feat: add Sagemaker integration

integrations/amazon-sagemaker.md

@@ -0,0 +1,111 @@
+---
+layout: integration
+name: Amazon Sagemaker
+description: Use Models from Huggingface, Anthropic, AI21 Labs, Cohere, Meta, and Amazon via Amazon Sagemaker with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/amazon-sagemaker-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_sagemaker
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/aws.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Haystack 2.x](#haystack-2x)
+    - [Installation](#installation)
+    - [Usage](#usage)
+- [Haystack 1.x](#haystack-1x)
+    - [Installation (1.x)](#installation-1x)
+    - [Usage (1.x)](#usage-1x)
+
+## Overview
+
+[Amazon Sagemaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) 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](https://docs.haystack.deepset.ai/v2.0/docs/sagemakergenerator).
+
+## Haystack 2.x
+
+### Installation
+
+Install the Amazon Sagemaker integration:
+```bash
+pip install amazon-sagemaker-haystack
+```
+
+### Usage
+
+Once installed, you will have access to a [SagemakerGenerator](https://docs.haystack.deepset.ai/v2.0/docs/sagemakergenerator) that supports models from various providers. To know more
+about which models are supported, check out [Sagemaker's documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models.html).
+
+To use this integration for text generation, initialize a `SagemakerGenerator` with the model name and aws credentials: 
+
+```python
+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 = generator.run("Who is the best American actor?")
+for reply in result["replies"]:
+    print(reply)
+```
+Output: 
+```shell
+'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:
+
+```python
+generator = SagemakerGenerator(model="jumpstart-dft-meta-textgenerationneuron-llama-2-7b", aws_custom_attributes={"accept_eula": True})
+```
+
+## Haystack 1.x  
+
+### Installation (1.x)
+
+```bash
+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`:
+
+```python
+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"])
+query_pipeline.run("YOUR_QUERY")
+```