a dg@s.ddlmZmZmZmZmZGdddZdS))AnyListMappingOptionalSequencec @seZdZdZd eeeeeeeeeedddZdeeeeeeeeeefee eeeefee eeefeeee efeeeefd dd Z dee eeeeeeefeeeefeeee efeeeefd d d Z dS)AmazonPersonalizeaOAmazon 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='' ) N) campaign_arnrecommender_arnclientcredentials_profile_name region_namec Cs||_||_|r|rtd|s,|s,tdz|dur>||_nlddl}ddl}|durd|j|d}n|}i} |r||| d<d} |jjdd} | | d <|j| fi| |_Wnt yt d Yn0dS) NzOCannot initialize AmazonPersonalize with both campaign_arn and recommender_arn.zSCannot initialize AmazonPersonalize. Provide one of campaign_arn or recommender_arnr)Z profile_namer zpersonalize-runtimeZ langchain)Zuser_agent_extraconfigzRCould not import boto3 python package. Please install it with `pip install boto3`.) rr ValueErrorr boto3Zbotocore.configSessionr ZConfig ImportErrorModuleNotFoundError) selfrr r r r rZbotocoresessionZ client_paramsZserviceZsession_configr/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_experimental/recommenders/amazon_personalize.py__init__s: zAmazonPersonalize.__init__ ) user_iditem_id filter_arn filter_values num_resultscontext promotionsmetadata_columnskwargsreturnc Ks|s|std|r|| d<|r(|| d<|r4|| d<|r@|| d<|rL|| d<|rX|| d<|rd|| d<|rp|| d <|jr|j| d <|jr|j| d <|jjfi| S) aGet 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='' ) response = personalize_client.get_recommendations(user_id="1") z%One of user_id or item_id is required filterArn filterValuesuserIdZ numResultsrrZitemIdmetadataColumns campaignArnZrecommenderArn)rrr r get_recommendations) rrrrrrrrr r!rrrr(Ls.,  z%AmazonPersonalize.get_recommendations)r input_listrrrr r!r"cKs^|r ||d<|r||d<|r$||d<|r0||d<|r<||d<|rH||d<|j|d<|j|S)a=Re-ranks a list of recommended items for the given user. https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetPersonalizedRanking.html Args: user_id: str, Required: The user identifier for which to retrieve recommendations input_list: List[str], Required: A list of items (by itemId) to rank filter_arn: str, Optional: The ARN of the filter to apply filter_values: Mapping, Optional: The values to use when filtering recommendations. context: Mapping, Optional: The contextual metadata to use when getting recommendations metadata_columns: Mapping, Optional: The metadata Columns to be returned as part of the response. Returns: response: Mapping[str, Any]: Returns personalizedRanking and recommendationId. Example: .. code-block:: python personalize_client = AmazonPersonalize(campaignArn='' ) response = personalize_client.get_personalized_ranking(user_id="1", input_list=["123,"256"]) r#r$r%Z inputListrr&r')rr get_personalized_ranking)rrr)rrrr r!rrrr*s' z*AmazonPersonalize.get_personalized_ranking)NNNNN)NNNNrNNN)NNNN)__name__ __module__ __qualname____doc__rstrrrrintrr(rr*rrrrrs^ 5 J rN)typingrrrrrrrrrrs