以编程方式使用文档

Amazon Bedrock 是一项完全托管的服务，提供来自 AI21 Labs、Anthropic、Cohere、Meta、Stability AI 和 Amazon 等领先 AI 公司的高性能基础模型（FM），通过单一 API 供您选择，还提供构建生成式 AI 应用程序所需的各种功能，包括安全性、隐私性和负责任的 AI。

这将帮助您开始使用 Amazon Bedrock 嵌入模型 使用 LangChain。如需获取有关 Bedrock 功能和配置选项的详细文档，请参阅 API 参考.

概述

集成详情

类包本地Py 支持下载量版本
Bedrock@langchain/aws❌✅!NPM - 下载量!NPM - 版本

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。

请访问 AWS 文档 注册 AWS 并设置您的凭证。您还需要为您的账户开启模型访问权限，您可以通过 遵循以下说明.

凭证

如果您想获取模型调用的自动化追踪功能，还可以设置您的 LangSmith API 密钥，只需取消下方注释：

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

安装

LangChain Bedrock 集成位于 @langchain/aws package:

npm install @langchain/aws @langchain/core
yarn add @langchain/aws @langchain/core
pnpm add @langchain/aws @langchain/core

实例化

现在我们可以实例化模型对象并嵌入文本。

有几种不同的方式可以向 AWS 进行身份验证——下面的示例依赖于您环境变量中设置的访问密钥、秘密访问密钥和区域：

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",
});

索引和检索

嵌入模型通常用于检索增强生成（RAG）流程，既可以作为索引数据的一部分，也可以在后续检索时使用。如需获取更详细的说明，请参阅 **学习** 标签页下的.

RAG 教程。请参阅以下内容，了解如何使用 embeddings 对象对数据进行索引和检索。在此示例中，我们将使用演示 MemoryVectorStore.

// Create a vector store with a sample text


const text = "LangChain is the framework for building context-aware reasoning applications";

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

// Use the vector store as a retriever that returns a single document
const retriever = vectorstore.asRetriever(1);

// Retrieve the most similar text
const retrievedDocuments = await retriever.invoke("What is LangChain?");

retrievedDocuments[0].pageContent;
LangChain is the framework for building context-aware reasoning applications

直接使用

在底层，向量存储和检索器的实现正在调用 embeddings.embedDocument(...) 和 embeddings.embedQuery(...) 来为 fromDocuments 以及检索器的 invoke 操作中使用的文本创建嵌入。

您可以直接调用这些方法来为您的使用场景获取嵌入。

嵌入单个文本

您可以使用以下方式嵌入搜索查询 embedQuery。这会生成针对该查询的向量表示：

const singleVector = await embeddings.embedQuery(text);

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

您可以使用以下方式嵌入多个文本用于索引 embedDocuments。此方法使用的内部实现可能（但不一定）与嵌入查询不同：

const text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs";

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

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

您可以传入自己的 BedrockRuntimeClient 实例如果您想自定义 credentials, region, retryPolicy等选项。

const getCredentials = () => {
  // do something to get credentials
}

// @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，请参阅 API 参考.