" : "How many employees are there"})

print(answerString)

▶ requirements.txt


aiohttp==3.9.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
certifi==2024.6.2
charset-normalizer==3.3.2
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.1
frozenlist==1.4.1
greenlet==3.0.3
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.3
langchain-community==0.2.4
langchain-core==0.2.5
langchain-openai==0.1.8
langchain-text-splitters==0.2.1
langsmith==0.1.77
marshmallow==3.21.3
multidict==6.0.5
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.33.0
orjson==3.10.4
packaging==23.2
pydantic==2.7.4
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.30
tenacity==8.3.0
tiktoken==0.7.0
tqdm==4.66.4
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.1
yarl==1.9.4

aiohttp==3.9.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

async-timeout==4.0.3

attrs==23.2.0

certifi==2024.6.2

charset-normalizer==3.3.2

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.1

frozenlist==1.4.1

greenlet==3.0.3

h11==0.14.0

httpcore==1.0.5

httpx==0.27.0

idna==3.7

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.3

langchain-community==0.2.4

langchain-core==0.2.5

langchain-openai==0.1.8

langchain-text-splitters==0.2.1

langsmith==0.1.77

marshmallow==3.21.3

multidict==6.0.5

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.33.0

orjson==3.10.4

packaging==23.2

pydantic==2.7.4

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.30

tenacity==8.3.0

tiktoken==0.7.0

tqdm==4.66.4

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.1

yarl==1.9.4

※ pip install langchain langchain-community langchain-openai 명령을 실행했다.

chinook.zip