---
title: "SagemakerGenerator"
id: sagemakergenerator
slug: "/sagemakergenerator"
description: "This component enables text generation using LLMs deployed on Amazon Sagemaker."
---

# SagemakerGenerator

This component enables text generation using LLMs deployed on Amazon Sagemaker.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.mdx)  |
| **Mandatory init variables** | `model`: The model to use  <br /> <br />`aws_access_key_id`: AWS access key ID. Can be set with `AWS_ACCESS_KEY_ID` env var.  <br /> <br />`aws_secret_access_key`: AWS secret access key. Can be set with `AWS_SECRET_ACCESS_KEY` env var. |
| **Mandatory run variables** | `prompt`: A string containing the prompt for the LLM |
| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Amazon Sagemaker](/reference/integrations-amazon-sagemaker) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_sagemaker |

</div>

`SagemakerGenerator` allows you to make use of models deployed on [AWS SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html).

## Parameters Overview

`SagemakerGenerator` needs AWS credentials to work.  Set the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables.

You also need to specify your Sagemaker endpoint at initialization time for the component to work. Pass the endpoint name to the `model` parameter like this:

```python
generator = SagemakerGenerator(model="jumpstart-dft-hf-llm-falcon-7b-instruct-bf16")
```

Additionally, you can pass any text generation parameters valid for your specific model directly to `SagemakerGenerator` using the `generation_kwargs` parameter, both at initialization and to `run()` method.

If your model also needs custom attributes, pass those as a dictionary at initialization time by setting the `aws_custom_attributes` parameter.

One notable family of models that needs these custom parameters is Llama2, which needs to be initialized with `{"accept_eula": True}` :

```python
generator = SagemakerGenerator(
    model="jumpstart-dft-meta-textgenerationneuron-llama-2-7b",
    aws_custom_attributes={"accept_eula": True},
)
```

## Usage

You need to install `amazon-sagemaker-haystack` package to use the  `SagemakerGenerator`:

```shell
pip install amazon-sagemaker-haystack
```

### On its own

Basic usage:

```python
from haystack_integrations.components.generators.amazon_sagemaker import SagemakerGenerator

client = SagemakerGenerator(model="jumpstart-dft-hf-llm-falcon-7b-instruct-bf16")
client.warm_up()
response = client.run("Briefly explain what NLP is in one sentence.")
print(response)

 'metadata': [{}]}
```

### In a pipeline

In a RAG pipeline:

```python
from haystack_integrations.components.generators.amazon_sagemaker import (
    SagemakerGenerator,
)
from haystack import Pipeline
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders import PromptBuilder

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: What's the official language of {{ country }}?
"""
pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component(
    "llm",
    SagemakerGenerator(model="jumpstart-dft-hf-llm-falcon-7b-instruct-bf16"),
)
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "llm")

pipe.run({"prompt_builder": {"country": "France"}})
```
