> ## 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.

# BedrockEmbeddings 集成

> 使用 LangChain JavaScript 集成 BedrockEmbeddings 嵌入模型。

[Amazon Bedrock](https://aws.amazon.com/bedrock/) 是一项完全托管的服务，通过单一 API 提供来自领先 AI 公司（如 AI21 Labs、Anthropic、Cohere、Meta、Stability AI 和 Amazon）的高性能基础模型（FMs）选择，以及构建具有安全性、隐私性和负责任 AI 的生成式 AI 应用程序所需的一系列广泛功能。

本文将帮助您开始使用 LangChain 集成 Amazon Bedrock 的[嵌入模型](/oss/javascript/integrations/embeddings)。有关 `Bedrock` 功能和配置选项的详细文档，请参阅 [API 参考](https://reference.langchain.com/javascript/langchain-aws/BedrockEmbeddings)。

## 概述

### 集成详情

| 类                                                                                       | 包                                                                |  本地 | [Python 支持](https://python.langchain.com/docs/integrations/embeddings/bedrock/) |                                               下载量                                              |                                              版本                                             |
| :-------------------------------------------------------------------------------------- | :--------------------------------------------------------------- | :-: | :-----------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: |
| [`Bedrock`](https://reference.langchain.com/javascript/langchain-aws/BedrockEmbeddings) | [`@langchain/aws`](https://www.npmjs.com/package/@langchain/aws) |  ❌  |                                        ✅                                        | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/aws?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/aws?style=flat-square\&label=%20&) |

## 设置

要访问 Bedrock 嵌入模型，您需要创建一个 AWS 账户，获取 API 密钥，并安装 `@langchain/aws` 集成包。

前往 [AWS 文档](https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html) 注册 AWS 并设置您的凭证。您还需要为您的账户启用模型访问权限，您可以[按照这些说明](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)进行操作。

### 凭证

如果您希望自动追踪模型调用，还可以通过取消注释以下内容来设置您的 [LangSmith](/langsmith/home) API 密钥：

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```

### 安装

LangChain Bedrock 集成位于 `@langchain/aws` 包中：

<CodeGroup>
  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install @langchain/aws @langchain/core
  ```

  ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  yarn add @langchain/aws @langchain/core
  ```

  ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pnpm add @langchain/aws @langchain/core
  ```
</CodeGroup>

## 实例化

现在我们可以实例化我们的模型对象并嵌入文本。

有几种不同的方式可以对 AWS 进行身份验证 - 以下示例依赖于在环境变量中设置的访问密钥、秘密访问密钥和区域：

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { BedrockEmbeddings } from "@langchain/aws";

const embeddings = new BedrockEmbeddings({
  region: process.env.BEDROCK_AWS_REGION!,
  credentials: {
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
  },
  model: "amazon.titan-embed-text-v1",
});
```

## 索引与检索

嵌入模型通常用于检索增强生成（RAG）流程中，既作为索引数据的一部分，也用于后续检索。更详细的说明，请参阅 [**学习** 标签页](/oss/javascript/learn/)下的 RAG 教程。

下面，看看如何使用上面初始化的 `embeddings` 对象来索引和检索数据。在此示例中，我们将使用演示版 [`MemoryVectorStore`](/oss/javascript/integrations/vectorstores/memory) 来索引和检索一个示例文档。

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// 使用示例文本创建向量存储
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";

const text = "LangChain 是用于构建上下文感知推理应用程序的框架";

const vectorstore = await MemoryVectorStore.fromDocuments(
  [{ pageContent: text, metadata: {} }],
  embeddings,
);

// 将向量存储用作返回单个文档的检索器
const retriever = vectorstore.asRetriever(1);

// 检索最相似的文本
const retrievedDocuments = await retriever.invoke("什么是 LangChain？");

retrievedDocuments[0].pageContent;
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
LangChain 是用于构建上下文感知推理应用程序的框架
```

## 直接使用

在底层，向量存储和检索器的实现会调用 `embeddings.embedDocument(...)` 和 `embeddings.embedQuery(...)` 来为 `fromDocuments` 中使用的文本以及检索器的 `invoke` 操作分别创建嵌入。

您可以直接调用这些方法来获取用于自己用例的嵌入。

### 嵌入单个文本

您可以使用 `embedQuery` 嵌入查询以进行搜索。这会生成特定于查询的向量表示：

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const singleVector = await embeddings.embedQuery(text);

console.log(singleVector.slice(0, 100));
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
         0.625,  0.111328125,      0.265625,   -0.20019531,  0.40820312,
  -0.010803223,  -0.22460938, -0.0002937317,    0.29882812, -0.14355469,
  -0.068847656,   -0.3984375,          0.75,    -0.1953125,  -0.5546875,
  -0.087402344,       0.5625,      1.390625,    -0.3515625,  0.39257812,
  -0.061767578,      0.65625,   -0.36328125,   -0.06591797,    0.234375,
   -0.36132812,   0.42382812,  -0.115234375,   -0.28710938, -0.29296875,
     -0.765625,  -0.16894531,    0.23046875,     0.6328125, -0.08544922,
    0.13671875, 0.0004272461,        0.3125,    0.12207031,   -0.546875,
    0.14257812, -0.119628906,  -0.111328125,    0.61328125,      0.6875,
     0.3671875,   -0.2578125,   -0.27734375,      0.703125,    0.203125,
    0.17675781,  -0.26757812,   -0.76171875,    0.71484375,  0.77734375,
    -0.1953125, -0.007232666,  -0.044921875,    0.23632812, -0.24121094,
  -0.012207031,    0.5078125,    0.08984375,    0.56640625,  -0.3046875,
     0.6484375,        -0.25,   -0.37890625,    -0.2421875,  0.38476562,
   -0.18164062,  -0.05810547,     0.7578125,    0.04296875,    0.609375,
    0.50390625,  0.023803711,   -0.23046875,   0.099121094,  0.79296875,
     -1.296875,     0.671875,   -0.66796875,    0.43359375, 0.087890625,
    0.14550781,  -0.37304688,  -0.068359375, 0.00012874603, -0.47265625,
     -0.765625,   0.07861328,  -0.029663086,   0.076660156, -0.32617188,
     -0.453125,   -0.5546875,   -0.45703125,     1.1015625, -0.29492188
]
```

### 嵌入多个文本

您可以使用 `embedDocuments` 嵌入多个文本以进行索引。此方法内部使用的机制可能（但不一定）与嵌入查询不同：

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const text2 = "LangGraph 是一个用于使用 LLMs 构建有状态、多参与者应用程序的库";

const vectors = await embeddings.embedDocuments([text, text2]);

console.log(vectors[0].slice(0, 100));
console.log(vectors[1].slice(0, 100));
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
         0.625,  0.111328125,      0.265625,   -0.20019531,  0.40820312,
  -0.010803223,  -0.22460938, -0.0002937317,    0.29882812, -0.14355469,
  -0.068847656,   -0.3984375,          0.75,    -0.1953125,  -0.5546875,
  -0.087402344,       0.5625,      1.390625,    -0.3515625,  0.39257812,
  -0.061767578,      0.65625,   -0.36328125,   -0.06591797,    0.234375,
   -0.36132812,   0.42382812,  -0.115234375,   -0.28710938, -0.29296875,
     -0.765625,  -0.16894531,    0.23046875,     0.6328125, -0.08544922,
    0.13671875, 0.0004272461,        0.3125,    0.12207031,   -0.546875,
    0.14257812, -0.119628906,  -0.111328125,    0.61328125,      0.6875,
     0.3671875,   -0.2578125,   -0.27734375,      0.703125,    0.203125,
    0.17675781,  -0.26757812,   -0.76171875,    0.71484375,  0.77734375,
    -0.1953125, -0.007232666,  -0.044921875,    0.23632812, -0.24121094,
  -0.012207031,    0.5078125,    0.08984375,    0.56640625,  -0.3046875,
     0.6484375,        -0.25,   -0.37890625,    -0.2421875,  0.38476562,
   -0.18164062,  -0.05810547,     0.7578125,    0.04296875,    0.609375,
    0.50390625,  0.023803711,   -0.23046875,   0.099121094,  0.79296875,
     -1.296875,     0.671875,   -0.66796875,    0.43359375, 0.087890625,
    0.14550781,  -0.37304688,  -0.068359375, 0.00012874603, -0.47265625,
     -0.765625,   0.07861328,  -0.029663086,   0.076660156, -0.32617188,
     -0.453125,   -0.5546875,   -0.45703125,     1.1015625, -0.29492188
]
[
       0.65625,    0.48242188,    0.70703125,   -0.13378906,    0.859375,
     0.2578125,   -0.13378906, -0.0002670288,      -0.34375,  0.25585938,
   -0.33984375,   -0.26367188,      0.828125,   -0.23242188, -0.61328125,
    0.12695312,    0.43359375,     1.3828125,  -0.099121094,   0.3203125,
   -0.34765625,    0.35351562,   -0.28710938,   0.009521484, 0.083496094,
   0.040283203,   -0.25390625,    0.17871094,   0.044189453, -0.19628906,
    0.45898438,    0.21191406,    0.67578125,     0.8359375, -0.29101562,
   0.021118164,    0.13671875,   0.083984375,    0.34570312,  0.30859375,
  -0.001625061,    0.31835938,   -0.18164062, -0.0058288574,  0.22460938,
    0.26757812,   -0.09082031,    0.17480469,     1.4921875, -0.24316406,
    0.36523438,    0.14550781,     -0.609375,    0.33007812,  0.10595703,
     0.3671875,    0.18359375,   -0.62109375,    0.51171875, 0.024047852,
   0.092285156,   -0.44335938,     0.4921875,      0.609375, -0.48242188,
      0.796875,   -0.47851562,      -0.53125,   -0.66796875,  0.68359375,
   -0.16796875,   0.110839844,    0.84765625,      0.703125,   0.8671875,
    0.37695312, -0.0022888184,   -0.30664062,     0.3671875,  0.16503906,
   -0.59765625,     0.3203125,      -0.34375,    0.08251953,    0.890625,
    0.38476562,   -0.24707031,        -0.125, 0.00013160706, -0.69921875,
      -0.53125,   0.052490234,    0.27734375,    0.42773438, -0.38867188,
    -0.2578125,         -0.25,      -0.46875,      0.828125, -0.94140625
]
```

## 配置 Bedrock 运行时客户端

如果您想自定义 `credentials`、`region`、`retryPolicy` 等选项，可以传入您自己的 `BedrockRuntimeClient` 实例。

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { BedrockRuntimeClient } from "@aws-sdk/client-bedrock-runtime";
import { BedrockEmbeddings } from "@langchain/aws";

const getCredentials = () => {
  // 执行某些操作以获取凭证
}

// @lc-ts-ignore
const client = new BedrockRuntimeClient({
  region: "us-east-1",
  credentials: getCredentials(),
});

const embeddingsWithCustomClient = new BedrockEmbeddings({
  client,
});
```

***

## API 参考

有关所有 Bedrock 功能和配置的详细文档，请前往 [API 参考](https://reference.langchain.com/javascript/langchain-aws/BedrockEmbeddings)。

***

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