构建一个 SQL 代理¶
在本教程中，我们将逐步学习如何构建一个能够回答有关 SQL 数据库问题的代理。
总体而言，该代理将执行以下步骤：
- 从数据库中获取可用的表
- 确定哪些表与问题相关
- 获取相关表的模式
- 根据问题和模式信息生成查询
- 使用 LLM 对查询进行检查，以发现常见错误
- 执行查询并返回结果
- 修正由数据库引擎提示出的问题，直到查询成功
- 根据结果制定响应内容
安全提示
构建 SQL 数据库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代理所需范围内。这将减轻但不能完全消除构建模型驱动系统所带来的风险。
1. 设置¶
首先，我们安装一些依赖项。本教程使用了 langchain-community 中的 SQL 数据库和工具抽象。我们还需要一个 LangChain 聊天模型。
为 LangGraph 开发设置 LangSmith
注册 LangSmith 可以快速发现并提升你的 LangGraph 项目性能。LangSmith 允许你使用追踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用 —— 有关如何入门的更多信息，请阅读 此处。
选择一个 LLM¶
首先我们初始化我们的 LLM。任何支持工具调用的模型都可以使用。我们在下面使用 OpenAI。
API Reference: init_chat_model
配置数据库¶
在本教程中，我们将创建一个 SQLite 数据库。SQLite 是一个轻量级的数据库，易于设置和使用。我们将加载 chinook 数据库，这是一个代表数字媒体商店的示例数据库。
有关该数据库的更多信息，请访问 此处。
为了方便起见，我们已将数据库（Chinook.db）托管在一个公共的 GCS 存储桶中。
import requests
url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db"
response = requests.get(url)
if response.status_code == 200:
# Open a local file in binary write mode
with open("Chinook.db", "wb") as file:
# Write the content of the response (the file) to the local file
file.write(response.content)
print("File downloaded and saved as Chinook.db")
else:
print(f"Failed to download the file. Status code: {response.status_code}")
我们将使用 langchain_community 包中提供的一个便捷的 SQL 数据库封装器来与数据库进行交互。该封装器提供了一个简单的接口来执行 SQL 查询并获取结果：
API Reference: SQLDatabase
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
print(f"Dialect: {db.dialect}")
print(f"Available tables: {db.get_usable_table_names()}")
print(f'Sample output: {db.run("SELECT * FROM Artist LIMIT 5;")}')
Dialect: sqlite
Available tables: ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']
Sample output: [(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains')]
用于数据库交互的工具¶
langchain-community 实现了一些内置工具，用于与我们的 SQLDatabase 进行交互，包括列出表、读取表结构以及检查和运行查询的工具：
API Reference: SQLDatabaseToolkit
from langchain_community.agent_toolkits import SQLDatabaseToolkit
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
tools = toolkit.get_tools()
for tool in tools:
print(f"{tool.name}: {tool.description}\n")
sql_db_query: 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.
sql_db_schema: 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
sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database.
sql_db_query_checker: 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!
2. 使用预构建的代理¶
借助这些工具，我们可以在一行代码中初始化一个预构建的代理。为了自定义代理的行为，我们编写一个描述性的系统提示。
API Reference: create_react_agent
from langgraph.prebuilt import create_react_agent
system_prompt = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} 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 {top_k} 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 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.
""".format(
dialect=db.dialect,
top_k=5,
)
agent = create_react_agent(
llm,
tools,
prompt=system_prompt,
)
Note
该系统提示包含一系列指令，例如在运行某些工具之前或之后始终运行特定工具。在下一节中，我们将通过图的结构来强制执行这些行为，这将为我们提供更大的控制权，并使我们能够简化提示。
让我们在示例查询上运行这个代理，并观察其行为：
question = "Which sales agent made the most in sales in 2009?"
for step in agent.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
================================ Human Message =================================
Which sales agent made the most in sales in 2009?
================================== Ai Message ==================================
Tool Calls:
sql_db_list_tables (call_0MfnhUYmRNVe03m6fYClaoa0)
Call ID: call_0MfnhUYmRNVe03m6fYClaoa0
Args:
tool_input:
================================= 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_XwI6uES0HeZ2xCRxkkdWzB3I)
Call ID: call_XwI6uES0HeZ2xCRxkkdWzB3I
Args:
table_names: Employee, Invoice, InvoiceLine
================================= Tool Message =================================
Name: sql_db_schema
CREATE TABLE "Employee" (
"EmployeeId" INTEGER NOT NULL,
"LastName" NVARCHAR(20) NOT NULL,
"FirstName" NVARCHAR(20) NOT NULL,
"Title" NVARCHAR(30),
"ReportsTo" INTEGER,
"BirthDate" DATETIME,
"HireDate" DATETIME,
"Address" NVARCHAR(70),
"City" NVARCHAR(40),
"State" NVARCHAR(40),
"Country" NVARCHAR(40),
"PostalCode" NVARCHAR(10),
"Phone" NVARCHAR(24),
"Fax" NVARCHAR(24),
"Email" NVARCHAR(60),
PRIMARY KEY ("EmployeeId"),
FOREIGN KEY("ReportsTo") REFERENCES "Employee" ("EmployeeId")
)
/*
3 rows from Employee table:
EmployeeId LastName FirstName Title ReportsTo BirthDate HireDate Address City State Country PostalCode Phone Fax Email
1 Adams Andrew General Manager None 1962-02-18 00:00:00 2002-08-14 00:00:00 11120 Jasper Ave NW Edmonton AB Canada T5K 2N1 +1 (780) 428-9482 +1 (780) 428-3457 andrew@chinookcorp.com
2 Edwards Nancy Sales Manager 1 1958-12-08 00:00:00 2002-05-01 00:00:00 825 8 Ave SW Calgary AB Canada T2P 2T3 +1 (403) 262-3443 +1 (403) 262-3322 nancy@chinookcorp.com
3 Peacock Jane Sales Support Agent 2 1973-08-29 00:00:00 2002-04-01 00:00:00 1111 6 Ave SW Calgary AB Canada T2P 5M5 +1 (403) 262-3443 +1 (403) 262-6712 jane@chinookcorp.com
*/
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 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98
2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96
3 8 2009-01-03 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_schema (call_dHE5FKSycFhYIkdPKtpEk9rM)
Call ID: call_dHE5FKSycFhYIkdPKtpEk9rM
Args:
table_names: Customer
================================= 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
*/
================================== Ai Message ==================================
Tool Calls:
sql_db_query_checker (call_QPtWY5cUZWgWhr13Kz6eBJkU)
Call ID: call_QPtWY5cUZWgWhr13Kz6eBJkU
Args:
query: SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'
GROUP BY e.EmployeeId, e.FirstName, e.LastName
ORDER BY TotalSales DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query_checker
\`\`\`sql
SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'
GROUP BY e.EmployeeId, e.FirstName, e.LastName
ORDER BY TotalSales DESC
LIMIT 1;
\`\`\`
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_X747YMElSeY5D3CfhE6KzLUj)
Call ID: call_X747YMElSeY5D3CfhE6KzLUj
Args:
query: SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'
GROUP BY e.EmployeeId, e.FirstName, e.LastName
ORDER BY TotalSales DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query
[(5, 'Steve', 'Johnson', 164.34)]
================================== Ai Message ==================================
The sales agent who made the most in sales in 2009 was Steve Johnson, with total sales of 164.34.
这效果已经相当不错了：代理正确地列出了表，获取了模式，编写了一个查询，检查了该查询，并运行它以提供最终响应。
Tip
你可以在 LangSmith trace 中检查上述运行的所有方面，包括采取的步骤、调用的工具、LLM看到的提示等。
3. 自定义代理¶
预构建的代理使我们能够快速入门，但在每一步中，代理都可以访问完整的工具集。上面，我们依赖系统提示来限制其行为——例如，我们指示代理始终从 "list tables" 工具开始，并在执行查询之前始终运行查询检查工具。
我们可以通过自定义代理在 LangGraph 中实现更高程度的控制。下面，我们将实现一个简单的 ReAct 代理设置，为特定的工具调用设置专用节点。我们将使用与预构建代理相同的 状态。
我们为以下步骤构建专用节点：
- 列出数据库表
- 调用 "get schema" 工具
- 生成查询
- 检查查询
将这些步骤放在专用节点中，使我们能够（1）在需要时强制调用工具，以及（2）自定义与每个步骤相关的提示。
API Reference: AIMessage | RunnableConfig | END | START | StateGraph | ToolNode
from typing import Literal
from langchain_core.messages import AIMessage
from langchain_core.runnables import RunnableConfig
from langgraph.graph import END, START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode
get_schema_tool = next(tool for tool in tools if tool.name == "sql_db_schema")
get_schema_node = ToolNode([get_schema_tool], name="get_schema")
run_query_tool = next(tool for tool in tools if tool.name == "sql_db_query")
run_query_node = ToolNode([run_query_tool], name="run_query")
# Example: create a predetermined tool call
def list_tables(state: MessagesState):
tool_call = {
"name": "sql_db_list_tables",
"args": {},
"id": "abc123",
"type": "tool_call",
}
tool_call_message = AIMessage(content="", tool_calls=[tool_call])
list_tables_tool = next(tool for tool in tools if tool.name == "sql_db_list_tables")
tool_message = list_tables_tool.invoke(tool_call)
response = AIMessage(f"Available tables: {tool_message.content}")
return {"messages": [tool_call_message, tool_message, response]}
# Example: force a model to create a tool call
def call_get_schema(state: MessagesState):
# Note that LangChain enforces that all models accept `tool_choice="any"`
# as well as `tool_choice=<string name of tool>`.
llm_with_tools = llm.bind_tools([get_schema_tool], tool_choice="any")
response = llm_with_tools.invoke(state["messages"])
return {"messages": [response]}
generate_query_system_prompt = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} 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 {top_k} 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.
DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
""".format(
dialect=db.dialect,
top_k=5,
)
def generate_query(state: MessagesState):
system_message = {
"role": "system",
"content": generate_query_system_prompt,
}
# We do not force a tool call here, to allow the model to
# respond naturally when it obtains the solution.
llm_with_tools = llm.bind_tools([run_query_tool])
response = llm_with_tools.invoke([system_message] + state["messages"])
return {"messages": [response]}
check_query_system_prompt = """
You are a SQL expert with a strong attention to detail.
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
If there are any of the above mistakes, rewrite the query. If there are no mistakes,
just reproduce the original query.
You will call the appropriate tool to execute the query after running this check.
""".format(dialect=db.dialect)
def check_query(state: MessagesState):
system_message = {
"role": "system",
"content": check_query_system_prompt,
}
# Generate an artificial user message to check
tool_call = state["messages"][-1].tool_calls[0]
user_message = {"role": "user", "content": tool_call["args"]["query"]}
llm_with_tools = llm.bind_tools([run_query_tool], tool_choice="any")
response = llm_with_tools.invoke([system_message, user_message])
response.id = state["messages"][-1].id
return {"messages": [response]}
最后，我们使用 Graph API 将这些步骤组合成一个工作流。我们在查询生成步骤中定义了一个条件边，如果生成了查询，则路由到查询检查器；如果没有工具调用存在（即 LLM 已经对查询提供了响应），则结束。
def should_continue(state: MessagesState) -> Literal[END, "check_query"]:
messages = state["messages"]
last_message = messages[-1]
if not last_message.tool_calls:
return END
else:
return "check_query"
builder = StateGraph(MessagesState)
builder.add_node(list_tables)
builder.add_node(call_get_schema)
builder.add_node(get_schema_node, "get_schema")
builder.add_node(generate_query)
builder.add_node(check_query)
builder.add_node(run_query_node, "run_query")
builder.add_edge(START, "list_tables")
builder.add_edge("list_tables", "call_get_schema")
builder.add_edge("call_get_schema", "get_schema")
builder.add_edge("get_schema", "generate_query")
builder.add_conditional_edges(
"generate_query",
should_continue,
)
builder.add_edge("check_query", "run_query")
builder.add_edge("run_query", "generate_query")
agent = builder.compile()
我们下面对应用程序进行可视化展示：
API Reference: CurveStyle | MermaidDrawMethod | NodeStyles
from IPython.display import Image, display
from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles
display(Image(agent.get_graph().draw_mermaid_png()))
我们现在可以像之前一样调用图：
question = "Which sales agent made the most in sales in 2009?"
for step in agent.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
================================ Human Message =================================
Which sales agent made the most in sales in 2009?
================================== Ai Message ==================================
Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:
sql_db_schema (call_rMs3sF1HVcAGggGf89ZNvtLE)
Call ID: call_rMs3sF1HVcAGggGf89ZNvtLE
Args:
table_names: Employee,Invoice,Customer
================================= 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 "Employee" (
"EmployeeId" INTEGER NOT NULL,
"LastName" NVARCHAR(20) NOT NULL,
"FirstName" NVARCHAR(20) NOT NULL,
"Title" NVARCHAR(30),
"ReportsTo" INTEGER,
"BirthDate" DATETIME,
"HireDate" DATETIME,
"Address" NVARCHAR(70),
"City" NVARCHAR(40),
"State" NVARCHAR(40),
"Country" NVARCHAR(40),
"PostalCode" NVARCHAR(10),
"Phone" NVARCHAR(24),
"Fax" NVARCHAR(24),
"Email" NVARCHAR(60),
PRIMARY KEY ("EmployeeId"),
FOREIGN KEY("ReportsTo") REFERENCES "Employee" ("EmployeeId")
)
/*
3 rows from Employee table:
EmployeeId LastName FirstName Title ReportsTo BirthDate HireDate Address City State Country PostalCode Phone Fax Email
1 Adams Andrew General Manager None 1962-02-18 00:00:00 2002-08-14 00:00:00 11120 Jasper Ave NW Edmonton AB Canada T5K 2N1 +1 (780) 428-9482 +1 (780) 428-3457 andrew@chinookcorp.com
2 Edwards Nancy Sales Manager 1 1958-12-08 00:00:00 2002-05-01 00:00:00 825 8 Ave SW Calgary AB Canada T2P 2T3 +1 (403) 262-3443 +1 (403) 262-3322 nancy@chinookcorp.com
3 Peacock Jane Sales Support Agent 2 1973-08-29 00:00:00 2002-04-01 00:00:00 1111 6 Ave SW Calgary AB Canada T2P 5M5 +1 (403) 262-3443 +1 (403) 262-6712 jane@chinookcorp.com
*/
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 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98
2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96
3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94
*/
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_TQyoJSV78xEWHqc69PiYHjdS)
Call ID: call_TQyoJSV78xEWHqc69PiYHjdS
Args:
query: SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE strftime('%Y', i.InvoiceDate) = '2009'
GROUP BY e.EmployeeId
ORDER BY TotalSales DESC
LIMIT 1;
================================== Ai Message ==================================
Tool Calls:
sql_db_query (call_tSQOuxc7iQ1jvLzXSofCqK4V)
Call ID: call_tSQOuxc7iQ1jvLzXSofCqK4V
Args:
query: SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE strftime('%Y', i.InvoiceDate) = '2009'
GROUP BY e.EmployeeId
ORDER BY TotalSales DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query
[('Steve', 'Johnson', 164.34)]
================================== Ai Message ==================================
The sales agent who made the most in sales in 2009 was Steve Johnson, with total sales of 164.34.
Tip
请参见 LangSmith trace 以查看上述运行。
下一步¶
查看此指南，了解如何使用 LangSmith 评估 LangGraph 应用程序，包括此类 SQL 代理。