Source code for langchain_experimental.recommenders.amazon_personalize

from typing import Any, List, Mapping, Optional, Sequence


[docs] class AmazonPersonalize: """Amazon Personalize Runtime wrapper for executing real-time operations. See [this link for more details](https://docs.aws.amazon.com/personalize/latest/dg/API_Operations_Amazon_Personalize_Runtime.html). Args: campaign_arn: str, Optional: The Amazon Resource Name (ARN) of the campaign to use for getting recommendations. recommender_arn: str, Optional: The Amazon Resource Name (ARN) of the recommender to use to get recommendations client: Optional: boto3 client credentials_profile_name: str, Optional :AWS profile name region_name: str, Optional: AWS region, e.g., us-west-2 Example: .. code-block:: python personalize_client = AmazonPersonalize ( campaignArn='<my-campaign-arn>' ) """
[docs] def __init__( self, campaign_arn: Optional[str] = None, recommender_arn: Optional[str] = None, client: Optional[Any] = None, credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, ): self.campaign_arn = campaign_arn self.recommender_arn = recommender_arn if campaign_arn and recommender_arn: raise ValueError( "Cannot initialize AmazonPersonalize with both " "campaign_arn and recommender_arn." ) if not campaign_arn and not recommender_arn: raise ValueError( "Cannot initialize AmazonPersonalize. Provide one of " "campaign_arn or recommender_arn" ) try: if client is not None: self.client = client else: import boto3 import botocore.config if credentials_profile_name is not None: session = boto3.Session(profile_name=credentials_profile_name) else: # use default credentials session = boto3.Session() client_params = {} if region_name: client_params["region_name"] = region_name service = "personalize-runtime" session_config = botocore.config.Config(user_agent_extra="langchain") client_params["config"] = session_config self.client = session.client(service, **client_params) except ImportError: raise ModuleNotFoundError( "Could not import boto3 python package. " "Please install it with `pip install boto3`." )
[docs] def get_recommendations( self, user_id: Optional[str] = None, item_id: Optional[str] = None, filter_arn: Optional[str] = None, filter_values: Optional[Mapping[str, str]] = None, num_results: Optional[int] = 10, context: Optional[Mapping[str, str]] = None, promotions: Optional[Sequence[Mapping[str, Any]]] = None, metadata_columns: Optional[Mapping[str, Sequence[str]]] = None, **kwargs: Any, ) -> Mapping[str, Any]: """Get recommendations from Amazon Personalize service. See more details at: https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html Args: user_id: str, Optional: The user identifier for which to retrieve recommendations item_id: str, Optional: The item identifier for which to retrieve recommendations filter_arn: str, Optional: The ARN of the filter to apply to the returned recommendations filter_values: Mapping, Optional: The values to use when filtering recommendations. num_results: int, Optional: Default=10: The number of results to return context: Mapping, Optional: The contextual metadata to use when getting recommendations promotions: Sequence, Optional: The promotions to apply to the recommendation request. metadata_columns: Mapping, Optional: The metadata Columns to be returned as part of the response. Returns: response: Mapping[str, Any]: Returns an itemList and recommendationId. Example: .. code-block:: python personalize_client = AmazonPersonalize(campaignArn='<my-campaign-arn>' )\n response = personalize_client.get_recommendations(user_id="1") """ if not user_id and not item_id: raise ValueError("One of user_id or item_id is required") if filter_arn: kwargs["filterArn"] = filter_arn if filter_values: kwargs["filterValues"] = filter_values if user_id: kwargs["userId"] = user_id if num_results: kwargs["numResults"] = num_results if context: kwargs["context"] = context if promotions: kwargs["promotions"] = promotions if item_id: kwargs["itemId"] = item_id if metadata_columns: kwargs["metadataColumns"] = metadata_columns if self.campaign_arn: kwargs["campaignArn"] = self.campaign_arn if self.recommender_arn: kwargs["recommenderArn"] = self.recommender_arn return self.client.get_recommendations(**kwargs)
[docs] def get_personalized_ranking( self, user_id: str, input_list: List[str], filter_arn: Optional[str] = None, filter_values: Optional[Mapping[str, str]] = None, context: Optional[Mapping[str, str]] = None, metadata_columns: Optional[Mapping[str, Sequence[str]]] = None, **kwargs: Any, ) -> Mapping[str, Any]: """Re-ranks a list of recommended items for the given user. https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetPersonalizedRanking.html Args: user_id: str, Required: The user identifier for which to retrieve recommendations input_list: List[str], Required: A list of items (by itemId) to rank filter_arn: str, Optional: The ARN of the filter to apply filter_values: Mapping, Optional: The values to use when filtering recommendations. context: Mapping, Optional: The contextual metadata to use when getting recommendations metadata_columns: Mapping, Optional: The metadata Columns to be returned as part of the response. Returns: response: Mapping[str, Any]: Returns personalizedRanking and recommendationId. Example: .. code-block:: python personalize_client = AmazonPersonalize(campaignArn='<my-campaign-arn>' )\n response = personalize_client.get_personalized_ranking(user_id="1", input_list=["123,"256"]) """ if filter_arn: kwargs["filterArn"] = filter_arn if filter_values: kwargs["filterValues"] = filter_values if user_id: kwargs["userId"] = user_id if input_list: kwargs["inputList"] = input_list if context: kwargs["context"] = context if metadata_columns: kwargs["metadataColumns"] = metadata_columns kwargs["campaignArn"] = self.campaign_arn return self.client.get_personalized_ranking(kwargs)