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Integration: Amazon Sagemaker

Use Models from Huggingface, Anthropic, AI21 Labs, Cohere, Meta, and Amazon via Amazon Sagemaker with Haystack


Table of Contents


Amazon Sagemaker is a comprehensive, fully managed machine learning service that allows data scientists and developers to build, train, and deploy ML models efficiently. More information can be found on the documentation page.

Haystack 2.x


Install the Amazon Sagemaker integration:

pip install amazon-sagemaker-haystack


Once installed, you will have access to a SagemakerGenerator that supports models from various providers. To know more about which models are supported, check out Sagemaker’s documentation.

To use this integration for text generation, initialize a SagemakerGenerator with the model name and aws credentials:

import os
haystack_integrations.components.generators.amazon_sagemaker import SagemakerGenerator

os.environ["AWS_ACCESS_KEY_ID"] = "..."
os.environ["AWS_SECRET_ACCESS_KEY"] = "..."
# This one is optional
os.environ["AWS_REGION_NAME"] = "..."

model = # Your Sagemaker endpoint name, such as "jumpstart-dft-hf-llm-falcon-7b-instruct-bf16"

generator = SagemakerGenerator(model=model)
result ="Who is the best American actor?")
for reply in result["replies"]:


'There is no definitive "best" American actor, as acting skill and talent are subjective.
However, some of the most acclaimed and influential American actors include Tom Hanks,
Daniel Day-Lewis, Denzel Washington, Meryl Streep, Rober# t De Niro, Al Pacino, Marlon Brando,
Jack Nicholson, Leonardo DiCaprio and John# ny Depp. Choosing a single "best" actor comes
down to personal preference.'

Note that different models may require different parameters. One notable example is the Llama2 family of models, which should be initialized with {'accept_eula': True} as a custom attribute:

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

Haystack 1.x

Installation (1.x)

pip install farm-haystack[aws]

Usage (1.x)

To use Sagemaker models in Haystack 1.x, initialize a PromptNode with the model name, AWS credentials and the prompt template. You can then use this PromptNode in a question answering pipeline to generate answers based on the given context.

Below is the example of generative questions answering pipeline using RAG with an EmbeddingRetriever using Cohere models and a Sagemaker-powered PromptNode:

from haystack.nodes import PromptNode, EmbeddingRetriever
from haystack.pipelines import Pipeline

retriever = EmbeddingRetriever(
    embedding_model="embed-english-v2.0", document_store=document_store, api_key=COHERE_API_KEY
prompt_node = PromptNode(model_name_or_path="sagemaker-model-endpoint-name", model_kwargs={"aws_profile_name": "my_aws_profile_name","aws_region_name": "your-aws-region"})

query_pipeline = Pipeline()
query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])"YOUR_QUERY")