> ## Documentation Index > Fetch the complete documentation index at: https://cndoc-langchain.site/llms.txt > Use this file to discover all available pages before exploring further. # 构建 SQL 代理 ## 概述 在本教程中，你将学习如何构建一个能够使用 LangChain [代理](/oss/python/langchain/agents) 回答关于 SQL 数据库问题的代理。 从高层次来看，该代理将： 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。请确保你的数据库连接权限始终尽可能地限定在代理所需的最小范围内。这将减轻（但不能消除）构建模型驱动系统的风险。 ### 概念 我们将涵盖以下概念： * 用于从 SQL 数据库读取的 [工具](/oss/python/langchain/tools) * LangChain [代理](/oss/python/langchain/agents) * [Human in the Loop](/oss/python/langchain/human-in-the-loop) 流程 ## 设置 ### 安装 ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain langgraph langchain-community ``` ### LangSmith 设置 [LangSmith](https://smith.langchain.com) 以检查你的链或代理内部发生的情况。然后设置以下环境变量： ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} export LANGSMITH_TRACING="true" export LANGSMITH_API_KEY="..." ``` ## 1. 选择一个 LLM 选择一个支持 [工具调用](/oss/python/integrations/providers/overview) 的模型： 👉 阅读 [OpenAI 聊天模型集成文档](/oss/python/integrations/chat/openai/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[openai]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["OPENAI_API_KEY"] = "sk-..." model = init_chat_model("gpt-5.4") ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_openai import ChatOpenAI os.environ["OPENAI_API_KEY"] = "sk-..." model = ChatOpenAI(model="gpt-5.4") ``` 👉 阅读 [Anthropic 聊天模型集成文档](/oss/python/integrations/chat/anthropic/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[anthropic]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["ANTHROPIC_API_KEY"] = "sk-..." model = init_chat_model("claude-sonnet-4-6") ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_anthropic import ChatAnthropic os.environ["ANTHROPIC_API_KEY"] = "sk-..." model = ChatAnthropic(model="claude-sonnet-4-6") ``` 👉 阅读 [Azure 聊天模型集成文档](/oss/python/integrations/chat/azure_chat_openai/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[openai]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["AZURE_OPENAI_API_KEY"] = "..." os.environ["AZURE_OPENAI_ENDPOINT"] = "..." os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview" model = init_chat_model( "azure_openai:gpt-5.4", azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_openai import AzureChatOpenAI os.environ["AZURE_OPENAI_API_KEY"] = "..." os.environ["AZURE_OPENAI_ENDPOINT"] = "..." os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview" model = AzureChatOpenAI( model="gpt-5.4", azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"] ) ``` 👉 阅读 [Google GenAI 聊天模型集成文档](/oss/python/integrations/chat/google_generative_ai/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[google-genai]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["GOOGLE_API_KEY"] = "..." model = init_chat_model("google_genai:gemini-2.5-flash-lite") ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_google_genai import ChatGoogleGenerativeAI os.environ["GOOGLE_API_KEY"] = "..." model = ChatGoogleGenerativeAI(model="gemini-2.5-flash-lite") ``` 👉 阅读 [AWS Bedrock 聊天模型集成文档](/oss/python/integrations/chat/bedrock/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[aws]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.chat_models import init_chat_model # 按照此处步骤配置您的凭证： # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html model = init_chat_model( "anthropic.claude-3-5-sonnet-20240620-v1:0", model_provider="bedrock_converse", ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_aws import ChatBedrock model = ChatBedrock(model="anthropic.claude-3-5-sonnet-20240620-v1:0") ``` 👉 阅读 [HuggingFace 聊天模型集成文档](/oss/python/integrations/chat/huggingface/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[huggingface]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..." model = init_chat_model( "microsoft/Phi-3-mini-4k-instruct", model_provider="huggingface", temperature=0.7, max_tokens=1024, ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..." llm = HuggingFaceEndpoint( repo_id="microsoft/Phi-3-mini-4k-instruct", temperature=0.7, max_length=1024, ) model = ChatHuggingFace(llm=llm) ``` 👉 阅读 [OpenRouter 聊天模型集成文档](/oss/python/integrations/chat/openrouter/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain-openrouter" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["OPENROUTER_API_KEY"] = "sk-..." model = init_chat_model( "auto", model_provider="openrouter", ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_openrouter import ChatOpenRouter os.environ["OPENROUTER_API_KEY"] = "sk-..." model = ChatOpenRouter(model="auto") ``` 下面示例中显示的输出使用了 OpenAI。 ## 2. 配置数据库 你将为本教程创建一个 [SQLite 数据库](https://www.sqlitetutorial.net/sqlite-sample-database/)。SQLite 是一个轻量级数据库，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。 为方便起见，我们已将数据库 (`Chinook.db`) 托管在公共 GCS 存储桶中。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import requests, pathlib url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db" local_path = pathlib.Path("Chinook.db") if local_path.exists(): print(f"{local_path} already exists, skipping download.") else: response = requests.get(url) if response.status_code == 200: local_path.write_bytes(response.content) print(f"File downloaded and saved as {local_path}") else: print(f"Failed to download the file. Status code: {response.status_code}") ``` 我们将使用 `langchain_community` 包中提供的便捷 SQL 数据库包装器与数据库交互。该包装器提供了一个简单的接口来执行 SQL 查询和获取结果： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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')] ``` ## 3. 添加用于数据库交互的工具 使用 `langchain_community` 包中提供的 `SQLDatabase` 包装器与数据库交互。该包装器提供了一个简单的接口来执行 SQL 查询和获取结果： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.agent_toolkits import SQLDatabaseToolkit toolkit = SQLDatabaseToolkit(db=db, llm=model) 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! ``` ## 4. 使用 `create_agent` 使用 [`create_agent`](https://reference.langchain.com/python/langchain/agents/factory/create_agent) 以最少的代码构建一个 [ReAct 代理](https://arxiv.org/pdf/2210.03629)。该代理将解释请求并生成 SQL 命令，然后由工具执行。如果命令有错误，错误消息将返回给模型。然后模型可以检查原始请求和新的错误消息，并生成新的命令。这个过程可以持续进行，直到 LLM 成功生成命令或达到结束计数。这种向模型提供反馈（在这种情况下是错误消息）的模式非常强大。 使用描述性的系统提示初始化代理以自定义其行为： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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, ) ``` 现在，使用模型、工具和提示创建代理： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.agents import create_agent agent = create_agent( model, tools, system_prompt=system_prompt, ) ``` ## 5. 运行代理 在示例查询上运行代理并观察其行为： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} question = "Which genre on average has the longest tracks?" for step in agent.stream( {"messages": [{"role": "user", "content": question}]}, stream_mode="values", ): step["messages"][-1].pretty_print() ``` ``` ================================ Human Message ================================= Which genre on average has the longest tracks? ================================== Ai Message ================================== Tool Calls: sql_db_list_tables (call_BQsWg8P65apHc8BTJ1NPDvnM) Call ID: call_BQsWg8P65apHc8BTJ1NPDvnM Args: ================================= 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_i89tjKECFSeERbuACYm4w0cU) Call ID: call_i89tjKECFSeERbuACYm4w0cU Args: table_names: Track, Genre ================================= Tool Message ================================= Name: sql_db_schema CREATE TABLE "Genre" ( "GenreId" INTEGER NOT NULL, "Name" NVARCHAR(120), PRIMARY KEY ("GenreId") ) /* 3 rows from Genre table: GenreId Name 1 Rock 2 Jazz 3 Metal */ CREATE TABLE "Track" ( "TrackId" INTEGER NOT NULL, "Name" NVARCHAR(200) NOT NULL, "AlbumId" INTEGER, "MediaTypeId" INTEGER NOT NULL, "GenreId" INTEGER, "Composer" NVARCHAR(220), "Milliseconds" INTEGER NOT NULL, "Bytes" INTEGER, "UnitPrice" NUMERIC(10, 2) NOT NULL, PRIMARY KEY ("TrackId"), FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"), FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"), FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId") ) /* 3 rows from Track table: TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice 1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99 2 Balls to the Wall 2 2 1 U. Dirkschneider, W. Hoffmann, H. Frank, P. Baltes, S. Kaufmann, G. Hoffmann 342562 5510424 0.99 3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99 */ ================================== Ai Message ================================== Tool Calls: sql_db_query_checker (call_G64yYm6R6UauiVPCXJZMA49b) Call ID: call_G64yYm6R6UauiVPCXJZMA49b Args: query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AverageLength FROM Track INNER JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.Name ORDER BY AverageLength DESC LIMIT 5; ================================= Tool Message ================================= Name: sql_db_query_checker SELECT Genre.Name, AVG(Track.Milliseconds) AS AverageLength FROM Track INNER JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.Name ORDER BY AverageLength DESC LIMIT 5; ================================== Ai Message ================================== Tool Calls: sql_db_query (call_AnO3SrhD0ODJBxh6dHMwvHwZ) Call ID: call_AnO3SrhD0ODJBxh6dHMwvHwZ Args: query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AverageLength FROM Track INNER JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.Name ORDER BY AverageLength DESC LIMIT 5; ================================= Tool Message ================================= Name: sql_db_query [('Sci Fi & Fantasy', 2911783.0384615385), ('Science Fiction', 2625549.076923077), ('Drama', 2575283.78125), ('TV Shows', 2145041.0215053763), ('Comedy', 1585263.705882353)] ================================== Ai Message ================================== On average, the genre with the longest tracks is "Sci Fi & Fantasy" with an average track length of approximately 2,911,783 milliseconds. This is followed by "Science Fiction," "Drama," "TV Shows," and "Comedy." ``` 代理正确编写了查询，检查了查询，并运行了查询以提供其最终响应。 你可以在 [LangSmith 跟踪](https://smith.langchain.com/public/cd2ce887-388a-4bb1-a29d-48208ce50d15/r) 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。 ### （可选）使用 Studio [Studio](/langsmith/studio) 提供了一个“客户端”循环以及内存，因此你可以将其作为聊天界面运行并查询数据库。你可以提出诸如“告诉我数据库的模式”或“显示前 5 名客户的发票”之类的问题。你将看到生成的 SQL 命令和结果输出。有关如何启动的详细信息如下。 除了前面提到的包之外，你还需要： ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langgraph-cli[inmem]>=0.4.0 ``` 在你将运行的目录中，你需要一个包含以下内容的 `langgraph.json` 文件： ```json theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} { "dependencies": ["."], "graphs": { "agent": "./sql_agent.py:agent", "graph": "./sql_agent_langgraph.py:graph" }, "env": ".env" } ``` 创建一个文件 `sql_agent.py` 并插入以下内容： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} #sql_agent.py for studio import pathlib from langchain.agents import create_agent from langchain.chat_models import init_chat_model from langchain_community.agent_toolkits import SQLDatabaseToolkit from langchain_community.utilities import SQLDatabase import requests # Initialize an LLM model = init_chat_model("gpt-5.4") # Get the database, store it locally url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db" local_path = pathlib.Path("Chinook.db") if local_path.exists(): print(f"{local_path} already exists, skipping download.") else: response = requests.get(url) if response.status_code == 200: local_path.write_bytes(response.content) print(f"File downloaded and saved as {local_path}") else: print(f"Failed to download the file. Status code: {response.status_code}") db = SQLDatabase.from_uri("sqlite:///Chinook.db") # Create the tools toolkit = SQLDatabaseToolkit(db=db, llm=model) tools = toolkit.get_tools() for tool in tools: print(f"{tool.name}: {tool.description}\n") # Use create_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_agent( model, tools, system_prompt=system_prompt, ) ``` ## 6. 实现Human in the Loop审查 在执行代理的 SQL 查询之前进行检查，以防止任何意外操作或低效，这可能是谨慎的做法。 LangChain 代理支持内置的 [Human in the Loop中间件](/oss/python/langchain/human-in-the-loop)，以增加对代理工具调用的监督。让我们配置代理，以便在调用 `sql_db_query` 工具时暂停以供人工审查： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.agents import create_agent from langchain.agents.middleware import HumanInTheLoopMiddleware # [!code highlight] from langgraph.checkpoint.memory import InMemorySaver # [!code highlight] agent = create_agent( model, tools, system_prompt=system_prompt, middleware=[ # [!code highlight] HumanInTheLoopMiddleware( # [!code highlight] interrupt_on={"sql_db_query": True}, # [!code highlight] description_prefix="Tool execution pending approval", # [!code highlight] ), # [!code highlight] ], # [!code highlight] checkpointer=InMemorySaver(), # [!code highlight] ) ``` 我们为代理添加了一个 [检查点](/oss/python/langchain/short-term-memory)，以允许执行暂停和恢复。有关此内容以及可用中间件配置的详细信息，请参阅 [Human in the Loop指南](/oss/python/langchain/human-in-the-loop)。 运行代理时，它现在将在执行 `sql_db_query` 工具之前暂停以供审查： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} question = "Which genre on average has the longest tracks?" config = {"configurable": {"thread_id": "1"}} # [!code highlight] for step in agent.stream( {"messages": [{"role": "user", "content": question}]}, config, # [!code highlight] stream_mode="values", ): if "__interrupt__" in step: # [!code highlight] print("INTERRUPTED:") # [!code highlight] interrupt = step["__interrupt__"][0] # [!code highlight] for request in interrupt.value["action_requests"]: # [!code highlight] print(request["description"]) # [!code highlight] elif "messages" in step: step["messages"][-1].pretty_print() else: pass ``` ``` ... INTERRUPTED: Tool execution pending approval Tool: sql_db_query Args: {'query': 'SELECT g.Name AS Genre, AVG(t.Milliseconds) AS AvgTrackLength FROM Track t JOIN Genre g ON t.GenreId = g.GenreId GROUP BY g.Name ORDER BY AvgTrackLength DESC LIMIT 1;'} ``` 我们可以恢复执行，在这种情况下接受查询，使用 [Command](/oss/python/langgraph/use-graph-api#combine-control-flow-and-state-updates-with-command)： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph.types import Command # [!code highlight] for step in agent.stream( Command(resume={"decisions": [{"type": "approve"}]}), # [!code highlight] config, stream_mode="values", ): if "messages" in step: step["messages"][-1].pretty_print() if "__interrupt__" in step: print("INTERRUPTED:") interrupt = step["__interrupt__"][0] for request in interrupt.value["action_requests"]: print(request["description"]) else: pass ``` ``` ================================== Ai Message ================================== Tool Calls: sql_db_query (call_7oz86Epg7lYRqi9rQHbZPS1U) Call ID: call_7oz86Epg7lYRqi9rQHbZPS1U Args: query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgDuration FROM Track JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.Name ORDER BY AvgDuration DESC LIMIT 5; ================================= Tool Message ================================= Name: sql_db_query [('Sci Fi & Fantasy', 2911783.0384615385), ('Science Fiction', 2625549.076923077), ('Drama', 2575283.78125), ('TV Shows', 2145041.0215053763), ('Comedy', 1585263.705882353)] ================================== Ai Message ================================== The genre with the longest average track length is "Sci Fi & Fantasy" with an average duration of about 2,911,783 milliseconds, followed by "Science Fiction" and "Drama." ``` 有关详细信息，请参阅 [Human in the Loop指南](/oss/python/langchain/human-in-the-loop)。 ## 后续步骤 如需更深入的自定义，请查看 [本教程](/oss/python/langgraph/sql-agent)，了解如何直接使用 LangGraph 原语实现 SQL 代理。 ***
[将这些文档](/use-these-docs) 通过 MCP 连接到 Claude、VSCode 等，以获取实时答案。 [在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/sql-agent.mdx) 或 [提交问题](https://github.com/langchain-ai/docs/issues/new/choose)。