> ## 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.
# VoyageEmbeddings integration
> Integrate with the VoyageEmbeddings embedding model using LangChain JavaScript.
This will help you get started with VoyageEmbeddings [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `VoyageEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-mongodb/VoyageEmbeddings).
## Overview
### Integration details
| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/voyageai/) | Downloads | Version |
| :-------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------- | :---: | :-------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: |
| [`VoyageEmbeddings`](https://reference.langchain.com/javascript/langchain-mongodb/VoyageEmbeddings) | [`@langchain/mongodb`](https://www.npmjs.com/package/@langchain/mongodb) | ❌ | ✅ |  |  |
## Setup
To access Voyage AI embedding models you'll need to create a Voyage AI account, get an API key, and install the `@langchain/mongodb` integration package.
### Credentials
Head to [voyageai.com](https://www.voyageai.com) to sign up and generate an API key. Once you've done this set the `VOYAGE_API_KEY` environment variable:
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export VOYAGE_API_KEY="your-api-key"
```
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 VoyageEmbeddings integration lives in the `@langchain/mongodb` package:
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/mongodb @langchain/core
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/mongodb @langchain/core
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/mongodb @langchain/core
```
## Instantiation
Now we can instantiate our model object and embed text:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { VoyageEmbeddings } from "@langchain/mongodb";
const embeddings = new VoyageEmbeddings({
apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.VOYAGE_API_KEY
model: "voyage-4",
});
```
## 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, 3));
```
### 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, 3));
console.log(vectors[1].slice(0, 3));
```
***
## API reference
For detailed documentation of all `VoyageEmbeddings` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-mongodb/VoyageEmbeddings).
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/embeddings/voyageai.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).