> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # BedrockEmbeddings integration > Integrate with the BedrockEmbeddings embedding model using LangChain JavaScript. [Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. This will help you get started with Amazon Bedrock [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `Bedrock` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-aws/BedrockEmbeddings). ## Overview ### Integration details | Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/bedrock/) | Downloads | Version | | :-------------------------------------------------------------------------------------- | :--------------------------------------------------------------- | :---: | :------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | | [`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&) | ## Setup To access Bedrock embedding models you'll need to create an AWS account, get an API key, and install the `@langchain/aws` integration package. Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html) to sign up for AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by [following these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html). ### Credentials If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/home) API key by uncommenting below: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # export LANGSMITH_TRACING="true" # export LANGSMITH_API_KEY="your-api-key" ``` ### Installation The LangChain Bedrock integration lives in the `@langchain/aws` package: ```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 ``` ## Instantiation Now we can instantiate our model object and embed text. There are a few different ways to authenticate with AWS - the below examples rely on an access key, secret access key and region set in your environment variables: ```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", }); ``` ## Indexing and retrieval Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [**Learn** tab](/oss/javascript/learn/). Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document using the demo [`MemoryVectorStore`](/oss/javascript/integrations/vectorstores/memory). ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} // Create a vector store with a sample text import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory"; 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; ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} LangChain is the framework for building context-aware reasoning applications ``` ## Direct usage Under the hood, the vectorstore and retriever implementations are calling `embeddings.embedDocument(...)` and `embeddings.embedQuery(...)` to create embeddings for the text(s) used in `fromDocuments` and the retriever's `invoke` operations, respectively. You can directly call these methods to get embeddings for your own use cases. ### Embed single texts You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query: ```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 ] ``` ### Embed multiple texts You can embed multiple texts for indexing with `embedDocuments`. The internals used for this method may (but do not have to) differ from embedding queries: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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)); ``` ```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 ] ``` ## Configuring the Bedrock runtime client You can pass in your own instance of the `BedrockRuntimeClient` if you want to customize options like `credentials`, `region`, `retryPolicy`, etc. ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { BedrockRuntimeClient } from "@aws-sdk/client-bedrock-runtime"; import { BedrockEmbeddings } from "@langchain/aws"; 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 reference For detailed documentation of all Bedrock features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-aws/BedrockEmbeddings). ***
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