SQLDatabaseToolkit

这将帮助你开始使用SQL数据库工具包。有关所有SQLDatabaseToolkit功能和配置的详细文档，请参阅API参考。

工具在SQLDatabaseToolkit中设计用于与SQL数据库交互。

常见的应用是使智能体能够使用关系数据库中的数据回答问题，可能会以迭代的方式进行（例如，在出错时恢复）。

⚠️ 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严格地限定在您的链/智能体需求范围内。这将缓解（但无法完全消除）构建模型驱动系统的风险。关于一般安全最佳实践的更多信息，请参阅此处。

设置

要启用单个工具的自动跟踪，请设置您的LangSmithAPI密钥：

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

这个工具包位于langchain-community包中:

%pip install --upgrade --quiet  langchain-community

为了演示目的，我们将从LangChain Hub 访问一个提示。我们还将需要 langgraph 来演示工具包与代理的使用方式。这并不是使用该工具包所必需的。

%pip install --upgrade --quiet langchainhub langgraph

Instantiation

The SQLDatabaseToolkit 工具包需要:

a SQL数据库对象；
a LLM或聊天模型（用于实例化QuerySQLCheckerTool工具）。

以下是创建工具集的实例。首先，我们来创建一个数据库对象。

此指南使用基于这些说明的Chinook示例数据库。

以下我们将使用requests库来拉取.sql文件并创建一个内存中的SQLite数据库。请注意，这种方法轻量级但临时且不是线程安全的。如果你想的话，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():
    """Pull sql file, populate in-memory database, and create engine."""
    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)

API 参考:SQL数据库

我们还需要一个大语言模型或聊天模型:

选择聊天模型:

pip install -qU "langchain[openai]"

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

现在我们可以实例化工具包:

from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit

toolkit = SQLDatabaseToolkit(db=db, llm=llm)

API 参考:SQLDatabaseToolkit

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={}))]

您可以直接使用单独的工具：

from langchain_community.tools.sql_database.tool import (
    InfoSQLDatabaseTool,
    ListSQLDatabaseTool,
    QuerySQLCheckerTool,
    QuerySQLDatabaseTool,
)

API 参考:InfoSQL数据库工具 |ListSQLDatabaseTool |QuerySQLCheckerTool |查询SQL数据库工具

使用于代理

在SQL Q&A教程之后，我们接下来将一个简单的问答代理装备上工具箱中的工具。首先我们拉取一个相关的提示，并填充其所需的参数：

from langchain import hub

prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")

assert len(prompt_template.messages) == 1
print(prompt_template.input_variables)

API 参考:中心

['dialect', 'top_k']

system_message = prompt_template.format(dialect="SQLite", top_k=5)

然后我们实例化代理:

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(llm, toolkit.get_tools(), prompt=system_message)

API 参考:create_react_agent

发布一个查询:

example_query = "Which country's customers spent the most?"

events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

================================[1m Human Message [0m=================================

Which country's customers spent the most?
==================================[1m Ai Message [0m==================================
Tool Calls:
  sql_db_list_tables (call_EBPjyfzqXzFutDn8BklYACLj)
 Call ID: call_EBPjyfzqXzFutDn8BklYACLj
  Args:
=================================[1m Tool Message [0m=================================
Name: sql_db_list_tables

Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
==================================[1m Ai Message [0m==================================
Tool Calls:
  sql_db_schema (call_kGcnKpxRVFIY8dPjYIJbRoVU)
 Call ID: call_kGcnKpxRVFIY8dPjYIJbRoVU
  Args:
    table_names: Customer, Invoice, InvoiceLine
=================================[1m Tool Message [0m=================================
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
*/
==================================[1m Ai Message [0m==================================
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
=================================[1m Tool Message [0m=================================
Name: sql_db_query

[('USA', 523.06)]
==================================[1m Ai Message [0m==================================

Customers from the USA spent the most, with a total amount spent of $523.06.

我们也可以观察到智能体从错误中恢复过来:

example_query = "Who are the top 3 best selling artists?"

events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

================================[1m Human Message [0m=================================

Who are the top 3 best selling artists?
==================================[1m Ai Message [0m==================================
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
=================================[1m Tool Message [0m=================================
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)
==================================[1m Ai Message [0m==================================
Tool Calls:
  sql_db_list_tables (call_K4Zvbowsq7XPgGFepbvc5G7i)
 Call ID: call_K4Zvbowsq7XPgGFepbvc5G7i
  Args:
=================================[1m Tool Message [0m=================================
Name: sql_db_list_tables

Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
==================================[1m Ai Message [0m==================================
Tool Calls:
  sql_db_schema (call_tUztueSK7VO2klZ99xT4ZVhM)
 Call ID: call_tUztueSK7VO2klZ99xT4ZVhM
  Args:
    table_names: Artist, Album, InvoiceLine
=================================[1m Tool Message [0m=================================
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
*/
==================================[1m Ai Message [0m==================================
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
=================================[1m Tool Message [0m=================================
Name: sql_db_query

[('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]
==================================[1m Ai Message [0m==================================

The top 3 best selling artists are:
1. Iron Maiden - 140 units sold
2. U2 - 107 units sold
3. Metallica - 91 units sold

具体功能

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一种便利。

⚠️免责声明⚠️ : 代理可能会生成插入/更新/删除查询。如果不期望这种情况，请使用自定义提示或创建一个没有写权限的SQL用户。

The final user might overload your SQL database by asking a simple question such as "run the biggest query possible". The generated query might look like:

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;

对于事务型SQL数据库，如果其中一个表包含数百万行数据，则查询可能会对其他使用同一数据库的应用程序造成麻烦。

大多数面向数据仓库的数据库支持按用户限制资源使用量的功能。

API 参考

详细介绍了所有SQLDatabaseToolkit功能和配置的文档，请参阅API参考。

工具概念指南概念引导
Tool 如何使用指南

设置​

安装​

Instantiation​

工具​

使用于代理​

具体功能​

API 参考​

相关​

设置

安装

Instantiation

工具

使用于代理

具体功能

API 参考

相关