SQLDatabase 工具包

这将帮助您开始使用 SQL 数据库工具包。有关所有SQLDatabaseToolkit功能和配置的详细文档，请访问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（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。这样做存在固有的风险。确保您的数据库连接权限始终尽可能狭窄地限定在您的链/代理的需求范围内。这将减轻（但不消除）构建模型驱动系统的风险。有关一般安全最佳实践的更多信息，请参阅此处。

设置

如果您希望从单个工具的运行中获取自动跟踪，您还可以通过取消以下注释来设置您的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 --upgrade --quiet  langchain-community

出于演示目的，我们将访问 LangChain Hub中的提示。我们还需要langgraph来演示如何将工具包与代理一起使用。这对于使用工具包不是必需的。

%pip install --upgrade --quiet 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）。

下面，我们使用这些对象实例化工具包。让我们首先创建一个数据库对象。

本指南使用基于这些说明的示例Chinook数据库。

下面我们将使用requests库来提取.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，但它是短暂的并且不是线程安全的。如果您愿意，您可以按照说明将文件保存在本地作为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：SQLDatabase

我们还需要一个 LLM 或聊天模型

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

pip install -qU langchain-google-vertexai

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

os.environ["NVIDIA_API_KEY"] = getpass.getpass()

from langchain import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

现在我们可以实例化工具包了

from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit

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

API 参考：SQLDatabaseToolkit

工具

查看可用的工具

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 0x105e02860>),
 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 0x105e02860>),
 ListSQLDatabaseTool(db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x105e02860>),
 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 0x105e02860>, llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x1148a97b0>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x1148aaec0>, temperature=0.0, 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 0x1148a97b0>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x1148aaec0>, temperature=0.0, openai_api_key=SecretStr('**********'), openai_proxy='')))]

API 参考

在代理中使用

按照SQL 问答教程，下面我们将为一个简单的问答代理配备工具包中的工具。首先，我们提取一个相关的提示并使用其所需的参数填充它

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)

['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(), state_modifier=system_message
)

API 参考：create_react_agent

并向其发出查询

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_eiheSxiL0s90KE50XyBnBtJY)
 Call ID: call_eiheSxiL0s90KE50XyBnBtJY
  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_YKwGWt4UUVmxxY7vjjBDzFLJ)
 Call ID: call_YKwGWt4UUVmxxY7vjjBDzFLJ
  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	[email protected]	3
2	Leonie	Köhler	None	Theodor-Heuss-Straße 34	Stuttgart	None	Germany	70174	+49 0711 2842222	None	[email protected]	5
3	François	Tremblay	None	1498 rue Bélanger	Montréal	QC	Canada	H2G 1A7	+1 (514) 721-4711	None	[email protected]	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_7WBDcMxl1h7MnI05njx1q8V9)
 Call ID: call_7WBDcMxl1h7MnI05njx1q8V9
  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', 523.0600000000003)]
==================================[1m Ai Message [0m==================================

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

我们还可以观察代理如何从错误中恢复

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_9F6Bp2vwsDkeLW6FsJFqLiet)
 Call ID: call_9F6Bp2vwsDkeLW6FsJFqLiet
  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_Gx5adzWnrBDIIxzUDzsn83zO)
 Call ID: call_Gx5adzWnrBDIIxzUDzsn83zO
  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_ftywrZgEgGWLrnk9dYC0xtZv)
 Call ID: call_ftywrZgEgGWLrnk9dYC0xtZv
  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_i6n3lmS7E2ZivN758VOayTiy)
 Call ID: call_i6n3lmS7E2ZivN758VOayTiy
  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

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利。

⚠️ 免责声明 ⚠️：代理可能会生成插入/更新/删除查询。当不期望这样做时，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询问诸如“运行尽可能大的查询”之类的简单问题来使您的 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;

对于事务性 SQL 数据库，如果上述表格之一包含数百万行，则该查询可能会给使用同一数据库的其他应用程序带来麻烦。

大多数面向数据仓库的数据库支持用户级配额，用于限制资源使用。

API 参考

有关所有 SQLDatabaseToolkit 功能和配置的详细文档，请访问API 参考。

工具概念指南
工具操作指南

SQLDatabase 工具包

设置

安装

实例化

工具

在代理中使用

特定功能

API 参考

此页面是否有帮助？

您还可以留下详细的反馈在 GitHub 上.

设置​

安装​

实例化​

工具​

在代理中使用​

特定功能​

API 参考​

相关​

此页面是否有帮助？

您还可以留下详细的反馈 在 GitHub 上.

设置

安装

实例化

工具

在代理中使用

特定功能

API 参考

相关

您还可以留下详细的反馈在 GitHub 上.