langchain/libs/experimental/langchain_experimental/recommenders/__init__.py

"""**Amazon Personalize** primitives.

[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html) 
is a fully managed machine learning service that uses your data to generate 
item recommendations for your users.
"""
from langchain_experimental.recommenders.amazon_personalize import AmazonPersonalize
from langchain_experimental.recommenders.amazon_personalize_chain import (
    AmazonPersonalizeChain,
)

__all__ = ["AmazonPersonalize", "AmazonPersonalizeChain"]
experimental[patch]: update module doc strings (#19539) Added missed module descriptions. Fixed format. 2024-03-26 14:38:10 +00:00			`"""Amazon Personalize primitives.`

			`[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html)`
			`is a fully managed machine learning service that uses your data to generate`
			`item recommendations for your users.`
infra: rm unused # noqa violations (#22049) Updating #21137 2024-05-22 22:21:08 +00:00			`"""`
experimental[minor]: Amazon Personalize support (#17436) ## Amazon Personalize support on Langchain This PR is a successor to this PR - https://github.com/langchain-ai/langchain/pull/13216 This PR introduces an integration with [Amazon Personalize](https://aws.amazon.com/personalize/) to help you to retrieve recommendations and use them in your natural language applications. This integration provides two new components: 1. An `AmazonPersonalize` client, that provides a wrapper around the Amazon Personalize API. 2. An `AmazonPersonalizeChain`, that provides a chain to pull in recommendations using the client, and then generating the response in natural language. We have added this to langchain_experimental since there was feedback from the previous PR about having this support in experimental rather than the core or community extensions. Here is some sample code to explain the usage. ```python from langchain_experimental.recommenders import AmazonPersonalize from langchain_experimental.recommenders import AmazonPersonalizeChain from langchain.llms.bedrock import Bedrock recommender_arn = "<insert_arn>" client=AmazonPersonalize( credentials_profile_name="default", region_name="us-west-2", recommender_arn=recommender_arn ) bedrock_llm = Bedrock( model_id="anthropic.claude-v2", region_name="us-west-2" ) chain = AmazonPersonalizeChain.from_llm( llm=bedrock_llm, client=client ) response = chain({'user_id': '1'}) ``` Reviewer: @3coins 2024-02-19 18:36:37 +00:00			`from langchain_experimental.recommenders.amazon_personalize import AmazonPersonalize`
			`from langchain_experimental.recommenders.amazon_personalize_chain import (`
			`AmazonPersonalizeChain,`
			`)`

			`__all__ = ["AmazonPersonalize", "AmazonPersonalizeChain"]`