> ## Documentation Index > Fetch the complete documentation index at: https://langchain-docs-zh.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # 构建数据分析代理 > 本指南演示如何使用 深度智能体 构建数据分析智能体。数据分析任务通常需要多步骤推理、代码执行以及处理脚本、报告和图表等工件——深度智能体正是为处理这些能力而设计的。 ## 概述 本指南演示如何使用 [深度智能体](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents.md) 构建数据分析智能体。数据分析任务通常需要多步骤推理、代码执行以及处理脚本、报告和图表等工件——深度智能体正是为处理这些能力而设计的。 您将构建的智能体将： 1. 接收 CSV 文件进行分析 2. 使用可选的待办事项列表规划并跟踪分析步骤 3. 执行探索性数据分析并生成可视化 4. 将结果分享到 Slack 频道 > [!TIP] > > Slack 集成是可选的。可以通过修改智能体将工件保存在本地或通过其他频道分享结果。 ### 核心概念 本教程涵盖： * 用于沙盒化代码执行的 [后端](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/backends.md) * 用于外部集成的自定义 [工具](https://langchain-docs-zh.mintlify.site/langchain/oss/langchain/tools.md) * 使用 @\[`TodoListMiddleware` 进行可选的 [任务规划](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/overview.md#任务规划) ## 设置 ### 安装 安装核心依赖项： :::python ```bash pip theme={null} pip install deepagents ``` ::: ### 可选依赖项 本教程将使用： * 用于分享结果的 [Slack Python SDK](https://docs.slack.dev/tools/python-slack-sdk/) ([令牌设置](https://docs.slack.dev/authentication/tokens/)) * 用于代码执行的 [LangSmith 沙盒](https://docs.langchain.com/langsmith/sandboxes) :::python ```bash pip theme={null} pip install langsmith slack-sdk ``` ::: > [!NOTE] > > 这些服务是可选的，但沙盒化环境对于任何生产使用都强烈推荐。您也可以使用本地 Shell 后端（需注意重要的 [安全考虑](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/backends.md#localshellbackend-本地-shell)）或直接从后端下载工件。 ### LangSmith 您使用 LangChain 构建的大多数应用程序都包含多个步骤，涉及多次 LLM 调用。随着应用程序变得越来越复杂，能够检查链或智能体内部确切发生的事情变得至关重要。[LangSmith](https://smith.langchain.com) 是实现这一目标的最佳方式。 在上述链接中注册后，请确保设置环境变量以开始记录跟踪： ```shell theme={null} export LANGSMITH_TRACING="true" export LANGSMITH_API_KEY="..." ``` :::python 或者，在 Python 中设置： ```python theme={null} import getpass import os os.environ["LANGSMITH_TRACING"] = "true" os.environ["LANGSMITH_API_KEY"] = getpass.getpass() ``` ::: ## 设置后端 深度智能体使用 [后端](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/backends.md) 在沙盒化环境中执行代码。 以下示例使用 [LangSmith 沙盒](https://docs.langchain.com/langsmith/sandboxes)。其他提供者请参阅 [可用提供者](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/sandboxes.md#可用提供者)。 > [tab] LangSmith > > ```bash pip theme={null} > pip install langsmith > ``` > > ```bash uv theme={null} > uv add langsmith > ``` > > ```python theme={null} > from deepagents.backends.langsmith import LangSmithSandbox > from langsmith.sandbox import SandboxClient > > client = SandboxClient() > ls_sandbox = client.create_sandbox() > backend = LangSmithSandbox(sandbox=ls_sandbox) > ``` > [tab] Local shell > > > [!WARNING] > > > > 此后端提供无限制的文件系统和 Shell 访问权限。仅在受控环境中用于开发和测试。更多详情请参阅 [安全考虑](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/backends.md#localshellbackend-本地-shell)。 > > ```python theme={null} > from deepagents.backends import LocalShellBackend > > backend = LocalShellBackend( > root_dir=".", > virtual_mode=True, > env={"PATH": "/usr/bin:/bin"}, > ) > ``` > [tab] AgentCore > > ```bash pip theme={null} > pip install langchain-agentcore-codeinterpreter > ``` > > ```bash uv theme={null} > uv add langchain-agentcore-codeinterpreter > ``` > > ```python theme={null} > from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter > from langchain_agentcore_codeinterpreter import AgentCoreSandbox > > interpreter = CodeInterpreter(region="us-west-2") > interpreter.start() > backend = AgentCoreSandbox(interpreter=interpreter) > ``` > [tab] Daytona > > ```bash pip theme={null} > pip install langchain-daytona > ``` > > ```bash uv theme={null} > uv add langchain-daytona > ``` > > ```python theme={null} > from daytona import Daytona > > from langchain_daytona import DaytonaSandbox > > sandbox = Daytona().create() > backend = DaytonaSandbox(sandbox=sandbox) > ``` > > 验证沙盒是否就绪： > > ```python theme={null} > result = backend.execute("echo ready") > print(result) > # ExecuteResponse(output='ready', exit_code=0, ...) > ``` > [tab] E2B > > ```bash pip theme={null} > pip install langchain-e2b > ``` > > ```bash uv theme={null} > uv add langchain-e2b > ``` > > ```python theme={null} > from e2b import Sandbox > from langchain_e2b import E2BSandbox > > e2b_sandbox = Sandbox.create() > backend = E2BSandbox(sandbox=e2b_sandbox) > ``` > [tab] Modal > > ```python theme={null} > import modal > > from langchain_modal import ModalSandbox > > app = modal.App.lookup("your-app") > modal_sandbox = modal.Sandbox.create(app=app) > backend = ModalSandbox(sandbox=modal_sandbox) > ``` > [tab] Runloop > > ```bash pip theme={null} > pip install langchain-runloop > ``` > > ```bash uv theme={null} > uv add langchain-runloop > ``` > > ```python theme={null} > from runloop_api_client import RunloopSDK > > from langchain_runloop import RunloopSandbox > > api_key = "..." > client = RunloopSDK(bearer_token=api_key) > > devbox = client.devbox.create() > backend = RunloopSandbox(devbox=devbox) > ``` ### 上传示例数据 创建并上传示例销售数据到后端： ```python theme={null} import csv import io # Create sample sales data data = [ ["Date", "Product", "Units Sold", "Revenue"], ["2025-08-01", "Widget A", 10, 250], ["2025-08-02", "Widget B", 5, 125], ["2025-08-03", "Widget A", 7, 175], ["2025-08-04", "Widget C", 3, 90], ["2025-08-05", "Widget B", 8, 200], ] # Convert to CSV bytes text_buf = io.StringIO() writer = csv.writer(text_buf) writer.writerows(data) csv_bytes = text_buf.getvalue().encode("utf-8") text_buf.close() # Upload to backend backend.upload_files([("/root/data/sales_data.csv", csv_bytes)]) ``` ## 实现自定义工具 数据分析任务可能会生成报告或图表等工件。 以下简单的 [工具](https://langchain-docs-zh.mintlify.site/langchain/oss/langchain/tools.md) 使用 `backend.download_files` 下载它们，然后使用 Slack SDK 上传。 我们也可以让智能体列出相关文件路径，而不是上传它们，以便相关方按需获取。 ```python theme={null} import os from langchain.tools import tool from slack_sdk import WebClient slack_token = os.environ["SLACK_USER_TOKEN"] slack_client = WebClient(token=slack_token) channel = "C0123456ABC" # specify your own channel here @tool(parse_docstring=True) def slack_send_message(text: str, file_path: str | None = None) -> str: """Send message, optionally including attachments such as images. Args: text: (str) text content of the message file_path: (str) file path of attachment in the filesystem. """ if not file_path: slack_client.chat_postMessage(channel=channel, text=text) else: fp = backend.download_files([file_path]) slack_client.files_upload_v2( channel=channel, content=fp[0].content, initial_comment=text, ) return "Message sent." ``` ※ 重要提示：在您之前的输出中占位符集有误。缺少的 ID：\['39']; 预期外多余的 ID：\[]。请再次翻译全文，确保每个占位符（六边形括号 + 数字）都原样保留，且仅出现一次。 > [!NOTE] > > 通常建议避免将凭证和其他机密信息添加到沙盒中。这里我们在工具外部管理 Slack token。 ## 启用任务规划 [任务规划](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/overview.md#任务规划)是可选的。数据分析通常涉及长时间、多步骤的工作，因此在创建智能体时传递 @\[`TodoListMiddleware`]。这为智能体提供了一个`write_todos`工具，用于跟踪探索性分析、可视化和共享步骤。 :::python ```python theme={null} from langchain.agents.middleware import TodoListMiddleware ``` ::: 在下一节的`create_deep_agent`调用中包含此中间件。 ## 运行智能体 让我们实例化一个智能体： ```python theme={null} from langchain_core.utils.uuid import uuid7 from deepagents import create_deep_agent from langchain.agents.middleware import TodoListMiddleware from langgraph.checkpoint.memory import InMemorySaver checkpointer = InMemorySaver() agent = create_deep_agent( model="google_genai:gemini-3.6-flash", tools=[slack_send_message], backend=backend, checkpointer=checkpointer, middleware=[TodoListMiddleware()], ) thread_id = str(uuid7()) config = {"configurable": {"thread_id": thread_id}} ``` 我们包含： * [模型](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/customization.md#模型)的选择 * 我们的定制[工具](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/customization.md#工具) * [后端](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/backends.md) * 一个[检查点器](https://langchain-docs-zh.mintlify.site/langchain/oss/langchain/short-term-memory.md)以支持多轮对话 * @\[`TodoListMiddleware`]用于可选的[任务规划](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/overview.md#任务规划) 现在让我们调用我们的智能体。 ```python theme={null} input_message = { "role": "user", "content": ( "Analyze ./data/sales_data.csv in the current dir and generate a beautiful plot. " "When finished, send your analysis and the plot to Slack using the tool." ), } stream = agent.stream_events( {"messages": [input_message]}, config, version="v3", ) for snapshot in stream.values: snapshot["messages"][-1].pretty_print() ``` ```Result expandable theme={null} ================================== Ai Message ================================== [{'text': "I'll help you analyze the sales data and create a beautiful plot, then send the results to Slack. Let me start by exploring the data.", 'type': 'text'}, {'id': 'toolu_01LRot5h6WkhdpDQ1SG6EQGQ', 'input': {'file_path': './data/sales_data.csv'}, 'name': 'read_file', 'type': 'tool_use'}] Tool Calls: read_file (toolu_01LRot5h6WkhdpDQ1SG6EQGQ) Call ID: toolu_01LRot5h6WkhdpDQ1SG6EQGQ Args: file_path: ./data/sales_data.csv ================================= Tool Message ================================= Name: read_file Error: File '/data/sales_data.csv' not found ================================== Ai Message ================================== [{'text': 'Let me check the current directory structure first:', 'type': 'text'}, {'id': 'toolu_018DSeF8Dx714Dh2M5vJ1JuK', 'input': {'command': 'pwd'}, 'name': 'execute', 'type': 'tool_use'}, {'id': 'toolu_01As782aRsVB3mJpKPKNwg9c', 'input': {'path': '/'}, 'name': 'ls', 'type': 'tool_use'}] Tool Calls: execute (toolu_018DSeF8Dx714Dh2M5vJ1JuK) Call ID: toolu_018DSeF8Dx714Dh2M5vJ1JuK Args: command: pwd ls (toolu_01As782aRsVB3mJpKPKNwg9c) Call ID: toolu_01As782aRsVB3mJpKPKNwg9c Args: path: / ================================= Tool Message ================================= Name: execute /root [Command succeeded with exit code 0] ================================= Tool Message ================================= Name: ls ['/bin', '/boot', '/dev', '/etc', '/home', '/lib', '/lib64', '/media', '/mnt', '/opt', '/proc', '/root', '/run', '/sbin', '/srv', '/sys', '/tmp', '/usr', '/var', '/.dockerenv'] ================================== Ai Message ================================== [{'id': 'toolu_01Sv4ut1P2XFsjrzVmQbJLpr', 'input': {'path': '/root'}, 'name': 'ls', 'type': 'tool_use'}] Tool Calls: ls (toolu_01Sv4ut1P2XFsjrzVmQbJLpr) Call ID: toolu_01Sv4ut1P2XFsjrzVmQbJLpr Args: path: /root ================================= Tool Message ================================= Name: ls ['/root/.bash_logout', '/root/.bashrc', '/root/.profile', '/root/data'] ================================== Ai Message ================================== [{'text': "Great! I can see there's a data directory. Let me check its contents:", 'type': 'text'}, {'id': 'toolu_01WN6V9RZjGGoDXzuWdEgB1z', 'input': {'path': '/root/data'}, 'name': 'ls', 'type': 'tool_use'}] Tool Calls: ls (toolu_01WN6V9RZjGGoDXzuWdEgB1z) Call ID: toolu_01WN6V9RZjGGoDXzuWdEgB1z Args: path: /root/data ================================= Tool Message ================================= Name: ls ['/root/data/sales_data.csv'] ================================== Ai Message ================================== [{'text': 'Perfect! Now let me read the sales data:', 'type': 'text'}, {'id': 'toolu_01RFmneg7mua1G5csH3rH2Zq', 'input': {'file_path': '/root/data/sales_data.csv'}, 'name': 'read_file', 'type': 'tool_use'}] Tool Calls: read_file (toolu_01RFmneg7mua1G5csH3rH2Zq) Call ID: toolu_01RFmneg7mua1G5csH3rH2Zq Args: file_path: /root/data/sales_data.csv ================================= Tool Message ================================= Name: read_file 1 Date,Product,Units Sold,Revenue 2 2025-08-01,Widget A,10,250 3 2025-08-02,Widget B,5,125 4 2025-08-03,Widget A,7,175 5 2025-08-04,Widget C,3,90 6 2025-08-05,Widget B,8,200 ================================== Ai Message ================================== [{'text': "Excellent! I can see the sales data. Now I'll create a Python script to analyze the data and generate a beautiful plot. Let me create a comprehensive analysis with visualizations:", 'type': 'text'}, {'id': 'toolu_01JcBjEXgPpCPuaGxn9ALwMg', 'input': {'file_path': '/root/analyze_sales.py', 'content': 'import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime\n\n# Set style for beautiful plots\nsns.set_style("whitegrid")\nplt.rcParams[\'figure.figsize\'] = (14, 10)\nplt.rcParams[\'font.size\'] = 11\n\n# Read the data\ndf = pd.read_csv(\'/root/data/sales_data.csv\')\ndf[\'Date\'] = pd.to_datetime(df[\'Date\'])\n\n# Analysis\nprint("=" * 60)\nprint("SALES DATA ANALYSIS REPORT")\nprint("=" * 60)\nprint("\\n1. DATA OVERVIEW")\nprint("-" * 60)\nprint(f"Date Range: {df[\'Date\'].min().strftime(\'%Y-%m-%d\')} to {df[\'Date\'].max().strftime(\'%Y-%m-%d\')}")\nprint(f"Total Records: {len(df)}")\nprint(f"Products: {\', \'.join(df[\'Product\'].unique())}")\n\nprint("\\n2. SUMMARY STATISTICS")\nprint("-" * 60)\nprint(f"Total Revenue: ${df[\'Revenue\'].sum():,.2f}")\nprint(f"Total Units Sold: {df[\'Units Sold\'].sum()}")\nprint(f"Average Daily Revenue: ${df[\'Revenue\'].mean():.2f}")\nprint(f"Average Units per Sale: {df[\'Units Sold\'].mean():.2f}")\n\nprint("\\n3. PRODUCT PERFORMANCE")\nprint("-" * 60)\nproduct_stats = df.groupby(\'Product\').agg({\n \'Revenue\': [\'sum\', \'mean\'],\n \'Units Sold\': [\'sum\', \'mean\']\n}).round(2)\nprint(product_stats)\n\nprint("\\n4. BEST PERFORMING DAY")\nprint("-" * 60)\nbest_day = df.loc[df[\'Revenue\'].idxmax()]\nprint(f"Date: {best_day[\'Date\'].strftime(\'%Y-%m-%d\')}")\nprint(f"Product: {best_day[\'Product\']}")\nprint(f"Revenue: ${best_day[\'Revenue\']:.2f}")\nprint(f"Units Sold: {best_day[\'Units Sold\']}")\n\n# Create visualizations\nfig = plt.figure(figsize=(16, 12))\nfig.suptitle(\'Sales Data Analysis Dashboard\', fontsize=20, fontweight=\'bold\', y=0.995)\n\n# 1. Revenue by Date\nax1 = plt.subplot(2, 3, 1)\ncolors = sns.color_palette("husl", len(df))\nbars = ax1.bar(df[\'Date\'].dt.strftime(\'%m-%d\'), df[\'Revenue\'], color=colors, edgecolor=\'black\', linewidth=1.5)\nax1.set_title(\'Daily Revenue\', fontsize=14, fontweight=\'bold\', pad=10)\nax1.set_xlabel(\'Date\', fontsize=12, fontweight=\'bold\')\nax1.set_ylabel(\'Revenue ($)\', fontsize=12, fontweight=\'bold\')\nax1.grid(axis=\'y\', alpha=0.3)\nfor bar in bars:\n height = bar.get_height()\n ax1.text(bar.get_x() + bar.get_width()/2., height,\n f\'${int(height)}\',\n ha=\'center\', va=\'bottom\', fontsize=9, fontweight=\'bold\')\nplt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)\n\n# 2. Units Sold by Date\nax2 = plt.subplot(2, 3, 2)\nbars = ax2.bar(df[\'Date\'].dt.strftime(\'%m-%d\'), df[\'Units Sold\'], color=colors, edgecolor=\'black\', linewidth=1.5)\nax2.set_title(\'Daily Units Sold\', fontsize=14, fontweight=\'bold\', pad=10)\nax2.set_xlabel(\'Date\', fontsize=12, fontweight=\'bold\')\nax2.set_ylabel(\'Units\', fontsize=12, fontweight=\'bold\')\nax2.grid(axis=\'y\', alpha=0.3)\nfor bar in bars:\n height = bar.get_height()\n ax2.text(bar.get_x() + bar.get_width()/2., height,\n f\'{int(height)}\',\n ha=\'center\', va=\'bottom\', fontsize=9, fontweight=\'bold\')\nplt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)\n\n# 3. Revenue by Product (Pie Chart)\nax3 = plt.subplot(2, 3, 3)\nproduct_revenue = df.groupby(\'Product\')[\'Revenue\'].sum()\ncolors_pie = sns.color_palette("Set2", len(product_revenue))\nwedges, texts, autotexts = ax3.pie(product_revenue, labels=product_revenue.index, autopct=\'%1.1f%%\',\n colors=colors_pie, startangle=90, textprops={\'fontsize\': 11, \'fontweight\': \'bold\'},\n explode=[0.05] * len(product_revenue))\nax3.set_title(\'Revenue Distribution by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nfor autotext in autotexts:\n autotext.set_color(\'white\')\n autotext.set_fontsize(12)\n\n# 4. Total Revenue by Product (Bar Chart)\nax4 = plt.subplot(2, 3, 4)\nproduct_revenue_sorted = product_revenue.sort_values(ascending=False)\ncolors_product = sns.color_palette("coolwarm", len(product_revenue_sorted))\nbars = ax4.barh(product_revenue_sorted.index, product_revenue_sorted.values, color=colors_product, edgecolor=\'black\', linewidth=1.5)\nax4.set_title(\'Total Revenue by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nax4.set_xlabel(\'Revenue ($)\', fontsize=12, fontweight=\'bold\')\nax4.set_ylabel(\'Product\', fontsize=12, fontweight=\'bold\')\nax4.grid(axis=\'x\', alpha=0.3)\nfor i, (product, revenue) in enumerate(product_revenue_sorted.items()):\n ax4.text(revenue, i, f\' ${int(revenue)}\', va=\'center\', fontsize=10, fontweight=\'bold\')\n\n# 5. Total Units Sold by Product\nax5 = plt.subplot(2, 3, 5)\nproduct_units = df.groupby(\'Product\')[\'Units Sold\'].sum().sort_values(ascending=False)\ncolors_units = sns.color_palette("viridis", len(product_units))\nbars = ax5.barh(product_units.index, product_units.values, color=colors_units, edgecolor=\'black\', linewidth=1.5)\nax5.set_title(\'Total Units Sold by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nax5.set_xlabel(\'Units\', fontsize=12, fontweight=\'bold\')\nax5.set_ylabel(\'Product\', fontsize=12, fontweight=\'bold\')\nax5.grid(axis=\'x\', alpha=0.3)\nfor i, (product, units) in enumerate(product_units.items()):\n ax5.text(units, i, f\' {int(units)}\', va=\'center\', fontsize=10, fontweight=\'bold\')\n\n# 6. Product Distribution\nax6 = plt.subplot(2, 3, 6)\nproduct_counts = df[\'Product\'].value_counts()\ncolors_count = sns.color_palette("muted", len(product_counts))\nwedges, texts, autotexts = ax6.pie(product_counts, labels=product_counts.index, autopct=\'%1.1f%%\',\n colors=colors_count, startangle=45, textprops={\'fontsize\': 11, \'fontweight\': \'bold\'},\n explode=[0.05] * len(product_counts))\nax6.set_title(\'Sales Transactions by Product\', fontsize=14, fontweight=\'bold\', pad=10)\nfor autotext in autotexts:\n autotext.set_color(\'white\')\n autotext.set_fontsize(12)\n\nplt.tight_layout()\nplt.savefig(\'/root/sales_analysis_plot.png\', dpi=300, bbox_inches=\'tight\', facecolor=\'white\')\nprint("\\n" + "=" * 60)\nprint("Plot saved as: sales_analysis_plot.png")\nprint("=" * 60)\n'}, 'name': 'write_file', 'type': 'tool_use'}] Tool Calls: write_file (toolu_01JcBjEXgPpCPuaGxn9ALwMg) Call ID: toolu_01JcBjEXgPpCPuaGxn9ALwMg Args: file_path: /root/analyze_sales.py content: import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime # Set style for beautiful plots sns.set_style("whitegrid") plt.rcParams['figure.figsize'] = (14, 10) plt.rcParams['font.size'] = 11 # Read the data df = pd.read_csv('/root/data/sales_data.csv') df['Date'] = pd.to_datetime(df['Date']) # Analysis print("=" * 60) print("SALES DATA ANALYSIS REPORT") print("=" * 60) print("\n1. DATA OVERVIEW") print("-" * 60) print(f"Date Range: {df['Date'].min().strftime('%Y-%m-%d')} to {df['Date'].max().strftime('%Y-%m-%d')}") print(f"Total Records: {len(df)}") print(f"Products: {', '.join(df['Product'].unique())}") print("\n2. SUMMARY STATISTICS") print("-" * 60) print(f"Total Revenue: ${df['Revenue'].sum():,.2f}") print(f"Total Units Sold: {df['Units Sold'].sum()}") print(f"Average Daily Revenue: ${df['Revenue'].mean():.2f}") print(f"Average Units per Sale: {df['Units Sold'].mean():.2f}") print("\n3. PRODUCT PERFORMANCE") print("-" * 60) product_stats = df.groupby('Product').agg({ 'Revenue': ['sum', 'mean'], 'Units Sold': ['sum', 'mean'] }).round(2) print(product_stats) print("\n4. BEST PERFORMING DAY") print("-" * 60) best_day = df.loc[df['Revenue'].idxmax()] print(f"Date: {best_day['Date'].strftime('%Y-%m-%d')}") print(f"Product: {best_day['Product']}") print(f"Revenue: ${best_day['Revenue']:.2f}") print(f"Units Sold: {best_day['Units Sold']}") # Create visualizations fig = plt.figure(figsize=(16, 12)) fig.suptitle('Sales Data Analysis Dashboard', fontsize=20, fontweight='bold', y=0.995) # 1. Revenue by Date ax1 = plt.subplot(2, 3, 1) colors = sns.color_palette("husl", len(df)) bars = ax1.bar(df['Date'].dt.strftime('%m-%d'), df['Revenue'], color=colors, edgecolor='black', linewidth=1.5) ax1.set_title('Daily Revenue', fontsize=14, fontweight='bold', pad=10) ax1.set_xlabel('Date', fontsize=12, fontweight='bold') ax1.set_ylabel('Revenue ($)', fontsize=12, fontweight='bold') ax1.grid(axis='y', alpha=0.3) for bar in bars: height = bar.get_height() ax1.text(bar.get_x() + bar.get_width()/2., height, f'${int(height)}', ha='center', va='bottom', fontsize=9, fontweight='bold') plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45) # 2. Units Sold by Date ax2 = plt.subplot(2, 3, 2) bars = ax2.bar(df['Date'].dt.strftime('%m-%d'), df['Units Sold'], color=colors, edgecolor='black', linewidth=1.5) ax2.set_title('Daily Units Sold', fontsize=14, fontweight='bold', pad=10) ax2.set_xlabel('Date', fontsize=12, fontweight='bold') ax2.set_ylabel('Units', fontsize=12, fontweight='bold') ax2.grid(axis='y', alpha=0.3) for bar in bars: height = bar.get_height() ax2.text(bar.get_x() + bar.get_width()/2., height, f'{int(height)}', ha='center', va='bottom', fontsize=9, fontweight='bold') plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45) # 3. Revenue by Product (Pie Chart) ax3 = plt.subplot(2, 3, 3) product_revenue = df.groupby('Product')['Revenue'].sum() colors_pie = sns.color_palette("Set2", len(product_revenue)) wedges, texts, autotexts = ax3.pie(product_revenue, labels=product_revenue.index, autopct='%1.1f%%', colors=colors_pie, startangle=90, textprops={'fontsize': 11, 'fontweight': 'bold'}, explode=[0.05] * len(product_revenue)) ax3.set_title('Revenue Distribution by Product', fontsize=14, fontweight='bold', pad=10) for autotext in autotexts: autotext.set_color('white') autotext.set_fontsize(12) # 4. Total Revenue by Product (Bar Chart) ax4 = plt.subplot(2, 3, 4) product_revenue_sorted = product_revenue.sort_values(ascending=False) colors_product = sns.color_palette("coolwarm", len(product_revenue_sorted)) bars = ax4.barh(product_revenue_sorted.index, product_revenue_sorted.values, color=colors_product, edgecolor='black', linewidth=1.5) ax4.set_title('Total Revenue by Product', fontsize=14, fontweight='bold', pad=10) ax4.set_xlabel('Revenue ($)', fontsize=12, fontweight='bold') ax4.set_ylabel('Product', fontsize=12, fontweight='bold') ax4.grid(axis='x', alpha=0.3) for i, (product, revenue) in enumerate(product_revenue_sorted.items()): ax4.text(revenue, i, f' ${int(revenue)}', va='center', fontsize=10, fontweight='bold') # 5. Total Units Sold by Product ax5 = plt.subplot(2, 3, 5) product_units = df.groupby('Product')['Units Sold'].sum().sort_values(ascending=False) colors_units = sns.color_palette("viridis", len(product_units)) bars = ax5.barh(product_units.index, product_units.values, color=colors_units, edgecolor='black', linewidth=1.5) ax5.set_title('Total Units Sold by Product', fontsize=14, fontweight='bold', pad=10) ax5.set_xlabel('Units', fontsize=12, fontweight='bold') ax5.set_ylabel('Product', fontsize=12, fontweight='bold') ax5.grid(axis='x', alpha=0.3) for i, (product, units) in enumerate(product_units.items()): ax5.text(units, i, f' {int(units)}', va='center', fontsize=10, fontweight='bold') # 6. Product Distribution ax6 = plt.subplot(2, 3, 6) product_counts = df['Product'].value_counts() colors_count = sns.color_palette("muted", len(product_counts)) wedges, texts, autotexts = ax6.pie(product_counts, labels=product_counts.index, autopct='%1.1f%%', colors=colors_count, startangle=45, textprops={'fontsize': 11, 'fontweight': 'bold'}, explode=[0.05] * len(product_counts)) ax6.set_title('Sales Transactions by Product', fontsize=14, fontweight='bold', pad=10) for autotext in autotexts: autotext.set_color('white') autotext.set_fontsize(12) plt.tight_layout() plt.savefig('/root/sales_analysis_plot.png', dpi=300, bbox_inches='tight', facecolor='white') print("\n" + "=" * 60) print("Plot saved as: sales_analysis_plot.png") print("=" * 60) ================================= Tool Message ================================= Name: write_file Updated file /root/analyze_sales.py ================================== Ai Message ================================== [{'text': 'Now let me run the analysis script:', 'type': 'text'}, {'id': 'toolu_015xkZr21TuzCTEKGzSqgnbp', 'input': {'command': 'cd /root && python analyze_sales.py'}, 'name': 'execute', 'type': 'tool_use'}] Tool Calls: execute (toolu_015xkZr21TuzCTEKGzSqgnbp) Call ID: toolu_015xkZr21TuzCTEKGzSqgnbp Args: command: cd /root && python analyze_sales.py ================================= Tool Message ================================= Name: execute ============================================================ SALES DATA ANALYSIS REPORT ============================================================ 1. DATA OVERVIEW ------------------------------------------------------------ Date Range: 2025-08-01 to 2025-08-05 Total Records: 5 Products: Widget A, Widget B, Widget C 2. SUMMARY STATISTICS ------------------------------------------------------------ Total Revenue: $840.00 Total Units Sold: 33 Average Daily Revenue: $168.00 Average Units per Sale: 6.60 3. PRODUCT PERFORMANCE ------------------------------------------------------------ Revenue Units Sold sum mean sum mean Product Widget A 425 212.5 17 8.5 Widget B 325 162.5 13 6.5 Widget C 90 90.0 3 3.0 4. BEST PERFORMING DAY ------------------------------------------------------------ Date: 2025-08-01 Product: Widget A Revenue: $250.00 Units Sold: 10 ============================================================ Plot saved as: sales_analysis_plot.png ============================================================ [Command succeeded with exit code 0] ================================== Ai Message ================================== [{'text': 'Perfect! The analysis is complete and the plot has been generated. Now let me send both the analysis and the plot to Slack:', 'type': 'text'}, {'id': 'toolu_01Qust5a8DECKPZuU9pBBvkz', 'input': {'text': '📊 **SALES DATA ANALYSIS REPORT** 📊\n\n**1. DATA OVERVIEW**\n• Date Range: August 1-5, 2025 (5 days)\n• Products Analyzed: Widget A, Widget B, Widget C\n\n**2. KEY METRICS**\n💰 Total Revenue: $840.00\n📦 Total Units Sold: 33 units\n📈 Average Daily Revenue: $168.00\n📊 Average Units per Sale: 6.60\n\n**3. PRODUCT PERFORMANCE**\n🏆 Widget A (Top Performer)\n - Total Revenue: $425 (50.6% of total)\n - Total Units Sold: 17 (avg 8.5 per transaction)\n\n🥈 Widget B\n - Total Revenue: $325 (38.7% of total)\n - Total Units Sold: 13 (avg 6.5 per transaction)\n\n🥉 Widget C\n - Total Revenue: $90 (10.7% of total)\n - Total Units Sold: 3 (avg 3.0 per transaction)\n\n**4. BEST PERFORMING DAY**\n🌟 August 1, 2025\n - Product: Widget A\n - Revenue: $250.00\n - Units Sold: 10\n\n**KEY INSIGHTS:**\n✅ Widget A is the clear leader, generating over 50% of total revenue\n✅ Widget B shows strong performance with consistent sales\n⚠️ Widget C has potential for improvement - lowest sales and revenue\n📊 Daily revenue ranges from $90 to $250\n\nSee attached visualization dashboard for detailed charts! 📈', 'file_path': '/root/sales_analysis_plot.png'}, 'name': 'slack_send_message', 'type': 'tool_use'}] Tool Calls: slack_send_message (toolu_01Qust5a8DECKPZuU9pBBvkz) Call ID: toolu_01Qust5a8DECKPZuU9pBBvkz Args: text: 📊 **SALES DATA ANALYSIS REPORT** 📊 **1. DATA OVERVIEW** • Date Range: August 1-5, 2025 (5 days) • Products Analyzed: Widget A, Widget B, Widget C **2. KEY METRICS** 💰 Total Revenue: $840.00 📦 Total Units Sold: 33 units 📈 Average Daily Revenue: $168.00 📊 Average Units per Sale: 6.60 **3. PRODUCT PERFORMANCE** 🏆 Widget A (Top Performer) - Total Revenue: $425 (50.6% of total) - Total Units Sold: 17 (avg 8.5 per transaction) 🥈 Widget B - Total Revenue: $325 (38.7% of total) - Total Units Sold: 13 (avg 6.5 per transaction) 🥉 Widget C - Total Revenue: $90 (10.7% of total) - Total Units Sold: 3 (avg 3.0 per transaction) **4. BEST PERFORMING DAY** 🌟 August 1, 2025 - Product: Widget A - Revenue: $250.00 - Units Sold: 10 **KEY INSIGHTS:** ✅ Widget A is the clear leader, generating over 50% of total revenue ✅ Widget B shows strong performance with consistent sales ⚠️ Widget C has potential for improvement - lowest sales and revenue 📊 Daily revenue ranges from $90 to $250 See attached visualization dashboard for detailed charts! 📈 file_path: /root/sales_analysis_plot.png ================================= Tool Message ================================= Name: slack_send_message Message sent. ================================== Ai Message ================================== Excellent! I've successfully completed the analysis and sent everything to Slack. Here's what I did: ## Summary **Analysis Completed:** - Analyzed the sales data from August 1-5, 2025 - Processed data for 3 products (Widget A, B, and C) - Generated comprehensive statistics and insights **Key Findings:** 1. **Total Revenue:** $840 across 5 days 2. **Best Product:** Widget A ($425 revenue, 50.6% of total) 3. **Best Day:** August 1st with $250 in revenue 4. **Total Units Sold:** 33 units **Visualization Created:** A beautiful 6-panel dashboard featuring: - Daily revenue trends - Daily units sold - Revenue distribution by product (pie chart) - Total revenue by product (horizontal bar chart) - Total units sold by product (horizontal bar chart) - Sales transactions distribution (pie chart) **Delivered to Slack:** ✅ Comprehensive analysis report with key metrics and insights ✅ High-resolution visualization dashboard (saved as PNG) The analysis reveals that Widget A is the top performer, while Widget C shows room for improvement. All results have been sent to your Slack workspace! ``` 查看此执行的完整[LangSmith 跟踪](https://smith.langchain.com/public/ac2443a8-16ad-4e00-8f02-afde6a737534/r)。 ## 结果 智能体成功分析数据，并与 Slack 分享了一个包含可视化的综合报告： ![Slack中的销售分析结果](https://mintlify.s3.us-west-1.amazonaws.com/langchain-docs-zh/images/data_analysis_slack_response.png) Agent-generated analysis report and visualization dashboard delivered to Slack > [!TIP] > > 您可以直接从后端下载工件，而无需使用外部工具： > > ```python theme={null} > backend.download_files(list_of_filepaths) > ``` > [!NOTE] > > 查看[提供者指南](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/sandboxes.md#可用提供者)，了解如何在完成后清理沙盒。 ## 下一步 现在您已经构建了一个数据分析智能体，探索这些资源以扩展其功能： * [托管深度智能体](https://docs.langchain.com/langsmith/managed-deep-agents-overview)：使用托管部署基础设施构建深度智能体 * [后端](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/backends.md)：了解深度智能体后端系统 * [沙盒](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/sandboxes.md)：回顾沙盒代码执行的后端，包括安全考虑和高级配置 * [定制](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/customization.md)：发现如何使用不同的模型、工具、提示词和可选的[任务规划](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/overview.md#任务规划)来自定义您的智能体 * [代码](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/code/overview.md)：尝试 Deep Agents Code 作为终端编码智能体，以在本地协助数据分析和其他智能体任务 * [技能](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/skills.md)：为您的智能体配备可重用的技能，用于常见工作流 * [人机交互](https://langchain-docs-zh.mintlify.site/langchain/oss/deepagents/human-in-the-loop.md)：为您的数据分析工作流中的关键操作添加交互式审批步骤 *** > 官方原文：[src/oss/deepagents/data-analysis.mdx](https://github.com/langchain-ai/docs/blob/main/src/oss/deepagents/data-analysis.mdx) · 非官方翻译，译文由 AI 生成，以英文原文为准。 This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.