> ## Documentation Index > Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt > Use this file to discover all available pages before exploring further. # Sql agent title: 构建自定义 SQL 智能体 sidebarTitle: 自定义 SQL 智能体 ------------------------- 在本教程中，我们将使用 LangGraph 构建一个可以回答有关 SQL 数据库问题的自定义智能体。 LangChain 提供了内置的 [智能体](/oss/javascript/langchain/agents) 实现，这些实现是使用 [LangGraph](/oss/javascript/langgraph/overview) 原语构建的。如果需要更深度的定制，可以直接在 LangGraph 中实现智能体。本指南演示了 SQL 智能体的一个示例实现。如需实践介绍，请参阅 [使用更高级别的 LangChain 抽象构建 SQL 智能体](/oss/javascript/langchain/sql-agent)。 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。确保您的数据库连接权限始终尽可能窄地限定在智能体的需求范围内。这将减轻（尽管不能完全消除）构建模型驱动系统的风险。 [预构建的智能体](/oss/javascript/langchain/sql-agent) 让我们能够快速开始，但我们依赖于系统提示来约束其行为——例如，我们指示智能体始终从“列出表”工具开始，并在执行查询之前始终运行查询检查器工具。 我们可以通过定制智能体在 LangGraph 中实施更高程度的控制。在这里，我们实现了一个简单的 ReAct 智能体设置，具有用于特定工具调用的专用节点。我们将使用与预构建智能体相同的 \[状态]。 ### 概念 我们将涵盖以下概念： * [工具](/oss/javascript/langchain/tools) 用于读取 SQL 数据库 * LangGraph [图 API](/oss/javascript/langgraph/graph-api)，包括状态、节点、边和条件边。 * [人在回路](/oss/javascript/langgraph/interrupts) 流程 ## 设置 ### 安装 ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm i langchain @langchain/core @langchain/classic @langchain/langgraph @langchain/openai typeorm sqlite3 zod ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add langchain @langchain/core @langchain/classic @langchain/langgraph @langchain/openai typeorm sqlite3 zod ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add langchain @langchain/core @langchain/classic @langchain/langgraph @langchain/openai typeorm sqlite3 zod ``` ### LangSmith 设置 [LangSmith](https://smith.langchain.com) 以检查链或智能体内部发生的情况。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 LLM 选择一个支持 [工具调用](/oss/javascript/integrations/providers/overview) 的模型： 👉 阅读 [OpenAI 聊天模型集成文档](/oss/javascript/integrations/chat/openai/) ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/openai ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm install @langchain/openai ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/openai ``` ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} bun add @langchain/openai ``` ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { initChatModel } from "langchain"; process.env.OPENAI_API_KEY = "your-api-key"; const model = await initChatModel("gpt-5.2"); ``` ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatOpenAI } from "@langchain/openai"; const model = new ChatOpenAI({ model: "gpt-5.2", apiKey: "your-api-key" }); ``` 👉 阅读 [Anthropic 聊天模型集成文档](/oss/javascript/integrations/chat/anthropic/) ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/anthropic ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm install @langchain/anthropic ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/anthropic ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/anthropic ``` ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { initChatModel } from "langchain"; process.env.ANTHROPIC_API_KEY = "your-api-key"; const model = await initChatModel("claude-sonnet-4-6"); ``` ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatAnthropic } from "@langchain/anthropic"; const model = new ChatAnthropic({ model: "claude-sonnet-4-6", apiKey: "your-api-key" }); ``` 👉 阅读 [Azure 聊天模型集成文档](/oss/javascript/integrations/chat/azure/) ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/azure ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm install @langchain/azure ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/azure ``` ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} bun add @langchain/azure ``` ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { initChatModel } from "langchain"; process.env.AZURE_OPENAI_API_KEY = "your-api-key"; process.env.AZURE_OPENAI_ENDPOINT = "your-endpoint"; process.env.OPENAI_API_VERSION = "your-api-version"; const model = await initChatModel("azure_openai:gpt-5.2"); ``` ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { AzureChatOpenAI } from "@langchain/openai"; const model = new AzureChatOpenAI({ model: "gpt-5.2", azureOpenAIApiKey: "your-api-key", azureOpenAIApiEndpoint: "your-endpoint", azureOpenAIApiVersion: "your-api-version" }); ``` 👉 阅读 [Google GenAI 聊天模型集成文档](/oss/javascript/integrations/chat/google_generative_ai/) ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/google-genai ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm install @langchain/google-genai ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/google-genai ``` ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} bun add @langchain/google-genai ``` ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { initChatModel } from "langchain"; process.env.GOOGLE_API_KEY = "your-api-key"; const model = await initChatModel("google-genai:gemini-2.5-flash-lite"); ``` ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatGoogleGenerativeAI } from "@langchain/google-genai"; const model = new ChatGoogleGenerativeAI({ model: "gemini-2.5-flash-lite", apiKey: "your-api-key" }); ``` 👉 阅读 [AWS Bedrock 聊天模型集成文档](/oss/javascript/integrations/chat/bedrock_converse/) ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/aws ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm install @langchain/aws ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/aws ``` ```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} bun add @langchain/aws ``` ```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { initChatModel } from "langchain"; // 按照以下步骤配置您的凭据： // https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html const model = await initChatModel("bedrock:gpt-5.2"); ``` ```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { ChatBedrockConverse } from "@langchain/aws"; // 按照以下步骤配置您的凭据： // https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html const model = new ChatBedrockConverse({ model: "gpt-5.2", region: "us-east-2" }); ``` 下面示例中显示的输出使用了 OpenAI。 ## 2. 配置数据库 您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。 为了方便起见，我们将数据库 (`Chinook.db`) 托管在公共 GCS 存储桶上。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import fs from "node:fs/promises"; import path from "node:path"; const url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db"; const localPath = path.resolve("Chinook.db"); async function resolveDbPath() { const exists = await fs.access(localPath).then(() => true).catch(() => false); if (exists) { console.log(`${localPath} already exists, skipping download.`); return localPath; } const resp = await fetch(url); if (!resp.ok) throw new Error(`Failed to download DB. Status code: ${resp.status}`); const buf = Buffer.from(await resp.arrayBuffer()); await fs.writeFile(localPath, buf); console.log(`File downloaded and saved as ${localPath}`); return localPath; } ``` 我们将使用 `@langchain/classic/sql_db` 模块中可用的便捷 SQL 数据库包装器来与数据库交互。该包装器提供了一个简单的接口来执行 SQL 查询并获取结果： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { SqlDatabase } from "@langchain/classic/sql_db"; import { DataSource } from "typeorm"; const dbPath = await resolveDbPath(); const datasource = new DataSource({ type: "sqlite", database: dbPath }); const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource }); const dialect = db.appDataSourceOptions.type; console.log(`Dialect: ${dialect}`); const tableNames = db.allTables.map(t => t.tableName); console.log(`Available tables: ${tableNames.join(", ")}`); const sampleResults = await db.run("SELECT * FROM Artist LIMIT 5;"); console.log(`Sample output: ${sampleResults}`); ``` ``` Dialect: sqlite Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track Sample output: [{"ArtistId":1,"Name":"AC/DC"},{"ArtistId":2,"Name":"Accept"},{"ArtistId":3,"Name":"Aerosmith"},{"ArtistId":4,"Name":"Alanis Morissette"},{"ArtistId":5,"Name":"Alice In Chains"}] ``` ## 3. 添加数据库交互工具 我们将创建自定义工具来与数据库交互： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { tool } from "langchain"; import * as z from "zod"; // Tool to list all tables const listTablesTool = tool( async () => { const tableNames = db.allTables.map(t => t.tableName); return tableNames.join(", "); }, { name: "sql_db_list_tables", description: "Input is an empty string, output is a comma-separated list of tables in the database.", schema: z.object({}), } ); // Tool to get schema for specific tables const getSchemaTool = tool( async ({ table_names }) => { const tables = table_names.split(",").map(t => t.trim()); return await db.getTableInfo(tables); }, { name: "sql_db_schema", 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", schema: z.object({ table_names: z.string().describe("Comma-separated list of table names"), }), } ); // Tool to execute SQL query const queryTool = tool( async ({ query }) => { try { const result = await db.run(query); return typeof result === "string" ? result : JSON.stringify(result); } catch (error) { return `Error: ${error.message}`; } }, { name: "sql_db_query", description: "Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again.", schema: z.object({ query: z.string().describe("SQL query to execute"), }), } ); const tools = [listTablesTool, getSchemaTool, queryTool]; for (const tool of tools) { console.log(`${tool.name}: ${tool.description}\n`); } ``` ``` sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database. 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_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. ``` ## 4. 定义应用步骤 我们为以下步骤构建专用节点： * 列出数据库表 * 调用“获取模式”工具 * 生成查询 * 检查查询 将这些步骤放在专用节点中，我们可以 (1) 在需要时强制进行工具调用，以及 (2) 自定义每个步骤关联的提示词。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { AIMessage, ToolMessage, SystemMessage, HumanMessage } from "@langchain/core/messages"; import { ToolNode } from "@langchain/langgraph/prebuilt"; import { StateSchema, MessagesValue, GraphNode, StateGraph, START, END } from "@langchain/langgraph"; import { z } from "zod/v4"; // Create tool nodes for schema and query execution const getSchemaNode = new ToolNode([getSchemaTool]); const runQueryNode = new ToolNode([queryTool]); // Define state schema const MessagesState = new StateSchema({ messages: MessagesValue, }); // Example: create a predetermined tool call const listTables: GraphNode = async (state) => { const toolCall = { name: "sql_db_list_tables", args: {}, id: "abc123", type: "tool_call" as const, }; const toolCallMessage = new AIMessage({ content: "", tool_calls: [toolCall], }); const toolMessage = await listTablesTool.invoke({}); const response = new AIMessage(`Available tables: ${toolMessage}`); return { messages: [toolCallMessage, new ToolMessage({ content: toolMessage, tool_call_id: "abc123" }), response] }; }; // Example: force a model to create a tool call const callGetSchema: GraphNode = async (state) => { const llmWithTools = model.bindTools([getSchemaTool], { tool_choice: "any", }); const response = await llmWithTools.invoke(state.messages); return { messages: [response] }; }; const topK = 5; const generateQuerySystemPrompt = ` 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 ${topK} 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. `; const generateQuery: GraphNode = async (state) => { const systemMessage = new SystemMessage(generateQuerySystemPrompt); // We do not force a tool call here, to allow the model to // respond naturally when it obtains the solution. const llmWithTools = model.bindTools([queryTool]); const response = await llmWithTools.invoke([systemMessage, ...state.messages]); return { messages: [response] }; }; const checkQuerySystemPrompt = ` 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. `; const checkQuery: GraphNode = async (state) => { const systemMessage = new SystemMessage(checkQuerySystemPrompt); // Generate an artificial user message to check const lastMessage = state.messages[state.messages.length - 1]; if (!lastMessage.tool_calls || lastMessage.tool_calls.length === 0) { throw new Error("No tool calls found in the last message"); } const toolCall = lastMessage.tool_calls[0]; const userMessage = new HumanMessage(toolCall.args.query); const llmWithTools = model.bindTools([queryTool], { tool_choice: "any", }); const response = await llmWithTools.invoke([systemMessage, userMessage]); // Preserve the original message ID response.id = lastMessage.id; return { messages: [response] }; }; ``` ## 5. 实现智能体 我们现在可以使用 [图 API](/oss/javascript/langgraph/graph-api) 将这些步骤组装成工作流。我们在查询生成步骤定义一个 [条件边](/oss/javascript/langgraph/graph-api#conditional-edges)，如果生成了查询，则路由到查询检查器，或者如果没有工具调用（例如 LLM 已提供对查询的响应），则结束。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { StateGraph, ConditionalEdgeRouter } from "@langchain/langgraph"; const shouldContinue: ConditionalEdgeRouter = (state) => { const messages = state.messages; const lastMessage = messages[messages.length - 1]; if (!lastMessage.tool_calls || lastMessage.tool_calls.length === 0) { return END; } else { return "check_query"; } }; const builder = new StateGraph(MessagesState) .addNode("list_tables", listTables) .addNode("call_get_schema", callGetSchema) .addNode("get_schema", getSchemaNode) .addNode("generate_query", generateQuery) .addNode("check_query", checkQuery) .addNode("run_query", runQueryNode) .addEdge(START, "list_tables") .addEdge("list_tables", "call_get_schema") .addEdge("call_get_schema", "get_schema") .addEdge("get_schema", "generate_query") .addConditionalEdges("generate_query", shouldContinue) .addEdge("check_query", "run_query") .addEdge("run_query", "generate_query"); const agent = builder.compile(); ``` 我们在下方可视化应用程序： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import * as fs from "node:fs/promises"; const drawableGraph = await agent.getGraphAsync(); const image = await drawableGraph.drawMermaidPng(); const imageBuffer = new Uint8Array(await image.arrayBuffer()); await fs.writeFile("graph.png", imageBuffer); ``` SQL 智能体图 我们现在可以调用图： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const question = "Which genre on average has the longest tracks?"; const stream = await agent.stream( { messages: [{ role: "user", content: question }] }, { streamMode: "values" } ); for await (const step of stream) { if (step.messages && step.messages.length > 0) { const lastMessage = step.messages[step.messages.length - 1]; console.log(lastMessage.toFormattedString()); } } ``` ``` ================================ Human Message ================================= Which genre on average has the longest tracks? ================================== Ai Message ================================== Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track ================================== Ai Message ================================== Tool Calls: sql_db_schema (call_yzje0tj7JK3TEzDx4QnRR3lL) Call ID: call_yzje0tj7JK3TEzDx4QnRR3lL Args: table_names: Genre, Track ================================= 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 (call_cb9ApLfZLSq7CWg6jd0im90b) Call ID: call_cb9ApLfZLSq7CWg6jd0im90b Args: query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds FROM Track JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.GenreId ORDER BY AvgMilliseconds DESC LIMIT 5; ================================== Ai Message ================================== Tool Calls: sql_db_query (call_DMVALfnQ4kJsuF3Yl6jxbeAU) Call ID: call_DMVALfnQ4kJsuF3Yl6jxbeAU Args: query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds FROM Track JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.GenreId ORDER BY AvgMilliseconds 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 tracks on average is "Sci Fi & Fantasy," with an average track length of approximately 2,911,783 milliseconds. Other genres with relatively long tracks include "Science Fiction," "Drama," "TV Shows," and "Comedy." ``` 查看上述运行的 [LangSmith 追踪](https://smith.langchain.com/public/a6a96896-686a-4040-b9b5-28d701453d6f/r)。 ## 6. 实现人在回路审查 在执行 SQL 查询之前检查智能体的查询是否有意外操作或低效之处可能是明智的。 这里我们利用 LangGraph 的 [人在回路](/oss/javascript/langgraph/interrupts) 功能，在执行 SQL 查询之前暂停运行并等待人工审查。使用 LangGraph 的 [持久化层](/oss/javascript/langgraph/persistence)，我们可以无限期地（或至少只要持久化层存活）暂停运行。 让我们将 `sql_db_query` 工具包装在一个接收人类输入的节点中。我们可以使用 [中断](/oss/javascript/langgraph/interrupts) 函数来实现这一点。下面，我们允许输入批准工具调用、编辑其参数或提供用户反馈。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { RunnableConfig } from "@langchain/core/runnables"; import { tool } from "langchain"; import { interrupt } from "@langchain/langgraph"; import * as z from "zod"; const queryToolWithInterrupt = tool( async (input, config: RunnableConfig) => { const request = { action: queryTool.name, args: input, description: "Please review the tool call", }; const response = interrupt([request]); // [!code highlight] // approve the tool call if (response.type === "accept") { const toolResponse = await queryTool.invoke(input, config); return toolResponse; } // update tool call args else if (response.type === "edit") { const editedInput = response.args.args; const toolResponse = await queryTool.invoke(editedInput, config); return toolResponse; } // respond to the LLM with user feedback else if (response.type === "response") { const userFeedback = response.args; return userFeedback; } else { throw new Error(`Unsupported interrupt response type: ${response.type}`); } }, { name: queryTool.name, description: queryTool.description, schema: queryTool.schema, } ); ``` 上述实现遵循更广泛的 [人在回路](/oss/javascript/langgraph/interrupts) 指南中的 [工具中断示例](/oss/javascript/langgraph/interrupts#interrupts-in-tools)。请参考该指南了解详细信息和替代方案。 现在让我们重新组装我们的图。我们将用人工审查替换程序化检查。请注意，我们现在包含一个 [检查点](/oss/javascript/langgraph/persistence)；这是暂停和恢复运行所必需的。 ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { MemorySaver, ConditionalEdgeRouter } from "@langchain/langgraph"; const shouldContinueWithHuman: ConditionalEdgeRouter = (state) => { const messages = state.messages; const lastMessage = messages[messages.length - 1]; if (!lastMessage.tool_calls || lastMessage.tool_calls.length === 0) { return END; } else { return "run_query"; } }; const runQueryNodeWithInterrupt = new ToolNode([queryToolWithInterrupt]); const builderWithHuman = new StateGraph(MessagesState) .addNode("list_tables", listTables) .addNode("call_get_schema", callGetSchema) .addNode("get_schema", getSchemaNode) .addNode("generate_query", generateQuery) .addNode("run_query", runQueryNodeWithInterrupt) .addEdge(START, "list_tables") .addEdge("list_tables", "call_get_schema") .addEdge("call_get_schema", "get_schema") .addEdge("get_schema", "generate_query") .addConditionalEdges("generate_query", shouldContinueWithHuman) .addEdge("run_query", "generate_query"); const checkpointer = new MemorySaver(); // [!code highlight] const agentWithHuman = builderWithHuman.compile({ checkpointer }); // [!code highlight] ``` 我们可以像以前一样调用图。这次，执行被中断： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const config = { configurable: { thread_id: "1" } }; const question = "Which genre on average has the longest tracks?"; const stream = await agentWithHuman.stream( { messages: [{ role: "user", content: question }] }, { ...config, streamMode: "values" } ); for await (const step of stream) { if (step.messages && step.messages.length > 0) { const lastMessage = step.messages[step.messages.length - 1]; console.log(lastMessage.toFormattedString()); } } // Check for interrupts const state = await agentWithHuman.getState(config); if (state.next.length > 0) { console.log("\nINTERRUPTED:"); console.log(JSON.stringify(state.tasks[0].interrupts[0], null, 2)); } ``` ``` ... INTERRUPTED: { "action": "sql_db_query", "args": { "query": "SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgLength FROM Track JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.Name ORDER BY AvgLength DESC LIMIT 5;" }, "description": "Please review the tool call" } ``` 我们可以使用 [命令](/oss/javascript/langgraph/use-graph-api#combine-control-flow-and-state-updates-with-command) 接受或编辑工具调用： ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { Command } from "@langchain/langgraph"; const resumeStream = await agentWithHuman.stream( new Command({ resume: { type: "accept" } }), // new Command({ resume: { type: "edit", args: { query: "..." } } }), { ...config, streamMode: "values" } ); for await (const step of resumeStream) { if (step.messages && step.messages.length > 0) { const lastMessage = step.messages[step.messages.length - 1]; console.log(lastMessage.toFormattedString()); } } ``` ``` ================================== Ai Message ================================== Tool Calls: sql_db_query (call_t4yXkD6shwdTPuelXEmY3sAY) Call ID: call_t4yXkD6shwdTPuelXEmY3sAY Args: query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgLength FROM Track JOIN Genre ON Track.GenreId = Genre.GenreId GROUP BY Genre.Name ORDER BY AvgLength 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 length of about 2,911,783 milliseconds. Other genres with long average track lengths include "Science Fiction," "Drama," "TV Shows," and "Comedy." ``` 有关详细信息，请参阅 [人在回路指南](/oss/javascript/langgraph/interrupts)。 ## 下一步 查看 [评估图](/langsmith/evaluate-graph) 指南，了解如何使用 LangSmith 评估 LangGraph 应用程序，包括此类 SQL 智能体。 ***
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