---
title: Interacting with Embeddings deployed in Amazon SageMaker Endpoint with LlamaIndex
 | Developer Documentation
---

An Amazon SageMaker endpoint is a fully managed resource that enables the deployment of machine learning models, for making predictions on new data.

This notebook demonstrates how to interact with Embedding endpoints using `SageMakerEmbedding`, unlocking additional llamaIndex features. So, It is assumed that an Embedding is deployed on a SageMaker endpoint.

## Setting Up

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-embeddings-sagemaker-endpoint
```

```
! pip install llama-index
```

You have to specify the endpoint name to interact with.

```
ENDPOINT_NAME = "<-YOUR-ENDPOINT-NAME->"
```

Credentials should be provided to connect to the endpoint. You can either:

- use an AWS profile by specifying the `profile_name` parameter, if not specified, the default credential profile will be used.
- Pass credentials as parameters (`aws_access_key_id`, `aws_secret_access_key`, `aws_session_token`, `region_name`).

for more details check [this link](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html).

**AWS profile name**

```
from llama_index.embeddings.sagemaker_endpoint import SageMakerEmbedding


AWS_ACCESS_KEY_ID = "<-YOUR-AWS-ACCESS-KEY-ID->"
AWS_SECRET_ACCESS_KEY = "<-YOUR-AWS-SECRET-ACCESS-KEY->"
AWS_SESSION_TOKEN = "<-YOUR-AWS-SESSION-TOKEN->"
REGION_NAME = "<-YOUR-ENDPOINT-REGION-NAME->"
```

```
embed_model = SageMakerEmbedding(
    endpoint_name=ENDPOINT_NAME,
    aws_access_key_id=AWS_ACCESS_KEY_ID,
    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
    aws_session_token=AWS_SESSION_TOKEN,
    region_name=REGION_NAME,
)
```

**With credentials**:

```
from llama_index.embeddings.sagemaker_endpoint import SageMakerEmbedding


ENDPOINT_NAME = "<-YOUR-ENDPOINT-NAME->"
PROFILE_NAME = "<-YOUR-PROFILE-NAME->"
embed_model = SageMakerEmbedding(
    endpoint_name=ENDPOINT_NAME, profile_name=PROFILE_NAME
)  # Omit the profile name to use the default profile
```

## Basic Usage

### Call `get_text_embedding`

```
embeddings = embed_model.get_text_embedding(
    "An Amazon SageMaker endpoint is a fully managed resource that enables the deployment of machine learning models, specifically LLM (Large Language Models), for making predictions on new data."
)
```

```
embeddings
```

```
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```

### Call `get_text_embedding_batch`

```
embeddings = embed_model.get_text_embedding_batch(
    [
        "An Amazon SageMaker endpoint is a fully managed resource that enables the deployment of machine learning models",
        "Sagemaker is integrated with llamaIndex",
    ]
)
```

```
len(embeddings)
```

```
2
```