[PYTHON/LANGCHAIN] SQLDatabaseToolkit 클래스 : get_tools 메소드를 사용해 도구 리스트 구하기

■ SQLDatabaseToolkit 클래스의 get_tools 메소드를 사용해 도구 리스트를 구하는 방법을 보여준다.

▶ main.py


import os

from langchain_community.utilities      import SQLDatabase
from langchain_openai                   import ChatOpenAI
from langchain_community.agent_toolkits import SQLDatabaseToolkit

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

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

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

# SQL 데이터베이스 툴킷을 설정한다.
sqlDatabaseToolkit = SQLDatabaseToolkit(db = sqlDatabase, llm = chatOpenAI)

# SQL 데이터베이스 툴킷의 도구 리스트를 설정한다.
toolList = sqlDatabaseToolkit.get_tools()

print(toolList)

"""
[QuerySQLDataBaseTool(description="Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields.", db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>), InfoSQLDatabaseTool(description='Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3', db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>), ListSQLDatabaseTool(db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>), QuerySQLCheckerTool(description='Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!', db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>, llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7f9408be6470>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7f9408be7b80>, model_name='gpt-3.5-turbo-0125', openai_api_key=SecretStr('**********'), openai_proxy=''), llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['dialect', 'query'], template='\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n\nOutput the final SQL query only.\n\nSQL Query: '), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7f9408be6470>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7f9408be7b80>, model_name='gpt-3.5-turbo-0125', openai_api_key=SecretStr('**********'), openai_proxy='')))]
"""

import os

from langchain_community.utilities import SQLDatabase

from langchain_openai import ChatOpenAI

from langchain_community.agent_toolkits import SQLDatabaseToolkit

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

# SQLITE 데이터베이스를 초기화한다.

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

# OpenAI 채팅 모델을 설정한다.

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

# SQL 데이터베이스 툴킷을 설정한다.

sqlDatabaseToolkit = SQLDatabaseToolkit(db = sqlDatabase, llm = chatOpenAI)

# SQL 데이터베이스 툴킷의 도구 리스트를 설정한다.

toolList = sqlDatabaseToolkit.get_tools()

print(toolList)

"""

[QuerySQLDataBaseTool(description="Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields.", db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>), InfoSQLDatabaseTool(description='Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3', db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>), ListSQLDatabaseTool(db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>), QuerySQLCheckerTool(description='Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!', db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x7f9408f5d540>, llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7f9408be6470>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7f9408be7b80>, model_name='gpt-3.5-turbo-0125', openai_api_key=SecretStr('**********'), openai_proxy=''), llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['dialect', 'query'], template='\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n\nOutput the final SQL query only.\n\nSQL Query: '), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7f9408be6470>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7f9408be7b80>, model_name='gpt-3.5-turbo-0125', openai_api_key=SecretStr('**********'), openai_proxy='')))]

"""

▶ 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==2.7.3
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

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.

chinook.zip