SQLDatabaseToolkit 功能和配置的详细文档，请前往 API 参考。
SQLDatabaseToolkit 中的工具旨在与 SQL 数据库进行交互。
一个常见的应用是使代理能够使用关系数据库中的数据来回答问题，可能以迭代的方式进行（例如，从错误中恢复）。
⚠️ 安全提示 ⚠️
构建 SQL 数据库的问答系统需要执行模型生成的 SQL 查询。这样做存在固有风险。请确保您的数据库连接权限始终尽可能地限定在您的链/代理所需范围内。这将减轻（但不能消除）构建模型驱动系统的风险。
设置
要启用单个工具的自动跟踪，请设置您的 LangSmith API 密钥：os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
安装
此工具包位于langchain-community 包中：
pip install -qU langchain-community
langgraph 来演示如何将工具包与代理一起使用。使用该工具包本身并不需要它。
pip install -qU langchainhub langgraph
实例化
SQLDatabaseToolkit 工具包需要：
- 一个 SQLDatabase 对象；
- 一个 LLM 或聊天模型（用于实例化 QuerySQLCheckerTool 工具）。
Chinook 数据库。
下面我们将使用 requests 库来拉取 .sql 文件并创建一个内存中的 SQLite 数据库。请注意，这种方法很轻量级，但它是临时的且非线程安全的。如果您愿意，可以按照说明将文件本地保存为 Chinook.db，并通过 db = SQLDatabase.from_uri("sqlite:///Chinook.db") 实例化数据库。
import sqlite3
import requests
from langchain_community.utilities.sql_database import SQLDatabase
from sqlalchemy import create_engine
from sqlalchemy.pool import StaticPool
def get_engine_for_chinook_db():
"""拉取 sql 文件，填充内存数据库，并创建引擎。"""
url = "https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql"
response = requests.get(url)
sql_script = response.text
connection = sqlite3.connect(":memory:", check_same_thread=False)
connection.executescript(sql_script)
return create_engine(
"sqlite://",
creator=lambda: connection,
poolclass=StaticPool,
connect_args={"check_same_thread": False},
)
engine = get_engine_for_chinook_db()
db = SQLDatabase(engine)
# | output: false
# | echo: false
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0)
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
工具
查看可用工具：toolkit.get_tools()
[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 0x103d5fa60>),
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 0x103d5fa60>),
ListSQLDatabaseTool(db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x103d5fa60>),
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 0x103d5fa60>, llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x10742d720>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x10742f7f0>, root_client=<openai.OpenAI object at 0x103d5fac0>, root_async_client=<openai.AsyncOpenAI object at 0x10742d780>, temperature=0.0, model_kwargs={}, openai_api_key=SecretStr('**********')), llm_chain=LLMChain(verbose=False, prompt=PromptTemplate(input_variables=['dialect', 'query'], input_types={}, partial_variables={}, 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 0x10742d720>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x10742f7f0>, root_client=<openai.OpenAI object at 0x103d5fac0>, root_async_client=<openai.AsyncOpenAI object at 0x10742d780>, temperature=0.0, model_kwargs={}, openai_api_key=SecretStr('**********')), output_parser=StrOutputParser(), llm_kwargs={}))]
from langchain_community.tools.sql_database.tool import (
InfoSQLDatabaseTool,
ListSQLDatabaseTool,
QuerySQLCheckerTool,
QuerySQLDatabaseTool,
)
在代理中使用
遵循 SQL 问答教程，下面我们为一个简单的问答代理配备我们工具包中的工具。首先，我们拉取一个相关的提示并填充其所需的参数：from langchain_classic import hub
prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
assert len(prompt_template.messages) == 1
print(prompt_template.input_variables)
['dialect', 'top_k']
system_message = prompt_template.format(dialect="SQLite", top_k=5)
from langchain.agents import create_agent
agent = create_agent(llm, toolkit.get_tools(), system_prompt=system_message)
example_query = "Which country's customers spent the most?"
events = agent.stream(
{"messages": [("user", example_query)]},
stream_mode="values",
)
for event in events:
event["messages"][-1].pretty_print()
================================ Human Message =================================
Which country's customers spent the most?
================================== Ai Message ==================================
Tool Calls:
sql_db_list_tables (call_EBPjyfzqXzFutDn8BklYACLj)
Call ID: call_EBPjyfzqXzFutDn8BklYACLj
Args:
================================= Tool Message =================================
Name: sql_db_list_tables
Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:
sql_db_schema (call_kGcnKpxRVFIY8dPjYIJbRoVU)
Call ID: call_kGcnKpxRVFIY8dPjYIJbRoVU
Args:
table_names: Customer, Invoice, InvoiceLine
================================= Tool Message =================================
Name: sql_db_schema
CREATE TABLE "Customer" (
"CustomerId" INTEGER NOT NULL,
"FirstName" NVARCHAR(40) NOT NULL,
"LastName" NVARCHAR(20) NOT NULL,
"Company" NVARCHAR(80),
"Address" NVARCHAR(70),
"City" NVARCHAR(40),
"State" NVARCHAR(40),
"Country" NVARCHAR(40),
"PostalCode" NVARCHAR(10),
"Phone" NVARCHAR(24),
"Fax" NVARCHAR(24),
"Email" NVARCHAR(60) NOT NULL,
"SupportRepId" INTEGER,
PRIMARY KEY ("CustomerId"),
FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")
)
/*
3 rows from Customer table:
CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId
1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 luisg@embraer.com.br 3
2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 +49 0711 2842222 None leonekohler@surfeu.de 5
3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None ftremblay@gmail.com 3
*/
CREATE TABLE "Invoice" (
"InvoiceId" INTEGER NOT NULL,
"CustomerId" INTEGER NOT NULL,
"InvoiceDate" DATETIME NOT NULL,
"BillingAddress" NVARCHAR(70),
"BillingCity" NVARCHAR(40),
"BillingState" NVARCHAR(40),
"BillingCountry" NVARCHAR(40),
"BillingPostalCode" NVARCHAR(10),
"Total" NUMERIC(10, 2) NOT NULL,
PRIMARY KEY ("InvoiceId"),
FOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")
)
/*
3 rows from Invoice table:
InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total
1 2 2021-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98
2 4 2021-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96
3 8 2021-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94
*/
CREATE TABLE "InvoiceLine" (
"InvoiceLineId" INTEGER NOT NULL,
"InvoiceId" INTEGER NOT NULL,
"TrackId" INTEGER NOT NULL,
"UnitPrice" NUMERIC(10, 2) NOT NULL,
"Quantity" INTEGER NOT NULL,
PRIMARY KEY ("InvoiceLineId"),
FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
FOREIGN KEY("InvoiceId") REFERENCES "Invoice" ("InvoiceId")
)
/*
3 rows from InvoiceLine table:
InvoiceLineId InvoiceId TrackId UnitPrice Quantity
1 1 2 0.99 1
2 1 4 0.99 1
3 2 6 0.99 1
*/
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_cTfI7OrY64FzJaDd49ILFWw7)
Call ID: call_cTfI7OrY64FzJaDd49ILFWw7
Args:
query: SELECT c.Country, SUM(i.Total) AS TotalSpent FROM Customer c JOIN Invoice i ON c.CustomerId = i.CustomerId GROUP BY c.Country ORDER BY TotalSpent DESC LIMIT 1
================================= Tool Message =================================
Name: sql_db_query
[('USA', 523.06)]
================================== Ai Message ==================================
Customers from the USA spent the most, with a total amount spent of $523.06.
example_query = "Who are the top 3 best selling artists?"
events = agent.stream(
{"messages": [("user", example_query)]},
stream_mode="values",
)
for event in events:
event["messages"][-1].pretty_print()
================================ Human Message =================================
Who are the top 3 best selling artists?
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_xAkvYiRFM7nCMKXsDNvk1OMx)
Call ID: call_xAkvYiRFM7nCMKXsDNvk1OMx
Args:
query: SELECT artist_name, SUM(quantity) AS total_sold FROM sales GROUP BY artist_name ORDER BY total_sold DESC LIMIT 3
================================= Tool Message =================================
Name: sql_db_query
Error: (sqlite3.OperationalError) no such table: sales
[SQL: SELECT artist_name, SUM(quantity) AS total_sold FROM sales GROUP BY artist_name ORDER BY total_sold DESC LIMIT 3]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
================================== Ai Message ==================================
Tool Calls:
sql_db_list_tables (call_K4Zvbowsq7XPgGFepbvc5G7i)
Call ID: call_K4Zvbowsq7XPgGFepbvc5G7i
Args:
================================= Tool Message =================================
Name: sql_db_list_tables
Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:
sql_db_schema (call_tUztueSK7VO2klZ99xT4ZVhM)
Call ID: call_tUztueSK7VO2klZ99xT4ZVhM
Args:
table_names: Artist, Album, InvoiceLine
================================= Tool Message =================================
Name: sql_db_schema
CREATE TABLE "Album" (
"AlbumId" INTEGER NOT NULL,
"Title" NVARCHAR(160) NOT NULL,
"ArtistId" INTEGER NOT NULL,
PRIMARY KEY ("AlbumId"),
FOREIGN KEY("ArtistId") REFERENCES "Artist" ("ArtistId")
)
/*
3 rows from Album table:
AlbumId Title ArtistId
1 For Those About To Rock We Salute You 1
2 Balls to the Wall 2
3 Restless and Wild 2
*/
CREATE TABLE "Artist" (
"ArtistId" INTEGER NOT NULL,
"Name" NVARCHAR(120),
PRIMARY KEY ("ArtistId")
)
/*
3 rows from Artist table:
ArtistId Name
1 AC/DC
2 Accept
3 Aerosmith
*/
CREATE TABLE "InvoiceLine" (
"InvoiceLineId" INTEGER NOT NULL,
"InvoiceId" INTEGER NOT NULL,
"TrackId" INTEGER NOT NULL,
"UnitPrice" NUMERIC(10, 2) NOT NULL,
"Quantity" INTEGER NOT NULL,
PRIMARY KEY ("InvoiceLineId"),
FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
FOREIGN KEY("InvoiceId") REFERENCES "Invoice" ("InvoiceId")
)
/*
3 rows from InvoiceLine table:
InvoiceLineId InvoiceId TrackId UnitPrice Quantity
1 1 2 0.99 1
2 1 4 0.99 1
3 2 6 0.99 1
*/
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_tVtLQIRPmCM6pukgpHFfq86A)
Call ID: call_tVtLQIRPmCM6pukgpHFfq86A
Args:
query: SELECT Artist.Name AS artist_name, SUM(InvoiceLine.Quantity) AS total_sold FROM Artist JOIN Album ON Artist.ArtistId = Album.ArtistId JOIN Track ON Album.AlbumId = Track.AlbumId JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY total_sold DESC LIMIT 3
================================= Tool Message =================================
Name: sql_db_query
[('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]
================================== Ai Message ==================================
The top 3 best selling artists are:
1. Iron Maiden - 140 units sold
2. U2 - 107 units sold
3. Metallica - 91 units sold
特定功能
SQLDatabaseToolkit 实现了一个 .get_context 方法，作为在提示或其他上下文中使用的便捷工具。
⚠️ 免责声明 ⚠️：代理可能会生成插入/更新/删除查询。当不期望发生这种情况时，请使用自定义提示或创建没有写入权限的 SQL 用户。
最终用户可能会通过提出一个简单的问题（例如“运行可能的最大查询”）来使您的 SQL 数据库过载。生成的查询可能如下所示：
SELECT * FROM "public"."users"
JOIN "public"."user_permissions" ON "public"."users".id = "public"."user_permissions".user_id
JOIN "public"."projects" ON "public"."users".id = "public"."projects".user_id
JOIN "public"."events" ON "public"."projects".id = "public"."events".project_id;
API 参考
有关所有 SQLDatabaseToolkit 功能和配置的详细文档，请前往 API 参考。将这些文档连接到 Claude、VSCode 等，通过 MCP 获取实时答案。