如何在 SQL 问答过程中执行查询验证

也许任何 SQL 链或代理中最容易出错的部分是编写有效且安全的 SQL 查询。在本指南中，我们将介绍一些验证查询和处理无效查询的策略。

我们将介绍：

1. 将 “query validator” 步骤附加到查询生成;
2. 及时进行工程设计以减少错误发生率。

设置

首先，获取所需的包并设置环境变量：

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

# Uncomment the below to use LangSmith. Not required.
# import os
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
# os.environ["LANGSMITH_TRACING"] = "true"

以下示例将使用与 Chinook 数据库的 SQLite 连接。按照这些安装步骤创建Chinook.db与此笔记本位于同一目录中：

• 将此文件另存为Chinook_Sqlite.sql
• 跑sqlite3 Chinook.db
• 跑.read Chinook_Sqlite.sql
• 测试SELECT * FROM Artist LIMIT 10;

现在Chinook.db在我们的目录中，我们可以使用 SQLAlchemy-drivenSQLDatabase类：

from langchain_community.utilities import SQLDatabase

db = SQLDatabase.from_uri("sqlite:///Chinook.db")
print(db.dialect)
print(db.get_usable_table_names())
print(db.run("SELECT * FROM Artist LIMIT 10;"))

API 参考：SQLDatabase

sqlite
['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']
[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham')]

查询检查器

也许最简单的策略是要求模型本身检查原始查询中的常见错误。假设我们有以下 SQL 查询链：

选择聊天模式：

OpenAI

Anthropic

天蓝色

谷歌双子座

谷歌顶点

AWS

Groq

Cohere

英伟达

烟花 AI

Mistral AI

携手 AI

IBM 沃森

Databricks

xAI

困惑

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")

from langchain.chains import create_sql_query_chain

chain = create_sql_query_chain(llm, db)

API 参考：create_sql_query_chain

我们想要验证它的输出。我们可以通过使用第二个 prompt 和 model 调用来扩展链：

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

system = """Double check the user's {dialect} query for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins

If there are any of the above mistakes, rewrite the query.
If there are no mistakes, just reproduce the original query with no further commentary.

Output the final SQL query only."""
prompt = ChatPromptTemplate.from_messages(
    [("system", system), ("human", "{query}")]
).partial(dialect=db.dialect)
validation_chain = prompt | llm | StrOutputParser()

full_chain = {"query": chain} | validation_chain

API 参考：StrOutputParser | 聊天提示模板

query = full_chain.invoke(
    {
        "question": "What's the average Invoice from an American customer whose Fax is missing since 2003 but before 2010"
    }
)
print(query)

SELECT AVG(i.Total) AS AverageInvoice
FROM Invoice i
JOIN Customer c ON i.CustomerId = c.CustomerId
WHERE c.Country = 'USA'
AND c.Fax IS NULL
AND i.InvoiceDate >= '2003-01-01' 
AND i.InvoiceDate < '2010-01-01'

请注意我们如何在 Langsmith 跟踪中看到链的两个步骤。

db.run(query)

'[(6.632999999999998,)]'

这种方法的明显缺点是我们需要进行两次模型调用，而不是一次来生成查询。为了解决这个问题，我们可以尝试在单个模型调用中执行查询生成和查询检查：

system = """You are a {dialect} expert. Given an input question, create a syntactically correct {dialect} query to run.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per {dialect}. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Pay attention to use date('now') function to get the current date, if the question involves "today".

Only use the following tables:
{table_info}

Write an initial draft of the query. Then double check the {dialect} query for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins

Use format:

First draft: <<FIRST_DRAFT_QUERY>>
Final answer: <<FINAL_ANSWER_QUERY>>
"""
prompt = ChatPromptTemplate.from_messages(
    [("system", system), ("human", "{input}")]
).partial(dialect=db.dialect)


def parse_final_answer(output: str) -> str:
    return output.split("Final answer: ")[1]


chain = create_sql_query_chain(llm, db, prompt=prompt) | parse_final_answer
prompt.pretty_print()

================================ System Message ================================

You are a {dialect} expert. Given an input question, create a syntactically correct {dialect} query to run.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per {dialect}. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Pay attention to use date('now') function to get the current date, if the question involves "today".

Only use the following tables:
{table_info}

Write an initial draft of the query. Then double check the {dialect} query for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins

Use format:

First draft: <<FIRST_DRAFT_QUERY>>
Final answer: <<FINAL_ANSWER_QUERY>>


================================ Human Message =================================

{input}

query = chain.invoke(
    {
        "question": "What's the average Invoice from an American customer whose Fax is missing since 2003 but before 2010"
    }
)
print(query)

SELECT AVG(i."Total") AS "AverageInvoice"
FROM "Invoice" i
JOIN "Customer" c ON i."CustomerId" = c."CustomerId"
WHERE c."Country" = 'USA'
AND c."Fax" IS NULL
AND i."InvoiceDate" BETWEEN '2003-01-01' AND '2010-01-01';

db.run(query)

'[(6.632999999999998,)]'

人机协同

在某些情况下，我们的数据足够敏感，以至于我们绝不想在没有人工批准的情况下执行 SQL 查询。前往工具使用：人机协同页面，了解如何将人机协同添加到任何工具、链或代理。

错误处理

在某些时候，模型会犯错误并制作无效的 SQL 查询。或者我们的数据库会出现问题。否则模型 API 将关闭。我们希望为我们的 chain 和 agent 添加一些错误处理行为，以便在这些情况下我们优雅地失败，甚至可能自动恢复。要了解如何使用工具进行错误处理，请前往工具使用：错误处理页面。