LangChain 0.2 - 基于 SQL 数据构建问答系统

本文翻译整理自：Build a Question/Answering system over SQL data
https://python.langchain.com/v0.2/docs/tutorials/sql_qa/

文章目录

- 一、项目说明
- - ⚠️ 安全说明⚠️
  - 架构
- 二、设置
- 三、Chains
- 四、Agents
- - 1、System Prompt
  - 2、初始化代理
  - 3、处理高基数列

一、项目说明

使 LLM 系统能够查询结构化数据与非结构化文本数据在性质上可能有所不同。后者通常生成可在向量数据库中搜索的文本，而结构化数据的方法通常是让 LLM 编写和执行 DSL（例如 SQL）中的查询。在本指南中，我们将介绍在数据库中的表格数据上创建问答系统的基本方法。我们将介绍使用链和代理的实现。这些系统将允许我们询问有关数据库中数据的问题并得到自然语言答案。两者之间的主要区别在于，我们的代理可以根据需要多次循环查询数据库以回答问题。

⚠️ 安全说明⚠️

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缩小以满足您的链/代理的需求。这将减轻但不会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。

架构

从高层次来看，这些系统的步骤如下：

将问题转换为 DSL 查询：模型将用户输入转换为 SQL 查询。
执行 SQL 查询：执行查询。
回答问题：模型使用查询结果响应用户输入。

请注意，查询 CSV 中的数据可以采用类似的方法。有关更多详细信息，请参阅我们关于通过 CSV 数据进行问答的操作指南。

示例代码：sql_usecase

二、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

我们将在本指南中使用 OpenAI 模型。

import getpass
import os

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

# Uncomment the below to use LangSmith. Not required.
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
# os.environ["LANGCHAIN_TRACING_V2"] = "true"

以下示例将使用与 Chinook 数据库的 SQLite 连接。请按照以下安装步骤Chinook.db在与此笔记本相同的目录中创建：

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

现在，Chinhook.db在我们的目录中，我们可以使用 SQLAlchemy 驱动的SQLDatabase类与它交互：

from langchain_community.utilities import SQLDatabase

db = SQLDatabase.from_uri("sqlite:///Chinook.db")
print(db.dialect)
print(db.get_usable_table_names())
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')]"

太棒了！我们有一个可以查询的 SQL 数据库。现在让我们尝试将其连接到 LLM。

三、Chains

链（即 LangChain Runnable的组合）支持步骤可预测的应用程序。我们可以创建一个简单的链，它接受一个问题并执行以下操作：

将问题转换为 SQL 查询；
执行查询；
用结果来回答原始问题。

这种安排不支持某些场景。例如，该系统将对任何用户输入执行 SQL 查询——甚至是“你好”。重要的是，正如我们将在下面看到的，有些问题需要多个查询才能回答。我们将在代理部分中解决这些场景。

1、将问题转换为 SQL查询

SQL 链或代理的第一步是获取用户输入并将其转换为 SQL 查询。LangChain 带有一个内置链用于此：create_sql_query_chain。

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-3.5-turbo-0125")

from langchain.chains import create_sql_query_chain

chain = create_sql_query_chain(llm, db)
response = chain.invoke({"question": "How many employees are there"})
response

API 参考：create_sql_query_chain

'SELECT COUNT("EmployeeId") AS "TotalEmployees" FROM "Employee"\nLIMIT 1;'

我们可以执行查询来确保它有效：

db.run(response)
# -> '[(8,)]'

我们可以查看LangSmith 跟踪，以更好地了解此链正在做什么。我们还可以直接检查链的提示。查看提示（如下），我们可以看到它是：

特定于方言。在这种情况下，它明确引用 SQLite。
具有所有可用表的定义。
每个表有三个示例行。

这种技术受到类似论文的启发，这些论文建议显示示例行并明确说明表格可以提高性能。我们也可以像这样检查完整的提示：

chain.get_prompts()[0].pretty_print()

You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. 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".

Use the following format:

Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here

Only use the following tables:
[33;1m[1;3m{table_info}[0m

Question: [33;1m[1;3m{input}[0m

2、执行 SQL查询

现在我们已经生成了 SQL 查询，我们将要执行它。**这是创建 SQL 链中最危险的部分。**请仔细考虑是否可以对数据运行自动查询。尽可能减少数据库连接权限。考虑在查询执行之前为链添加人工批准步骤（见下文）。

我们可以使用以下QuerySQLDatabaseTool方法轻松地将查询执行添加到我们的链中：

from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool

execute_query = QuerySQLDataBaseTool(db=db)
write_query = create_sql_query_chain(llm, db)
chain = write_query | execute_query
chain.invoke({"question": "How many employees are there"})
# -> '[(8,)]'

API 参考：QuerySQLDataBaseTool

3、回答问题

现在我们已经有了自动生成和执行查询的方法，我们只需要将原始问题和 SQL 查询结果结合起来即可生成最终答案。我们可以通过再次将问题和结果传递给 LLM 来实现这一点：

from operator import itemgetter

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough

answer_prompt = PromptTemplate.from_template(
    """Given the following user question, corresponding SQL query, and SQL result, answer the user question.

Question: {question}
SQL Query: {query}
SQL Result: {result}
Answer: """
)

chain = (
    RunnablePassthrough.assign(query=write_query).assign(
        result=itemgetter("query") | execute_query
    )
    | answer_prompt
    | llm
    | StrOutputParser()
)

chain.invoke({"question": "How many employees are there"})
# -> 'There are a total of 8 employees.'

API 参考：StrOutputParser | PromptTemplate | RunnablePassthrough

让我们回顾一下上面的 LCEL 中发生了什么。假设调用了此链。

在第一个之后RunnablePassthrough.assign，我们有一个包含两个元素的可运行对象：
{"question": question, "query": write_query.invoke(question)}
Wherewrite_query将生成一个 SQL 查询来回答问题。
在第二个之后RunnablePassthrough.assign，我们添加了第三个元素，"result"它包含execute_query.invoke(query)，其中query是在上一步中计算的。
这三个输入被格式化为提示并传递到 LLM。
提取StrOutputParser()输出消息的字符串内容。

请注意，我们将 LLM、工具、提示和其他链组合在一起，但由于每个链都实现了 Runnable 接口，因此它们的输入和输出可以以合理的方式绑定在一起。

后续

对于更复杂的查询生成，我们可能需要创建少样本提示或添加查询检查步骤。有关此类高级技术以及更多内容，请查看：

提示策略：先进的提示工程技术。
查询检查：添加查询验证和错误处理。
大型数据库：处理大型数据库的技术。

四、Agents

LangChain 有一个 SQL 代理，它提供了比链更灵活的与 SQL 数据库交互的方式。使用 SQL 代理的主要优点是：

它可以根据数据库的模式以及数据库的内容（如描述特定的表）回答问题。
它可以通过运行生成的查询、捕获回溯并正确地重新生成来从错误中恢复。
它可以根据需要多次查询数据库来回答用户的问题。
它将仅通过从相关表中检索模式来保存令牌。

为了初始化代理，我们将使用SQLDatabaseToolkit创建一组工具：

创建并执行查询
检查查询语法
检索表描述
… 和更多

from langchain_community.agent_toolkits import SQLDatabaseToolkit

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

tools = toolkit.get_tools()

tools

API 参考：SQLDatabaseToolkit

[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 0x113403b50>),
 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 0x113403b50>),
 ListSQLDatabaseTool(db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x113403b50>),
 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 0x113403b50>, llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x115b7e890>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x115457e10>, 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 0x115b7e890>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x115457e10>, temperature=0.0, openai_api_key=SecretStr('**********'), openai_proxy='')))]

1、System Prompt

我们还想为我们的代理创建一个系统提示。这将包括如何操作的说明。

from langchain_core.messages import SystemMessage

SQL_PREFIX = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

To start you should ALWAYS look at the tables in the database to see what you can query.
Do NOT skip this step.
Then you should query the schema of the most relevant tables."""

system_message = SystemMessage(content=SQL_PREFIX)

API 参考：SystemMessage

2、初始化代理

首先，获取所需的包LangGraph

%pip install --upgrade --quiet langgraph

我们将使用预先构建的LangGraph代理来构建我们的代理

from langchain_core.messages import HumanMessage
from langgraph.prebuilt import chat_agent_executor

agent_executor = chat_agent_executor.create_tool_calling_executor(
    llm, tools, messages_modifier=system_message
)

API 参考：HumanMessage

考虑一下代理人如何回答以下问题：

for s in agent_executor.stream(
    {"messages": [HumanMessage(content="Which country's customers spent the most?")]}
):
    print(s)
    print("----")

{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_vnHKe3oul1xbpX0Vrb2vsamZ', 'function': {'arguments': '{"query":"SELECT c.Country, SUM(i.Total) AS Total_Spent FROM customers c JOIN invoices i ON c.CustomerId = i.CustomerId GROUP BY c.Country ORDER BY Total_Spent DESC LIMIT 1"}', 'name': 'sql_db_query'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 53, 'prompt_tokens': 557, 'total_tokens': 610}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-da250593-06b5-414c-a9d9-3fc77036dd9c-0', tool_calls=[{'name': 'sql_db_query', 'args': {'query': 'SELECT c.Country, SUM(i.Total) AS Total_Spent FROM customers c JOIN invoices i ON c.CustomerId = i.CustomerId GROUP BY c.Country ORDER BY Total_Spent DESC LIMIT 1'}, 'id': 'call_vnHKe3oul1xbpX0Vrb2vsamZ'}])]}}
----
{'action': {'messages': [ToolMessage(content='Error: (sqlite3.OperationalError) no such table: customers\n[SQL: SELECT c.Country, SUM(i.Total) AS Total_Spent FROM customers c JOIN invoices i ON c.CustomerId = i.CustomerId GROUP BY c.Country ORDER BY Total_Spent DESC LIMIT 1]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)', name='sql_db_query', id='1a5c85d4-1b30-4af3-ab9b-325cbce3b2b4', tool_call_id='call_vnHKe3oul1xbpX0Vrb2vsamZ')]}}
----
{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_pp3BBD1hwpdwskUj63G3tgaQ', 'function': {'arguments': '{}', 'name': 'sql_db_list_tables'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 12, 'prompt_tokens': 699, 'total_tokens': 711}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-04cf0e05-61d0-4673-b5dc-1a9b5fd71fff-0', tool_calls=[{'name': 'sql_db_list_tables', 'args': {}, 'id': 'call_pp3BBD1hwpdwskUj63G3tgaQ'}])]}}
----
{'action': {'messages': [ToolMessage(content='Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track', name='sql_db_list_tables', id='c2668450-4d73-4d32-8d75-8aac8fa153fd', tool_call_id='call_pp3BBD1hwpdwskUj63G3tgaQ')]}}
----
{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_22Asbqgdx26YyEvJxBuANVdY', 'function': {'arguments': '{"query":"SELECT c.Country, SUM(i.Total) AS Total_Spent FROM Customer c JOIN Invoice i ON c.CustomerId = i.CustomerId GROUP BY c.Country ORDER BY Total_Spent DESC LIMIT 1"}', 'name': 'sql_db_query'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 53, 'prompt_tokens': 744, 'total_tokens': 797}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-bdd94241-ca49-4f15-b31a-b7c728a34ea8-0', tool_calls=[{'name': 'sql_db_query', 'args': {'query': 'SELECT c.Country, SUM(i.Total) AS Total_Spent FROM Customer c JOIN Invoice i ON c.CustomerId = i.CustomerId GROUP BY c.Country ORDER BY Total_Spent DESC LIMIT 1'}, 'id': 'call_22Asbqgdx26YyEvJxBuANVdY'}])]}}
----
{'action': {'messages': [ToolMessage(content="[('USA', 523.0600000000003)]", name='sql_db_query', id='f647e606-8362-40ab-8d34-612ff166dbe1', tool_call_id='call_22Asbqgdx26YyEvJxBuANVdY')]}}
----
{'agent': {'messages': [AIMessage(content='Customers from the USA spent the most, with a total amount spent of $523.06.', response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 819, 'total_tokens': 839}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'stop', 'logprobs': None}, id='run-92e88de0-ff62-41da-8181-053fb5632af4-0')]}}
----

请注意，代理会执行多个查询，直到获得所需的信息：

列出可用的表；
检索三个表的模式；
通过连接操作查询多个表。

然后，代理能够使用最终查询的结果来生成原始问题的答案。

代理可以类似地处理定性问题：

for s in agent_executor.stream(
    {"messages": [HumanMessage(content="Describe the playlisttrack table")]}
):
    print(s)
    print("----")

{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_WN0N3mm8WFvPXYlK9P7KvIEr', 'function': {'arguments': '{"table_names":"playlisttrack"}', 'name': 'sql_db_schema'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 554, 'total_tokens': 571}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-be278326-4115-4c67-91a0-6dc97e7bffa4-0', tool_calls=[{'name': 'sql_db_schema', 'args': {'table_names': 'playlisttrack'}, 'id': 'call_WN0N3mm8WFvPXYlK9P7KvIEr'}])]}}
----
{'action': {'messages': [ToolMessage(content="Error: table_names {'playlisttrack'} not found in database", name='sql_db_schema', id='fe32b3d3-a40f-4802-a6b8-87a2453af8c2', tool_call_id='call_WN0N3mm8WFvPXYlK9P7KvIEr')]}}
----
{'agent': {'messages': [AIMessage(content='I apologize for the error. Let me first check the available tables in the database.', additional_kwargs={'tool_calls': [{'id': 'call_CzHt30847ql2MmnGxgYeVSL2', 'function': {'arguments': '{}', 'name': 'sql_db_list_tables'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 30, 'prompt_tokens': 592, 'total_tokens': 622}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-f6c107bb-e945-4848-a83c-f57daec1144e-0', tool_calls=[{'name': 'sql_db_list_tables', 'args': {}, 'id': 'call_CzHt30847ql2MmnGxgYeVSL2'}])]}}
----
{'action': {'messages': [ToolMessage(content='Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track', name='sql_db_list_tables', id='a4950f74-a0ad-4558-ba54-7bcf99539a02', tool_call_id='call_CzHt30847ql2MmnGxgYeVSL2')]}}
----
{'agent': {'messages': [AIMessage(content='The database contains a table named "PlaylistTrack". Let me retrieve the schema and sample rows from the "PlaylistTrack" table.', additional_kwargs={'tool_calls': [{'id': 'call_wX9IjHLgRBUmxlfCthprABRO', 'function': {'arguments': '{"table_names":"PlaylistTrack"}', 'name': 'sql_db_schema'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 44, 'prompt_tokens': 658, 'total_tokens': 702}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-e8d34372-1159-4654-a185-1e7d0cb70269-0', tool_calls=[{'name': 'sql_db_schema', 'args': {'table_names': 'PlaylistTrack'}, 'id': 'call_wX9IjHLgRBUmxlfCthprABRO'}])]}}
----
{'action': {'messages': [ToolMessage(content='\nCREATE TABLE "PlaylistTrack" (\n\t"PlaylistId" INTEGER NOT NULL, \n\t"TrackId" INTEGER NOT NULL, \n\tPRIMARY KEY ("PlaylistId", "TrackId"), \n\tFOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), \n\tFOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")\n)\n\n/*\n3 rows from PlaylistTrack table:\nPlaylistId\tTrackId\n1\t3402\n1\t3389\n1\t3390\n*/', name='sql_db_schema', id='f6ffc37a-188a-4690-b84e-c9f2c78b1e49', tool_call_id='call_wX9IjHLgRBUmxlfCthprABRO')]}}
----
{'agent': {'messages': [AIMessage(content='The "PlaylistTrack" table has the following schema:\n- PlaylistId: INTEGER (NOT NULL)\n- TrackId: INTEGER (NOT NULL)\n- Primary Key: (PlaylistId, TrackId)\n- Foreign Key: TrackId references Track(TrackId)\n- Foreign Key: PlaylistId references Playlist(PlaylistId)\n\nHere are 3 sample rows from the "PlaylistTrack" table:\n1. PlaylistId: 1, TrackId: 3402\n2. PlaylistId: 1, TrackId: 3389\n3. PlaylistId: 1, TrackId: 3390\n\nIf you have any specific questions or queries regarding the "PlaylistTrack" table, feel free to ask!', response_metadata={'token_usage': {'completion_tokens': 145, 'prompt_tokens': 818, 'total_tokens': 963}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'stop', 'logprobs': None}, id='run-961a4552-3cbd-4d28-b338-4d2f1ac40ea0-0')]}}
----

3、处理高基数列

为了过滤包含专有名词（例如地址、歌曲名称或艺术家）的列，我们首先需要仔细检查拼写，以便正确过滤数据。

我们可以通过创建一个向量存储来实现这一点，该向量存储包含数据库中存在的所有不同专有名词。然后，我们可以让代理在每次用户在问题中包含专有名词时查询该向量存储，以找到该单词的正确拼写。通过这种方式，代理可以在构建目标查询之前确保它了解用户指的是哪个实体。

首先，我们需要每个实体的唯一值，为此我们定义一个函数将结果解析为元素列表：

import ast
import re

def query_as_list(db, query):
    res = db.run(query)
    res = [el for sub in ast.literal_eval(res) for el in sub if el]
    res = [re.sub(r"\b\d+\b", "", string).strip() for string in res]
    return list(set(res))

artists = query_as_list(db, "SELECT Name FROM Artist")
albums = query_as_list(db, "SELECT Title FROM Album")
albums[:5]

['Big Ones',
 'Cidade Negra - Hits',
 'In Step',
 'Use Your Illusion I',
 'Voodoo Lounge']

利用这个函数，我们可以创建一个代理可以自行执行的检索工具。

from langchain.agents.agent_toolkits import create_retriever_tool
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

vector_db = FAISS.from_texts(artists + albums, OpenAIEmbeddings())
retriever = vector_db.as_retriever(search_kwargs={"k": 5})
description = """Use to look up values to filter on. Input is an approximate spelling of the proper noun, output is \
valid proper nouns. Use the noun most similar to the search."""
retriever_tool = create_retriever_tool(
    retriever,
    name="search_proper_nouns",
    description=description,
)

API 参考：create_retriever_tool | FAISS | OpenAIEmbeddings

让我们尝试一下：

print(retriever_tool.invoke("Alice Chains"))

Alice In Chains

Alanis Morissette

Pearl Jam

Pearl Jam

Audioslave

这样，如果代理确定需要根据“爱丽丝链”之类的艺术家编写过滤器，它可以首先使用检索工具来观察列的相关值。

综合起来：

system = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the given tools. Only use the information returned by the tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

You have access to the following tables: {table_names}

If you need to filter on a proper noun, you must ALWAYS first look up the filter value using the "search_proper_nouns" tool!
Do not try to guess at the proper name - use this function to find similar ones.""".format(
    table_names=db.get_usable_table_names()
)

system_message = SystemMessage(content=system)

agent = chat_agent_executor.create_tool_calling_executor(
    llm, tools, messages_modifier=system_message
)

for s in agent.stream(
    {"messages": [HumanMessage(content="How many albums does alis in chain have?")]}
):
    print(s)
    print("----")

{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_r5UlSwHKQcWDHx6LrttnqE56', 'function': {'arguments': '{"query":"SELECT COUNT(*) AS album_count FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = \'Alice In Chains\')"}', 'name': 'sql_db_query'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 40, 'prompt_tokens': 612, 'total_tokens': 652}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-548353fd-b06c-45bf-beab-46f81eb434df-0', tool_calls=[{'name': 'sql_db_query', 'args': {'query': "SELECT COUNT(*) AS album_count FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alice In Chains')"}, 'id': 'call_r5UlSwHKQcWDHx6LrttnqE56'}])]}}
----
{'action': {'messages': [ToolMessage(content='[(1,)]', name='sql_db_query', id='093058a9-f013-4be1-8e7a-ed839b0c90cd', tool_call_id='call_r5UlSwHKQcWDHx6LrttnqE56')]}}
----
{'agent': {'messages': [AIMessage(content='Alice In Chains has 11 albums.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 665, 'total_tokens': 674}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'stop', 'logprobs': None}, id='run-f804eaab-9812-4fb3-ae8b-280af8594ac6-0')]}}
----