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# docs: ecosystem/integrations update It is the first in a series of `ecosystem/integrations` updates. The ecosystem/integrations list is missing many integrations. I'm adding the missing integrations in a consistent format: 1. description of the integrated system 2. `Installation and Setup` section with 'pip install ...`, Key setup, and other necessary settings 3. Sections like `LLM`, `Text Embedding Models`, `Chat Models`... with links to correspondent examples and imports of the used classes. This PR keeps new docs, that are presented in the `docs/modules/models/text_embedding/examples` but missed in the `ecosystem/integrations`. The next PRs will cover the next example sections. Also updated `integrations.rst`: added the `Dependencies` section with a link to the packages used in LangChain. ## Who can review? @hwchase17 @eyurtsev @dev2049
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SageMaker Endpoint
Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.
We use SageMaker to host our model and expose it as the SageMaker Endpoint.
Installation and Setup
pip install boto3
For instructions on how to expose model as a SageMaker Endpoint, please see here.
Note: In order to handle batched requests, we need to adjust the return line in the predict_fn() function within the custom inference.py script:
Change from
return {"vectors": sentence_embeddings[0].tolist()}
to:
return {"vectors": sentence_embeddings.tolist()}
We have to set up following required parameters of the SagemakerEndpoint call:
endpoint_name: The name of the endpoint from the deployed Sagemaker model. Must be unique within an AWS Region.credentials_profile_name: The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which has either access keys or role information specified. If not specified, the default credential profile or, if on an EC2 instance, credentials from IMDS will be used. See this guide.
LLM
See a usage example.
from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
Text Embedding Models
See a usage example.
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase