> ## 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.

# IBM watsonx.ai integration

> Integrate with the IBM watsonx.ai embedding model using LangChain JavaScript.

This will help you get started with IBM watsonx.ai [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `IBM watsonx.ai` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-ibm/WatsonxEmbeddings).

## Overview

### Integration details

| Class                                                                                             | Package                                                          | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/ibm_watsonx/) |                                            Downloads                                           |                                           Version                                           |
| :------------------------------------------------------------------------------------------------ | :--------------------------------------------------------------- | :---: | :----------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: |
| [`WatsonxEmbeddings`](https://reference.langchain.com/javascript/langchain-ibm/WatsonxEmbeddings) | [`@langchain/ibm`](https://www.npmjs.com/package/@langchain/ibm) |   ❌   |                                           ✅                                          | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/ibm?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/ibm?style=flat-square\&label=%20&) |

## Setup

To access IBM WatsonxAI embeddings you'll need to create an IBM watsonx.ai account, get an API key or any other type of credentials, and install the `@langchain/ibm` integration package.

### Credentials

Head to [IBM Cloud](https://cloud.ibm.com/login) to sign up to IBM watsonx.ai and generate an API key or provide any other authentication form as presented below.

#### IAM authentication

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export WATSONX_AI_AUTH_TYPE=iam
export WATSONX_AI_APIKEY=<YOUR-APIKEY>
```

#### Bearer token authentication

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export WATSONX_AI_AUTH_TYPE=bearertoken
export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>
```

#### IBM watsonx.ai software authentication

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export WATSONX_AI_AUTH_TYPE=cp4d
export WATSONX_AI_USERNAME=<YOUR_USERNAME>
export WATSONX_AI_PASSWORD=<YOUR_PASSWORD>
export WATSONX_AI_URL=<URL>
```

Once these are placed in your environment variables and object is initialized authentication will proceed automatically.

Authentication can also be accomplished by passing these values as parameters to a new instance.

## IAM authentication

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

const props = {
  version: "YYYY-MM-DD",
  serviceUrl: "<SERVICE_URL>",
  projectId: "<PROJECT_ID>",
  watsonxAIAuthType: "iam",
  watsonxAIApikey: "<YOUR-APIKEY>",
};
const instance = new WatsonxEmbeddings(props);
```

## Bearer token authentication

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

const props = {
  version: "YYYY-MM-DD",
  serviceUrl: "<SERVICE_URL>",
  projectId: "<PROJECT_ID>",
  watsonxAIAuthType: "bearertoken",
  watsonxAIBearerToken: "<YOUR-BEARERTOKEN>",
};
const instance = new WatsonxEmbeddings(props);
```

### IBM watsonx.ai software authentication

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

const props = {
  version: "YYYY-MM-DD",
  serviceUrl: "<SERVICE_URL>",
  projectId: "<PROJECT_ID>",
  watsonxAIAuthType: "cp4d",
  watsonxAIUsername: "<YOUR-USERNAME>",
  watsonxAIPassword: "<YOUR-PASSWORD>",
  watsonxAIUrl: "<url>",
};
const instance = new WatsonxEmbeddings(props);
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) 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 IBM watsonx.ai integration lives in the `@langchain/ibm` package:

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

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

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

## Instantiation

Now we can instantiate our model object and embed text:

```javascript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { WatsonxEmbeddings } from "@langchain/ibm";

const embeddings = new WatsonxEmbeddings({
  version: "YYYY-MM-DD",
  serviceUrl: process.env.API_URL,
  projectId: "<PROJECT_ID>",
  spaceId: "<SPACE_ID>",
  model: "<MODEL_ID>",
});
```

Note:

* You must provide `spaceId` or `projectId` in order to proceed.
* Depending on the region of your provisioned service instance, use correct serviceUrl.

### Model Gateway

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

const instance = new WatsonxEmbeddings({
  version: "YYYY-MM-DD",
  serviceUrl: process.env.API_URL,
  model: "<ALIAS_MODEL_ID>",
  modelGateway: true,
});
```

## 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).

```javascript 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:

```javascript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    const singleVector = await embeddings.embedQuery(text);
    singleVector.slice(0, 10);
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
   -0.017436018,  -0.01469498,
   -0.015685871, -0.013543149,
  -0.0011519607, -0.008123747,
    0.015286108, -0.023845721,
    -0.02454774,   0.07235078
]
```

### 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:

```javascript 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, 10));
    console.log(vectors[1].slice(0, 10));

```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
  -0.017436024, -0.014695002,
   -0.01568589, -0.013543164,
  -0.001151976, -0.008123703,
   0.015286064, -0.023845702,
  -0.024547677,   0.07235076
]
[
     0.03278884, -0.017893745,
  -0.0027520044,  0.016506646,
    0.028271576,  -0.01284331,
    0.014344065, -0.007968607,
    -0.03899479,  0.039327156
]
```

***

## API reference

For detailed documentation of all **module\_name** features and configurations head to the API reference: **api\_ref\_module**

***

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