构建基于 SQL 数据的问答系统
使大型语言模型系统能够查询结构化数据，与查询非结构化文本数据有着本质的区别。在非结构化文本场景中，常见做法是生成可被向量数据库搜索的文本；而对于结构化数据，通常的做法是让大型语言模型编写并执行特定领域语言（DSL）中的查询语句，例如 SQL。在本指南中，我们将介绍在数据库表格数据上构建问答系统的基本方法。我们将涵盖使用链式（chains）和智能体（agents）两种实现方式。这些系统将允许我们向数据库中的数据提出问题，并获得自然语言形式的回答。两者的主要区别在于：智能体可以按需多次循环查询数据库，直至回答问题。
⚠️ 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。
架构
在高层级上，这些系统的步骤是：
- 将问题转换为 SQL 查询: 模型将用户输入转换为 SQL 查询。
- 执行 SQL 查询: 执行该查询。
- 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。有关基于 CSV 数据进行问答的更多详细信息，请参阅我们的操操作指南。
设置
首先，获取所需的软件包并设置环境变量：
%%capture --no-stderr
%pip install --upgrade --quiet langchain-community langgraph
# Comment out the below to opt-out of using LangSmith in this notebook. Not required.
if not os.environ.get("LANGSMITH_API_KEY"):
os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
os.environ["LANGSMITH_TRACING"] = "true"
示例数据
下面的示例将使用 SQLite 连接和 Chinook 数据库，这是一个代表数字媒体商店的示例数据库。请遵循这些安装步骤来在与此笔记本相同的目录中创建Chinook.db。您也可以通过命令行下载并构建该数据库：
curl -s https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql | sqlite3 Chinook.db
现在，Chinook.db 已存在于我们的目录中，我们可以使用 SQLAlchemy 驱动的 SQLDatabase 类与其进行交互：
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:SQL数据库
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')]"
太好了！我们有了一个可以查询的 SQL 数据库。现在让我们尝试将其连接到 LLM。
链式操作
链是可预测步骤的组合。在 LangGraph 中，我们可以通过简单的节点序列来表示一个链。让我们创建一个步骤序列，给定一个问题后执行以下操作：
- 将问题转换为 SQL 查询；
- 执行查询；
- 使用结果来回答原始问题。
此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——即使是“hello”。重要的是，正如我们稍后将看到的，有些问题需要多个查询才能回答。我们将在智能体（Agents）部分解决这些场景。
应用程序状态
我们的应用程序的 LangGraph 状态 控制输入到应用程序的数据、在步骤之间传输的数据以及由应用程序输出的数据。它通常是一个 TypedDict，但也可以是一个 Pydantic BaseModel。
对于此应用程序，我们只需跟踪输入问题、生成的查询、查询结果和生成的答案：
from typing_extensions import TypedDict
class State(TypedDict):
question: str
query: str
result: str
answer: str
现在我们只需要操作此状态并填充其内容的函数。
将问题转换为 SQL 查询
第一步是获取用户输入并将其转换为 SQL 查询。为了可靠地获取 SQL 查询（不包含 markdown 格式、解释或说明），我们将利用 LangChain 的 结构化输出 抽象功能。
让我们为我们的应用选择一个聊天模型：
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")
让我们为我们的模型提供一些说明：
from langchain_core.prompts import ChatPromptTemplate
system_message = """
Given an input question, create a syntactically correct {dialect} query to
run to help find the answer. Unless the user specifies in his question 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 a the
few relevant columns given the question.
Pay attention to use only the column names that you can see in the schema
description. Be careful to not query for columns that do not exist. Also,
pay attention to which column is in which table.
Only use the following tables:
{table_info}
"""
user_prompt = "Question: {input}"
query_prompt_template = ChatPromptTemplate(
[("system", system_message), ("user", user_prompt)]
)
for message in query_prompt_template.messages:
message.pretty_print()
API 参考:ChatPromptTemplate
================================[1m System Message [0m================================
Given an input question, create a syntactically correct [33;1m[1;3m{dialect}[0m query to
run to help find the answer. Unless the user specifies in his question a
specific number of examples they wish to obtain, always limit your query to
at most [33;1m[1;3m{top_k}[0m 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 a the
few relevant columns given the question.
Pay attention to use only the column names that you can see in the schema
description. Be careful to not query for columns that do not exist. Also,
pay attention to which column is in which table.
Only use the following tables:
[33;1m[1;3m{table_info}[0m
================================[1m Human Message [0m=================================
Question: [33;1m[1;3m{input}[0m
该提示包含我们需要填充的多个参数，例如 SQL 方言和表架构。LangChain 的 SQLDatabase 对象包含有助于完成此操作的方法。我们的 write_query 步骤将仅用于填充这些参数并提示模型生成 SQL 查询：
from typing_extensions import Annotated
class QueryOutput(TypedDict):
"""Generated SQL query."""
query: Annotated[str, ..., "Syntactically valid SQL query."]
def write_query(state: State):
"""Generate SQL query to fetch information."""
prompt = query_prompt_template.invoke(
{
"dialect": db.dialect,
"top_k": 10,
"table_info": db.get_table_info(),
"input": state["question"],
}
)
structured_llm = llm.with_structured_output(QueryOutput)
result = structured_llm.invoke(prompt)
return {"query": result["query"]}
让我们测试一下：
write_query({"question": "How many Employees are there?"})
{'query': 'SELECT COUNT(*) as employee_count FROM Employee;'}
执行查询
这是创建 SQL 链时最危险的部分。请仔细考虑是否可以在您的数据上运行自动化查询。尽可能最小化数据库连接权限。考虑在查询执行前向您的链中添加人工审批步骤（见下文）。
要执行查询，我们将从 langchain-community 加载一个工具。我们的 execute_query 节点将仅包装此工具：
from langchain_community.tools.sql_database.tool import QuerySQLDatabaseTool
def execute_query(state: State):
"""Execute SQL query."""
execute_query_tool = QuerySQLDatabaseTool(db=db)
return {"result": execute_query_tool.invoke(state["query"])}
API 参考:查询SQL数据库工具
测试此步骤：
execute_query({"query": "SELECT COUNT(EmployeeId) AS EmployeeCount FROM Employee;"})
{'result': '[(8,)]'}
生成答案
最后，我们的最后一步根据从数据库中检索到的信息生成问题的答案：
def generate_answer(state: State):
"""Answer question using retrieved information as context."""
prompt = (
"Given the following user question, corresponding SQL query, "
"and SQL result, answer the user question.\n\n"
f'Question: {state["question"]}\n'
f'SQL Query: {state["query"]}\n'
f'SQL Result: {state["result"]}'
)
response = llm.invoke(prompt)
return {"answer": response.content}
使用 LangGraph 进行编排
最后，我们将应用程序编译为一个单一的 graph 对象。在这种情况下，我们只是将三个步骤连接成一个单一序列。
from langgraph.graph import START, StateGraph
graph_builder = StateGraph(State).add_sequence(
[write_query, execute_query, generate_answer]
)
graph_builder.add_edge(START, "write_query")
graph = graph_builder.compile()
API 参考:状态图
LangGraph 还内置了用于可视化应用程序控制流的实用工具：
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
让我们测试我们的应用程序！请注意，我们可以流式传输各个步骤的结果：
for step in graph.stream(
{"question": "How many employees are there?"}, stream_mode="updates"
):
print(step)
{'write_query': {'query': 'SELECT COUNT(*) as employee_count FROM Employee;'}}
{'execute_query': {'result': '[(8,)]'}}
{'generate_answer': {'answer': 'There are 8 employees in total.'}}
查看 LangSmith 追踪。
Human-in-the-loop
LangGraph 支持许多可用于此工作流的特性。其中之一是 人在回路（human-in-the-loop）：我们可以在执行敏感步骤（如运行 SQL 查询）之前中断应用程序，以便进行人工审查。这一功能由 LangGraph 的 持久化层（persistence layer） 实现，该层将运行进度保存到您选择的存储中。下面，我们将存储指定为内存存储：
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
graph = graph_builder.compile(checkpointer=memory, interrupt_before=["execute_query"])
# Now that we're using persistence, we need to specify a thread ID
# so that we can continue the run after review.
config = {"configurable": {"thread_id": "1"}}
API 参考:内存保存器
display(Image(graph.get_graph().draw_mermaid_png()))
让我们重复相同的运行，添加一个简单的“是/否”审批步骤：
for step in graph.stream(
{"question": "How many employees are there?"},
config,
stream_mode="updates",
):
print(step)
try:
user_approval = input("Do you want to go to execute query? (yes/no): ")
except Exception:
user_approval = "no"
if user_approval.lower() == "yes":
# If approved, continue the graph execution
for step in graph.stream(None, config, stream_mode="updates"):
print(step)
else:
print("Operation cancelled by user.")
{'write_query': {'query': 'SELECT COUNT(EmployeeId) AS EmployeeCount FROM Employee;'}}
{'__interrupt__': ()}
``````output
Do you want to go to execute query? (yes/no): yes
``````output
{'execute_query': {'result': '[(8,)]'}}
{'generate_answer': {'answer': 'There are 8 employees.'}}
有关更多详细信息和示例，请参阅 此 LangGraph 指南。
下一步
对于更复杂的查询生成，我们可能需要创建少样本提示（few-shot prompts）或添加查询检查步骤。如需了解此类高级技巧及其他内容，请查看：
代理
智能体 利用大语言模型的推理能力在执行过程中做出决策。使用智能体允许你将查询生成和执行过程中的额外裁量权外包出去。尽管其行为不如上述“链”可预测，但它们具有一些优势：
- 他们可以根据需要多次查询数据库以回答用户的问题。
- 它们可以通过运行生成的查询、捕获回溯并正确重新生成来从错误中恢复。
- 他们可以根据数据库的架构以及数据库的内容（例如描述特定的表）来回答问题。
下面我们将组装一个最小化的 SQL 智能体。我们将使用 LangChain 的 SQLDatabaseToolkit 为其配备一组工具。利用 LangGraph 的 预构建的 ReAct 智能体构造函数，我们可以仅用一行代码完成此操作。
提示
查看 LangGraph 的 SQL Agent 教程，以了解更高级的 SQL Agent 实现方案。
The SQLDatabaseToolkit 包含以下工具：
- 创建并执行查询
- 检查查询语法
- 检索表描述
- ... and more
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 0x10d5f9120>),
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 0x10d5f9120>),
ListSQLDatabaseTool(db=<langchain_community.utilities.sql_database.SQLDatabase object at 0x10d5f9120>),
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 0x10d5f9120>, llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x119315480>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x119317550>, root_client=<openai.OpenAI object at 0x10d5f8df0>, root_async_client=<openai.AsyncOpenAI object at 0x1193154e0>, model_name='gpt-4o', 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 0x119315480>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x119317550>, root_client=<openai.OpenAI object at 0x10d5f8df0>, root_async_client=<openai.AsyncOpenAI object at 0x1193154e0>, model_name='gpt-4o', temperature=0.0, model_kwargs={}, openai_api_key=SecretStr('**********')), output_parser=StrOutputParser(), llm_kwargs={}))]
系统提示
我们还将需要为我们的代理加载一个系统提示。这将包含关于如何行为的具体指令。请注意，下面的提示包含多个参数，我们在下面进行分配。
system_message = """
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="SQLite",
top_k=5,
)
正在初始化智能体
我们将使用预构建的 LangGraph 代理来构建我们的代理
from langchain_core.messages import HumanMessage
from langgraph.prebuilt import create_react_agent
agent_executor = create_react_agent(llm, tools, prompt=system_message)
API 参考:人类消息 |create_react_agent
考虑代理如何回答以下问题：
question = "Which country's customers spent the most?"
for step in agent_executor.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["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_tFp7HYD6sAAmCShgeqkVZH6Q)
Call ID: call_tFp7HYD6sAAmCShgeqkVZH6Q
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_KJZ1Jx6JazyDdJa0uH1UeiOz)
Call ID: call_KJZ1Jx6JazyDdJa0uH1UeiOz
Args:
table_names: Customer, Invoice
=================================[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
*/
==================================[1m Ai Message [0m==================================
Tool Calls:
sql_db_query_checker (call_AQuTGbgH63u4gPgyV723yrjX)
Call ID: call_AQuTGbgH63u4gPgyV723yrjX
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_checker
\`\`\`sql
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 Ai Message [0m==================================
Tool Calls:
sql_db_query (call_B88EwU44nwwpQL5M9nlcemSU)
Call ID: call_B88EwU44nwwpQL5M9nlcemSU
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==================================
The country whose customers spent the most is the USA, with a total spending of 523.06.
您也可以使用 LangSmith 追踪 来可视化这些步骤及相关元数据。
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；
- 检索三个表的架构；
- 通过连接操作查询多个表。
代理随后能够利用最终查询的结果来生成对原始问题的回答。
代理同样可以处理定性问题：
question = "Describe the playlisttrack table"
for step in agent_executor.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================
Describe the playlisttrack table
==================================[1m Ai Message [0m==================================
Tool Calls:
sql_db_list_tables (call_fMF8eTmX5TJDJjc3Mhdg52TI)
Call ID: call_fMF8eTmX5TJDJjc3Mhdg52TI
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_W8Vkk4NEodkAAIg8nexAszUH)
Call ID: call_W8Vkk4NEodkAAIg8nexAszUH
Args:
table_names: PlaylistTrack
=================================[1m Tool Message [0m=================================
Name: sql_db_schema
CREATE TABLE "PlaylistTrack" (
"PlaylistId" INTEGER NOT NULL,
"TrackId" INTEGER NOT NULL,
PRIMARY KEY ("PlaylistId", "TrackId"),
FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")
)
/*
3 rows from PlaylistTrack table:
PlaylistId TrackId
1 3402
1 3389
1 3390
*/
==================================[1m Ai Message [0m==================================
The `PlaylistTrack` table is designed to associate tracks with playlists. It has the following structure:
- **PlaylistId**: An integer that serves as a foreign key referencing the `Playlist` table. It is part of the composite primary key.
- **TrackId**: An integer that serves as a foreign key referencing the `Track` table. It is also part of the composite primary key.
The primary key for this table is a composite key consisting of both `PlaylistId` and `TrackId`, ensuring that each track can be uniquely associated with a playlist. The table enforces referential integrity by linking to the `Track` and `Playlist` tables through foreign keys.
处理高基数列
为了筛选包含专有名词（如地址、歌曲名称或艺术家）的列，我们首先需要对拼写进行双重检查，以便正确过滤数据。
我们可以通过创建一个包含数据库中所有不同专有名词的向量存储来实现这一点。然后，当用户在问题中包含专有名词时，代理会查询该向量存储，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理解用户指的是哪个实体。
首先，我们需要为每个目标实体获取唯一值，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]
['In Through The Out Door',
'Transmission',
'Battlestar Galactica (Classic), Season',
'A Copland Celebration, Vol. I',
'Quiet Songs']
使用此函数，我们可以创建一个检索工具，代理可以根据需要执行它。
选择一个嵌入模型:
选择 嵌入模型：
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_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
选择一个向量存储:
选择 向量存储:
pip install -qU langchain-core
from langchain_core.vectorstores import InMemoryVectorStore
vector_store = InMemoryVectorStore(embeddings)
我们现在可以构建一个检索工具，用于在数据库中搜索相关专有名词：
from langchain.agents.agent_toolkits import create_retriever_tool
_ = vector_store.add_texts(artists + albums)
retriever = vector_store.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
让我们试一试：
print(retriever_tool.invoke("Alice Chains"))
Alice In Chains
Alanis Morissette
Pearl Jam
Pearl Jam
Audioslave
这样，如果代理确定需要基于类似"Alice Chains"的艺术家编写过滤器，它可以首先使用检索器工具来观察列的相关值。
将其整合：
# Add to system message
suffix = (
"If you need to filter on a proper noun like a Name, 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."
)
system = f"{system_message}\n\n{suffix}"
tools.append(retriever_tool)
agent = create_react_agent(llm, tools, prompt=system)
question = "How many albums does alis in chain have?"
for step in agent.stream(
{"messages": [{"role": "user", "content": question}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================
How many albums does alis in chain have?
==================================[1m Ai Message [0m==================================
Tool Calls:
search_proper_nouns (call_8ryjsRPLAr79mM3Qvnq6gTOH)
Call ID: call_8ryjsRPLAr79mM3Qvnq6gTOH
Args:
query: alis in chain
=================================[1m Tool Message [0m=================================
Name: search_proper_nouns
Alice In Chains
Aisha Duo
Xis
Da Lama Ao Caos
A-Sides
==================================[1m Ai Message [0m==================================
Tool Calls:
sql_db_list_tables (call_NJjtCpU89MBMplssjn1z0xzq)
Call ID: call_NJjtCpU89MBMplssjn1z0xzq
Args:
search_proper_nouns (call_1BfrueC9koSIyi4OfMu2Ao8q)
Call ID: call_1BfrueC9koSIyi4OfMu2Ao8q
Args:
query: Alice In Chains
=================================[1m Tool Message [0m=================================
Name: search_proper_nouns
Alice In Chains
Pearl Jam
Pearl Jam
Foo Fighters
Soundgarden
==================================[1m Ai Message [0m==================================
Tool Calls:
sql_db_schema (call_Kn09w9jd9swcNzIZ1b5MlKID)
Call ID: call_Kn09w9jd9swcNzIZ1b5MlKID
Args:
table_names: Album, Artist
=================================[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
*/
==================================[1m Ai Message [0m==================================
Tool Calls:
sql_db_query (call_WkHRiPcBoGN9bc58MIupRHKP)
Call ID: call_WkHRiPcBoGN9bc58MIupRHKP
Args:
query: SELECT COUNT(*) FROM Album WHERE ArtistId = (SELECT ArtistId FROM Artist WHERE Name = 'Alice In Chains')
=================================[1m Tool Message [0m=================================
Name: sql_db_query
[(1,)]
==================================[1m Ai Message [0m==================================
Alice In Chains has released 1 album in the database.
正如我们所见，在流式步骤和 LangSmith 追踪 中，代理使用了 search_proper_nouns 工具来检查如何正确查询数据库以获取这位特定艺术家的信息。