■ QuerySQLDataBaseTool 클래스를 사용해 쿼리 생성 체인에서 생성한 쿼리를 실행하는 방법을 보여준다.
▶ main.py
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import os 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 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) # 쿼리 실행 체인을 설정한다. executeQueryRunnableSequence = generateQueryRunnableSequence | querySQLDataBaseTool # 질의 응답을 실행한다. resultTupleList = executeQueryRunnableSequence.invoke({"question" : "How many employees are there?"}) print(resultTupleList) """ [(8,)] """ |
▶ requirements.txt
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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==2.7.3 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.
