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加载器教程

许多现有系统和平台都支持从 CSV 文件加载数据。许多用户更喜欢使用电子表格软件和多表文件格式（包括 XLSX）。SheetJS 库通过将复杂的工作簿转换为简单的 CSV 数据来帮助弥补差距。

¥Many existing systems and platforms include support for loading data from CSV files. Many users prefer to work in spreadsheet software and multi-sheet file formats including XLSX. SheetJS libraries help bridge the gap by translating complex workbooks to simple CSV data.

此示例的目标是将电子表格数据加载到向量存储中，并使用大型语言模型根据英语输入生成查询。现有工具支持 CSV，但不支持真正的电子表格。

¥The goal of this example is to load spreadsheet data into a vector store and use a large language model to generate queries based on English language input. The existing tooling supports CSV but does not support real spreadsheets.

在 "SheetJS 转换" 中，我们将使用 SheetJS 库为 LangChain CSV 加载器生成 CSV 文件。这些转换可以在预处理步骤中运行，而不会破坏现有的 CSV 工作流程。

¥In "SheetJS Conversion", we will use SheetJS libraries to generate CSV files for the LangChain CSV loader. These conversions can be run in a preprocessing step without disrupting existing CSV workflows.

在 "SheetJS 加载器" 中，我们将在自定义 LoadOfSheet 数据加载器中使用 SheetJS 库直接生成文档和元数据。

¥In "SheetJS Loader", we will use SheetJS libraries in a custom LoadOfSheet data loader to directly generate documents and metadata.

"SheetJS 加载器演示" 是一个完整的演示，它使用 SheetJS Loader 根据 XLS 工作簿中的数据回答问题。

¥"SheetJS Loader Demo" is a complete demo that uses the SheetJS Loader to answer questions based on data from a XLS workbook.

测试部署

此演示在以下配置中进行了测试：

¥This demo was tested in the following configurations:

日期平台
2024-08-31NVIDIA RTX 4090（24 GB VRAM）+ i9-10910（128 GB RAM）
2024-08-09NVIDIA RTX 4080 SUPER（16 GB VRAM）+ i9-10910（128 GB RAM）
2024-09-21AMD RX 7900 XTX（24 GB VRAM）+ Ryzen Z1 Extreme（16 GB RAM）
2024-07-15Apple M2 Max 12 核 CPU + 30 核 GPU（32 GB 统一内存）

SheetJS 用户已在其他配置中验证了此演示：

¥SheetJS users have verified this demo in other configurations:

Other tested configurations (click to show)
DemoPlatform
LangChainJSNVIDIA RTX 4070 Ti (12 GB VRAM) + Ryzen 7 5800x (64 GB RAM)
LangChainJSNVIDIA RTX 4060 (8 GB VRAM) + Ryzen 7 5700g (32 GB RAM)
LangChainJSNVIDIA RTX 3090 (24 GB VRAM) + Ryzen 9 3900XT (128 GB RAM)
LangChainJSNVIDIA RTX 3080 (12 GB VRAM) + Ryzen 7 5800X (32 GB RAM)
LangChainJSNVIDIA RTX 3070 (8 GB VRAM) + Ryzen Z1 Extreme (16 GB RAM)
LangChainJSNVIDIA RTX 3060 (12 GB VRAM) + i5-11400 (32 GB RAM)
LangChainJSNVIDIA RTX 2080 (12 GB VRAM) + i7-9700K (16 GB RAM)
LangChainJSNVIDIA RTX 2060 (6 GB VRAM) + Ryzen 5 3600 (32 GB RAM)
LangChainJSNVIDIA GTX 1080 (8 GB VRAM) + Ryzen 7 5800x (64 GB RAM)
LangChainJSNVIDIA GTX 1070 (8 GB VRAM) + Ryzen 7 7700x (32 GB RAM)
LangChainJSAMD RX 6800 XT (16 GB VRAM) + Ryzen Z1 Extreme (16 GB RAM)

特别感谢：

¥Special thanks to:

CSV 加载器​

¥CSV Loader

此说明已针对 LangChain 0.2 进行了验证。

¥This explanation was verified against LangChain 0.2.

文档加载器从数据源生成数据对象（"documents"）和相关元数据。

¥Document loaders generate data objects ("documents") and associated metadata from data sources.

LangChain 提供 CSVLoader[^1] 组件，用于从文件加载 CSV 数据：

¥LangChain offers a CSVLoader[^1] component for loading CSV data from a file:

Generating Documents from a CSV file
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";

const loader = new CSVLoader("pres.csv");
const docs = await loader.load();

console.log(docs);

CSV 加载器使用第一行确定列标题并为每个数据行生成一个文档。例如，以下 CSV 保存总统数据：

¥The CSV loader uses the first row to determine column headers and generates one document per data row. For example, the following CSV holds Presidential data:

Name,Index
Bill Clinton,42
GeorgeW Bush,43
Barack Obama,44
Donald Trump,45
Joseph Biden,46

每个数据行都转换为一个文档，其内容是属性和值的列表。例如，第三个数据行如下所示：

¥Each data row is translated to a document whose content is a list of attributes and values. For example, the third data row is shown below:

CSV RowDocument Content
Name,Index
Barack Obama,44
Name: Barack Obama
Index: 44

LangChain CSV 加载器将在文档中包含源元数据：

¥The LangChain CSV loader will include source metadata in the document:

Document generated by the CSV loader
Document {
  pageContent: 'Name: Barack Obama\nIndex: 44',
  metadata: { source: 'pres.csv', line: 3 }
}

SheetJS 转换​

¥SheetJS Conversion

SheetJS NodeJS 模块 可以在使用 LangChain 和其他 JavaScript 库的 NodeJS 脚本中导入。

¥The SheetJS NodeJS module can be imported in NodeJS scripts that use LangChain and other JavaScript libraries.

一个简单的预处理步骤可以将工作簿转换为可以由现有 CSV 工具处理的 CSV 文件：

¥A simple pre-processing step can convert workbooks to CSV files that can be processed by the existing CSV tooling:

从文件系统解析文件

¥Parsing files from the filesystem

SheetJS readFile 方法 [^2] 可以读取工作簿文件。该方法接受路径并返回符合 SheetJS 数据模型 [^3] 的工作簿对象。

¥The SheetJS readFile method[^2] can read workbook files. The method accepts a path and returns a workbook object that conforms to the SheetJS data model[^3].

/* Load SheetJS Libraries */
import { readFile, set_fs } from 'xlsx';

/* Load 'fs' for readFile support */
import * as fs from 'fs';
set_fs(fs);

/* Parse `pres.xlsx` */
const wb = readFile("pres.xlsx");

检查 SheetJS 工作簿和工作表对象

¥Inspecting SheetJS workbook and worksheet objects

Workbook 对象表示多工作表工作簿文件。它们存储单个工作表对象和其他元数据。

¥Workbook objects represent multi-sheet workbook files. They store individual worksheet objects and other metadata.

与此讨论相关，工作簿对象使用以下键 [^7]：

¥Relevant to this discussion, the workbook object uses the following keys[^7]:

  • SheetNames 是一个工作表名称数组

    ¥SheetNames is an array of worksheet names

  • Sheets 是一个对象，其键是工作表名称，其值是工作表对象。

    ¥Sheets is an object whose keys are sheet names and whose values are sheet objects.

SheetNames[0] 是第一个工作表名称，因此以下代码片段将从工作簿中提取第一个工作表：

¥SheetNames[0] is the first worksheet name, so the following snippet will pull the first worksheet from the workbook:

const first_ws = wb.Sheets[wb.SheetNames[0]];

将 SheetJS 工作表导出到 CSV

¥Exporting SheetJS worksheets to CSV

可以使用 SheetJS sheet_to_csv[^4] 方法将工作簿中的每个工作表写入 CSV 文本。该方法接受 SheetJS 工作表对象并返回字符串。

¥Each worksheet in the workbook can be written to CSV text using the SheetJS sheet_to_csv[^4] method. The method accepts a SheetJS worksheet object and returns a string.

const csv = utils.sheet_to_csv(first_ws);

完成脚本

¥Complete Script

例如，以下 NodeJS 脚本读取 pres.xlsx 并显示第一个工作表中的 CSV 行：

¥For example, the following NodeJS script reads pres.xlsx and displays CSV rows from the first worksheet:

Print CSV data from the first worksheet
/* Load SheetJS Libraries */
import { readFile, set_fs, utils } from 'xlsx';

/* Load 'fs' for readFile support */
import * as fs from 'fs';
set_fs(fs);

/* Parse `pres.xlsx` */
const wb = readFile("pres.xlsx");

/* Print CSV rows from first worksheet */
const first_ws = wb.Sheets[wb.SheetNames[0]];
const csv = utils.sheet_to_csv(first_ws);
console.log(csv);

许多演示涵盖了精神上相似的工作流程：

¥A number of demos cover spiritually similar workflows:

  • Stata、MATLAB 和 Maple 支持 XLSX 数据导入。SheetJS 集成从用户提供的电子表格生成干净的 XLSX 工作簿。

    ¥Stata, MATLAB and Maple support XLSX data import. The SheetJS integrations generate clean XLSX workbooks from user-supplied spreadsheets.

  • TensorFlow.js、熊猫 和 Mathematica 支持 CSV 数据导入。SheetJS 集成生成干净的 CSV 并使用内置的 CSV 处理器。

    ¥TensorFlow.js, Pandas and Mathematica support CSV data import. The SheetJS integrations generate clean CSVs and use built-in CSV processors.

  • "命令行工具" 演示涵盖了制作用于文件转换的独立命令行工具的技术。

    ¥The "Command-Line Tools" demo covers techniques for making standalone command-line tools for file conversion.

单一工作表​

¥Single Worksheet

对于单个工作表，SheetJS 预处理步骤可以将 CSV 行写入文件，CSVLoader 可以加载新写入的文件。

¥For a single worksheet, a SheetJS pre-processing step can write the CSV rows to file and the CSVLoader can load the newly written file.

Code example (click to hide)
Pulling data from the first worksheet of a workbook
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";
import { readFile, set_fs, utils } from 'xlsx';

/* Load 'fs' for readFile support */
import * as fs from 'fs';
set_fs(fs);

/* Parse `pres.xlsx`` */
const wb = readFile("pres.xlsx");

/* Generate CSV and write to `pres.xlsx.csv` */
const first_ws = wb.Sheets[wb.SheetNames[0]];
const csv = utils.sheet_to_csv(first_ws);
fs.writeFileSync("pres.xlsx.csv", csv);

/* Create documents with CSVLoader */
const loader = new CSVLoader("pres.xlsx.csv");
const docs = await loader.load();

console.log(docs);
// ...

工作簿​

¥Workbook

工作簿是工作表的集合。每个工作表都可以导出到单独的 CSV。如果 CSV 写入子文件夹，DirectoryLoader[^5] 可以一步处理文件。

¥A workbook is a collection of worksheets. Each worksheet can be exported to a separate CSV. If the CSVs are written to a subfolder, a DirectoryLoader[^5] can process the files in one step.

Code example (click to hide)

在此示例中，脚本创建了一个名为 csv 的子文件夹。将处理工作簿中的每个工作表，并将生成的 CSV 存储到编号文件中。第一个工作表将存储到 csv/0.csv。

¥In this example, the script creates a subfolder named csv. Each worksheet in the workbook will be processed and the generated CSV will be stored to numbered files. The first worksheet will be stored to csv/0.csv.

Pulling data from the each worksheet of a workbook
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";
import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import { readFile, set_fs, utils } from 'xlsx';

/* Load 'fs' for readFile support */
import * as fs from 'fs';
set_fs(fs);

/* Parse `pres.xlsx`` */
const wb = readFile("pres.xlsx");

/* Create a folder `csv` */
try { fs.mkdirSync("csv"); } catch(e) {}

/* Generate CSV data for each worksheet */
wb.SheetNames.forEach((name, idx) => {
  const ws = wb.Sheets[name];
  const csv = utils.sheet_to_csv(ws);
  fs.writeFileSync(`csv/${idx}.csv`, csv);
});

/* Create documents with DirectoryLoader */
const loader = new DirectoryLoader("csv", {
  ".csv": (path) => new CSVLoader(path)
});
const docs = await loader.load();

console.log(docs);
// ...

SheetJS 加载器​

¥SheetJS Loader

LangChain 附带的 CSVLoader 不添加任何文档元数据，也不生成任何属性。自定义加载器可以绕过 CSV 工具中的限制，并可能包含没有 CSV 等效项的元数据。

¥The CSVLoader that ships with LangChain does not add any Document metadata and does not generate any attributes. A custom loader can work around limitations in the CSV tooling and potentially include metadata that has no CSV equivalent.

演示 LoadOfSheet 加载器 将在所有工作表中为每个数据行生成一个文档。它还将尝试构建元数据和属性以用于自查询检索器。

¥The demo LoadOfSheet loader will generate one Document per data row across all worksheets. It will also attempt to build metadata and attributes for use in self-querying retrievers.

Sample usage
/* read and parse `data.xlsb` */
const loader = new LoadOfSheet("./data.xlsb");

/* generate documents */
const docs = await loader.load();

/* synthesized attributes for the SelfQueryRetriever */
const attributes = loader.attributes;
Sample SheetJS Loader (click to show)

This example loader pulls data from each worksheet. It assumes each worksheet includes one header row and a number of data rows.

loadofsheet.mjs
import { Document } from "@langchain/core/documents";
import { BufferLoader } from "langchain/document_loaders/fs/buffer";
import { read, utils } from "xlsx";

/**

 * Document loader that uses SheetJS to load documents.

 *  * Each worksheet is parsed into an array of row objects using the SheetJS

 * `sheet_to_json` method and projected to a `Document`. Metadata includes

 * original sheet name, row data, and row index
 */
export default class LoadOfSheet extends BufferLoader {
  /** @type {import("langchain/chains/query_constructor").AttributeInfo[]}  */
  attributes = [];

  /**

   * Document loader that uses SheetJS to load documents.

   *    * @param {string|Blob} filePathOrBlob Source Data
   */
  constructor(filePathOrBlob) {
    super(filePathOrBlob);
    this.attributes = [];
  }

  /**

   * Parse document

   *    * NOTE: column labels in multiple sheets are not disambiguated!

   *    * @param {Buffer} raw Raw data Buffer

   * @param {Document["metadata"]} metadata Document metadata

   * @returns {Promise<Document[]>} Array of Documents
   */
  async parse(raw, metadata) {
    /** @type {Document[]} */
    const result = [];

    this.attributes = [
      { name: "worksheet", description: "Sheet or Worksheet Name", type: "string" },
      { name: "rowNum", description: "Row index", type: "number" }
    ];

    const wb = read(raw, {type: "buffer", WTF:1});
    for(let name of wb.SheetNames) {
      const fields = {};
      const ws = wb.Sheets[name];
      if(!ws) return;

      const aoo = utils.sheet_to_json(ws);
      aoo.forEach((row, idx) => {
        result.push({
          pageContent: "Row " + (idx + 1) + " has the following content: \n" + Object.entries(row).map(kv => `- ${kv[0]}: ${kv[1]}`).join("\n") + "\n",
          metadata: {
            worksheet: name,
            rowNum: row["__rowNum__"],
            ...metadata,
            ...row
          }
        });
        Object.entries(row).forEach(([k,v]) => { if(v != null) (fields[k] || (fields[k] = {}))[v instanceof Date ? "date" : typeof v] = true } );
      });
      Object.entries(fields).forEach(([k,v]) => this.attributes.push({
        name: k, description: k, type: Object.keys(v).join(" or ")
      }));
    }

    return result;
  }
};

从文本到二进制​

¥From Text to Binary

许多库和平台提供通用 "text" 加载器，这些加载器假定采用 UTF8 编码来处理文件。这会破坏许多电子表格格式，包括 XLSX、XLSB、XLSM 和 XLS。

¥Many libraries and platforms offer generic "text" loaders that process files assuming the UTF8 encoding. This corrupts many spreadsheet formats including XLSX, XLSB, XLSM and XLS.

此问题影响许多 JavaScript 工具。各种演示涵盖解决方法：

¥This issue affects many JavaScript tools. Various demos cover workarounds:

CSVLoader 扩展了一个特殊的 TextLoader，强制进行 UTF8 文本解析。

¥The CSVLoader extends a special TextLoader that forces UTF8 text parsing.

PDF 加载器使用一个单独的 BufferLoader 类，它使用 NodeJS Buffer 对象传递原始数据。

¥There is a separate BufferLoader class, used by the PDF loader, that passes the raw data using NodeJS Buffer objects.

BinaryText
pdf.ts (structure)
export class PDFLoader extends BufferLoader {
  // ...
  public async parse(
    raw: Buffer,
    metadata: Document["metadata"]
  ): Promise<Document[]> {
    // ...
  }
  // ...
}
csv.ts (structure)
export class CSVLoader extends TextLoader {
  // ...
  protected async parse(
    raw: string

  ): Promise<string[]> {
    // ...
  }
  // ...
}

NodeJS 缓冲区​

¥NodeJS Buffers

SheetJS read 方法直接支持 NodeJS Buffer 对象 [^6]：

¥The SheetJS read method supports NodeJS Buffer objects directly[^6]:

Parsing a workbook in a BufferLoader
import { BufferLoader } from "langchain/document_loaders/fs/buffer";
import { read, utils } from "xlsx";

export default class LoadOfSheet extends BufferLoader {
  // ...
  async parse(raw, metadata) {
    const wb = read(raw, {type: "buffer"});
    // At this point, `wb` is a SheetJS workbook object
    // ...
  }
}

read 方法返回 SheetJS 工作簿对象 [^7]。

¥The read method returns a SheetJS workbook object[^7].

生成内容​

¥Generating Content

SheetJS sheet_to_json 方法 [^8] 返回一个数据对象数组，其键从工作表的第一行中提取。

¥The SheetJS sheet_to_json method[^8] returns an array of data objects whose keys are drawn from the first row of the worksheet.

SpreadsheetArray of Objects

pres.xlsx data

[
  { Name: "Bill Clinton", Index: 42 },
  { Name: "GeorgeW Bush", Index: 43 },
  { Name: "Barack Obama", Index: 44 },
  { Name: "Donald Trump", Index: 45 },
  { Name: "Joseph Biden", Index: 46 }
]

原始 CSVLoader 为每个键值对写入一行。可以通过循环数据行对象的键和值来生成此文本。Object.entries 辅助函数简化了转换：

¥The original CSVLoader wrote one row for each key-value pair. This text can be generated by looping over the keys and values of the data row object. The Object.entries helper function simplifies the conversion:

function make_csvloader_doc_from_row_object(row) {
  return Object.entries(row).map(([k,v]) => `${k}: ${v}`).join("\n");
}

生成文档​

¥Generating Documents

加载器必须为工作簿中的每个工作表生成行对象。

¥The loader must generate row objects for each worksheet in the workbook.

在 SheetJS 数据模型中，工作簿对象有两个相关字段：

¥In the SheetJS data model, the workbook object has two relevant fields:

  • SheetNames 是一个工作表名称数组

    ¥SheetNames is an array of sheet names

  • Sheets 是一个对象，其键是工作表名称，其值是工作表对象。

    ¥Sheets is an object whose keys are sheet names and values are sheet objects.

for..of 循环可以遍历工作表：

¥A for..of loop can iterate across the worksheets:

Looping over a workbook (skeleton)
    const wb = read(raw, {type: "buffer", WTF:1});
    for(let name of wb.SheetNames) {
      const ws = wb.Sheets[name];
      const aoa = utils.sheet_to_json(ws);
      // at this point, `aoa` is an array of objects
    }

此简化的 parse 函数使用上一节中的代码片段：

¥This simplified parse function uses the snippet from the previous section:

BufferLoader parse function (skeleton)
  async parse(raw, metadata) {
    /* array to hold generated documents */
    const result = [];

    /* read workbook */
    const wb = read(raw, {type: "buffer", WTF:1});

    /* loop over worksheets */
    for(let name of wb.SheetNames) {
      const ws = wb.Sheets[name];
      const aoa = utils.sheet_to_json(ws);

      /* loop over data rows */
      aoa.forEach((row, idx) => {
        /* generate a new document and add to the result array */
        result.push({
          pageContent: Object.entries(row).map(([k,v]) => `${k}: ${v}`).join("\n")
        });
      });
    }

    return result;
  }

元数据和属性​

¥Metadata and Attributes

强烈建议为自查询检索应用生成额外的元数据和属性。

¥It is strongly recommended to generate additional metadata and attributes for self-query retrieval applications.

Implementation Details (click to show)

Metadata

Metadata is attached to each document object. The following example appends the raw row data to the document metadata:

Document with metadata (snippet)
        /* generate a new document and add to the result array */
        result.push({
          pageContent: Object.entries(row).map(([k,v]) => `${k}: ${v}`).join("\n"),
          metadata: {
            worksheet: name, // name of the worksheet
            rowNum: idx, // data row index
            ...row // raw row data
          }
        });

Attributes

Each attribute object specifies three properties:

  • name corresponds to the field in the document metadata
  • description is a description of the field
  • type is a description of the data type.

While looping through data rows, a simple type check can keep track of the data type for each column:

Tracking column types (sketch)
    for(let name of wb.SheetNames) {
      /* track column types */
      const fields = {};
      // ...

      aoo.forEach((row, idx) => {
        result.push({/* ... */});
        /* Check each property */
        Object.entries(row).forEach(([k,v]) => {
          /* Update fields entry to reflect the new data point */
          if(v != null) (fields[k] || (fields[k] = {}))[v instanceof Date ? "date" : typeof v] = true
        });
      });
      // ...
    }

Attributes can be generated after writing the worksheet data. Storing attributes in a loader property will make it accessible to scripts that use the loader.

Adding Attributes to a Loader (sketch)
export default class LoadOfSheet extends BufferLoader {
  attributes = [];
  // ...

  async parse(raw, metadata) {
    // Add the worksheet name and row index attributes
    this.attributes = [
      { name: "worksheet", description: "Sheet or Worksheet Name", type: "string" },
      { name: "rowNum", description: "Row index", type: "number" }
    ];
    const wb = read(raw, {type: "buffer", WTF:1});
    for(let name of wb.SheetNames) {
      const fields = {};
      // ...
      const aoo = utils.sheet_to_json(ws);
      aoo.forEach((row, idx) => {
        result.push({/* ... */});
        /* Check each property */
        Object.entries(row).forEach(([k,v]) => {
          /* Update fields entry to reflect the new data point */
          if(v != null) (fields[k] || (fields[k] = {}))[v instanceof Date ? "date" : typeof v] = true
        });
      });
      /* Add one attribute per metadata field */
      Object.entries(fields).forEach(([k,v]) => this.attributes.push({
        name: k, description: k,
        /* { number: true, string: true } -> "number or string" */
        type: Object.keys(v).join(" or ")
      }));
    }
    // ...
  }

SheetJS 加载器演示​

¥SheetJS Loader Demo

演示针对 示例汽车数据集 执行查询 "哪些行每加仑行驶里程超过 40 英里？" 并显示结果。

¥The demo performs the query "Which rows have over 40 miles per gallon?" against a sample cars dataset and displays the results.

SheetJS 团队成员已使用 PowerShell 和 Ollama for Windows 在 Windows 10 和 Windows 11 上测试了此演示。

¥SheetJS team members have tested this demo on Windows 10 and Windows 11 using PowerShell and Ollama for Windows.

SheetJS 用户还在 Windows Subsystem for Linux 中测试了此演示。

¥SheetJS users have also tested this demo within Windows Subsystem for Linux.

此演示使用 Ollama 中的 ChatQA-1.5 model[^9] 进行了测试。

¥This demo was tested using the ChatQA-1.5 model[^9] in Ollama.

测试模型使用了高达 9.2GB 的 VRAM。强烈建议在较新的 Apple Silicon Mac 或配备 Nvidia GPU（至少 12GB VRAM）的 PC 上运行演示。SheetJS 用户已在 VRAM 低至 6GB 的系统上测试了此演示。

¥The tested model used up to 9.2GB VRAM. It is strongly recommended to run the demo on a newer Apple Silicon Mac or a PC with an Nvidia GPU with at least 12GB VRAM. SheetJS users have tested the demo on systems with as little as 6GB VRAM.

  1. 安装先决条件：

    ¥Install pre-requisites:

Ollama 应安装在与 NodeJS 相同的平台上。如果在 WSL 中运行 NodeJS，则还应在 WSL 中安装 Ollama。

¥Ollama should be installed on the same platform as NodeJS. If NodeJS is run within WSL, Ollama should also be installed within WSL.

安装依赖后，启动新的终端会话。

¥After installing dependencies, start a new terminal session.

  1. 创建一个新项目：

    ¥Create a new project:

mkdir sheetjs-loader
cd sheetjs-loader
npm init -y

  1. 下载演示脚本：

    ¥Download the demo scripts:

curl -LO https://xlsx.nodejs.cn/loadofsheet/query.mjs
curl -LO https://xlsx.nodejs.cn/loadofsheet/loadofsheet.mjs

在 PowerShell 中，命令可能会因参数错误而失败：

¥In PowerShell, the command may fail with a parameter error:

Invoke-WebRequest : A parameter cannot be found that matches parameter name 'LO'.

curl.exe 必须直接调用：

¥curl.exe must be invoked directly:

curl.exe -LO https://xlsx.nodejs.cn/loadofsheet/query.mjs
curl.exe -LO https://xlsx.nodejs.cn/loadofsheet/loadofsheet.mjs

  1. 安装 SheetJS NodeJS 模块：

    ¥Install the SheetJS NodeJS module:

npm i --save https://sheet.lol/balls/xlsx-0.20.3.tgz

  1. 安装依赖：

    ¥Install dependencies:

npm i --save @langchain/community@0.2.18 @langchain/core@0.2.15 langchain@0.2.9 peggy@4.0.3 --force

上次测试此演示时，出现与依赖和对等依赖版本相关的错误消息。--force 标志是必需的。

¥When this demo was last tested, there were error messages relating to dependency and peer dependency versions. The --force flag was required.

  1. 下载 汽车数据集：

    ¥Download the cars dataset:

curl -LO https://xlsx.nodejs.cn/cd.xls

在 PowerShell 中，命令可能会因参数错误而失败：

¥In PowerShell, the command may fail with a parameter error:

Invoke-WebRequest : A parameter cannot be found that matches parameter name 'LO'.

curl.exe 必须直接调用：

¥curl.exe must be invoked directly:

curl.exe -LO https://xlsx.nodejs.cn/cd.xls

  1. 使用 Ollama 安装 llama3-chatqa:8b-v1.5-q8_0 模型：

    ¥Install the llama3-chatqa:8b-v1.5-q8_0 model using Ollama:

ollama pull llama3-chatqa:8b-v1.5-q8_0

  1. 运行演示脚本

    ¥Run the demo script

node query.mjs

演示执行查询 "哪些行每加仑行驶里程超过 40 英里？"。它将打印以下九个结果：

¥The demo performs the query "Which rows have over 40 miles per gallon?". It will print the following nine results:

Expected output (order of lines may differ)
{ Name: 'volkswagen rabbit custom diesel', MPG: 43.1 }
{ Name: 'vw rabbit c (diesel)', MPG: 44.3 }
{ Name: 'renault lecar deluxe', MPG: 40.9 }
{ Name: 'honda civic 1500 gl', MPG: 44.6 }
{ Name: 'datsun 210', MPG: 40.8 }
{ Name: 'vw pickup', MPG: 44 }
{ Name: 'mazda glc', MPG: 46.6 }
{ Name: 'vw dasher (diesel)', MPG: 43.4 }
{ Name: 'vw rabbit', MPG: 41.5 }

一些使用较旧 GPU 的 SheetJS 用户报告了错误。

¥Some SheetJS users with older GPUs have reported errors.

如果命令失败，请尝试再次运行脚本。

¥If the command fails, please try running the script a second time.

要找到预期结果：

¥To find the expected results:

  • 在 Excel 中打开 cd.xls 电子表格

    ¥Open the cd.xls spreadsheet in Excel

  • 在功能区中选择主页 > 排序和筛选 > 筛选

    ¥Select Home > Sort & Filter > Filter in the Ribbon

  • 选择 B 列 (Miles_per_Gallon) 的筛选选项

    ¥Select the filter option for column B (Miles_per_Gallon)

  • 在弹出窗口中，在 Filter 下拉列表中选择 "大于" 并输入 40

    ¥In the popup, select "Greater Than" in the Filter dropdown and type 40

过滤后的结果应与以下屏幕截图匹配：

¥The filtered results should match the following screenshot:

Expected Results

SheetJS 模型 公开 formulae 和其他功能。

¥The SheetJS model exposes formulae and other features.

SheetJS 专业版 构建公开了单元格样式、图片、图表、表格和其他功能。

¥SheetJS Pro builds expose cell styling, images, charts, tables, and other features.

[^1]: 有关 LangChain 文档中的 "如何加载 CSV 数据"

¥See "How to load CSV data" in the LangChain documentation

[^2]: 见 readFile 于 "读取文件"

¥See readFile in "Reading Files"

[^3]: 见 "SheetJS 数据模型"

¥See "SheetJS Data Model"

[^4]: 见 sheet_to_csv 于 "CSV 和文本"

¥See sheet_to_csv in "CSV and Text"

[^5]: 有关 LangChain 文档中的 "包含多个文件的文件夹"

¥See "Folders with multiple files" in the LangChain documentation

[^6]: 见 "写入文件" 中的 "支持的输出格式" 类型

¥See "Supported Output Formats" type in "Writing Files"

[^7]: 见 "工作簿对象"

¥See "Workbook Object"

[^8]: 见 sheet_to_json 于 "实用工具"

¥See sheet_to_json in "Utilities"

[^9]: 有关 ChatQA 论文和其他模型详细信息，请参阅 官方 ChatQA 网站。

¥See the official ChatQA website for the ChatQA paper and other model details.