---
title: IBM watsonx.ai
 | LlamaIndex OSS Documentation
---

> WatsonxEmbeddings is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) embedding models.

This example shows how to communicate with `watsonx.ai` embedding models using the `LlamaIndex` Embeddings API.

## Setting up

Install the `llama-index-embeddings-ibm` package:

```
!pip install -qU llama-index-embeddings-ibm
```

The cell below defines the credentials required to work with watsonx Embeddings.

**Action:** Provide the IBM Cloud user API key. For details, see [Managing user API keys](https://cloud.ibm.com/docs/account?topic=account-userapikey\&interface=ui).

```
import os
from getpass import getpass


watsonx_api_key = getpass()
os.environ["WATSONX_APIKEY"] = watsonx_api_key
```

Additionally, you can pass additional secrets as an environment variable:

```
import os


os.environ["WATSONX_URL"] = "your service instance url"
os.environ["WATSONX_TOKEN"] = "your token for accessing the CPD cluster"
os.environ["WATSONX_PASSWORD"] = "your password for accessing the CPD cluster"
os.environ["WATSONX_USERNAME"] = "your username for accessing the CPD cluster"
os.environ[
    "WATSONX_INSTANCE_ID"
] = "your instance_id for accessing the CPD cluster"
```

## Load the model

You might need to adjust embedding parameters for different tasks:

```
truncate_input_tokens = 3
```

Initialize the `WatsonxEmbeddings` class with the previously set parameter.

**Note**:

- To provide context for the API call, you must pass the `project_id` or `space_id`. To get your project or space ID, open your project or space, go to the **Manage** tab, and click **General**. For more information see: [Project documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects) or [Deployment space documentation](https://www.ibm.com/docs/en/watsonx/saas?topic=spaces-creating-deployment).
- Depending on the region of your provisioned service instance, use one of the urls listed in [watsonx.ai API Authentication](https://ibm.github.io/watsonx-ai-python-sdk/setup_cloud.html#authentication).

In this example, we’ll use the `project_id` and Dallas URL.

You need to specify the `model_id` that will be used for inferencing. You can find the list of all the available models in [Supported foundation models](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes).

```
from llama_index.embeddings.ibm import WatsonxEmbeddings


watsonx_embedding = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
    truncate_input_tokens=truncate_input_tokens,
)
```

Alternatively, you can use Cloud Pak for Data credentials. For details, see [watsonx.ai software setup](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html).

```
watsonx_embedding = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    url="PASTE YOUR URL HERE",
    username="PASTE YOUR USERNAME HERE",
    password="PASTE YOUR PASSWORD HERE",
    instance_id="openshift",
    version="4.8",
    project_id="PASTE YOUR PROJECT_ID HERE",
    truncate_input_tokens=truncate_input_tokens,
)
```

## Usage

### Embed query

```
query = "Example query."


query_result = watsonx_embedding.get_query_embedding(query)
print(query_result[:5])
```

```
[-0.05538924, 0.05161056, 0.01207759, 0.0017501727, -0.017691258]
```

### Embed list of texts

```
texts = ["This is a content of one document", "This is another document"]


doc_result = watsonx_embedding.get_text_embedding_batch(texts)
print(doc_result[0][:5])
```

```
[0.009447167, -0.024981938, -0.02601326, -0.04048393, -0.05780444]
```