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AWS for Query.Farm

aws.qconnect.get_recommendations

Example SQL Queries

SELECT * FROM
aws.qconnect.get_recommendations
WHERE
"assistant_id" = 'VALUE'
AND "session_id" = 'VALUE';

Description

This API will be discontinued starting June 1, 2024. To receive generative responses after March 1, 2024, you will need to create a new Assistant in the Amazon Connect console and integrate the Amazon Q in Connect JavaScript library (amazon-q-connectjs) into your applications.

Retrieves recommendations for the specified session. To avoid retrieving the same recommendations in subsequent calls, use NotifyRecommendationsReceived. This API supports long-polling behavior with the waitTimeSeconds parameter. Short poll is the default behavior and only returns recommendations already available. To perform a manual query against an assistant, use QueryAssistant.

Table Definition

Column NameColumn Data Type
assistant_id Required Input Column

The identifier of the Amazon Q in Connect assistant. Can be either the ID or the ARN. URLs cannot contain the ARN.

VARCHAR
session_id Required Input Column

The identifier of the session. Can be either the ID or the ARN. URLs cannot contain the ARN.

VARCHAR
max_results Input Column

The maximum number of results to return per page.

BIGINT
wait_time_seconds Input Column

The duration (in seconds) for which the call waits for a recommendation to be made available before returning. If a recommendation is available, the call returns sooner than WaitTimeSeconds. If no messages are available and the wait time expires, the call returns successfully with an empty list.

BIGINT
_aws_profile Input Column

The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role.

STRUCT(
    "type" VARCHAR,
    "name" VARCHAR,
    "account_id" VARCHAR,
    "via_profile_name" VARCHAR,
    "assumed_role_arn" VARCHAR,
    "organization" STRUCT(
        "account_name" VARCHAR,
        "id" VARCHAR,
        "tags" STRUCT(
            "key" VARCHAR,
            "value" VARCHAR
        )[],
        "master_account" STRUCT(
            "id" VARCHAR,
            "email" VARCHAR
        ),
        "parents" STRUCT(
            "type" VARCHAR,
            "id" VARCHAR,
            "name" VARCHAR,
            "tags" STRUCT(
                "key" VARCHAR,
                "value" VARCHAR
            )[]
        )[]
    )
)
Show child fields
_aws_profile.account_id

The AWS account id

_aws_profile.assumed_role_arn

The ARN of the assumed role

_aws_profile.name

The unique name of the profile.

_aws_profile.organization

Information about this profile's membership in the AWS organization.

Show child fields
_aws_profile.organization.account_name

The name of account speciifed by the organization

_aws_profile.organization.id

The organization id

_aws_profile.organization.master_account
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_aws_profile.organization.master_account.email

The organization master account email address

_aws_profile.organization.master_account.id

The organization master account id

_aws_profile.organization.parents[]
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_aws_profile.organization.parents[].id

The id of the parent

_aws_profile.organization.parents[].name

The name of the parent

_aws_profile.organization.parents[].tags[]
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_aws_profile.organization.parents[].tags[].key
_aws_profile.organization.parents[].tags[].value
_aws_profile.organization.parents[].type

The type of parent can be an organization unit or a root

_aws_profile.organization.tags[]
Show child fields
_aws_profile.organization.tags[].key
_aws_profile.organization.tags[].value
_aws_profile.type

The type of profile, either 'credentials' or 'assumed_role'

_aws_profile.via_profile_name

This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.

recommendations

The recommendations.

STRUCT(
    "data" STRUCT(
        "details" STRUCT(
            "content_data" STRUCT(
                "ranking_data" STRUCT(
                    "relevance_level" VARCHAR,
                    "relevance_score" DOUBLE
                ),
                "text_data" STRUCT(
                    "excerpt" STRUCT(
                        "highlights" STRUCT(
                            "begin_offset_inclusive" BIGINT,
                            "end_offset_exclusive" BIGINT
                        )[],
                        "text" VARCHAR
                    ),
                    "title" STRUCT(
                        "highlights" STRUCT(
                            "begin_offset_inclusive" BIGINT,
                            "end_offset_exclusive" BIGINT
                        )[],
                        "text" VARCHAR
                    )
                )
            ),
            "generative_data" STRUCT(
                "completion" VARCHAR,
                "ranking_data" STRUCT(
                    "relevance_level" VARCHAR,
                    "relevance_score" DOUBLE
                )
            ),
            "source_content_data" STRUCT(
                "id" VARCHAR,
                "ranking_data" STRUCT(
                    "relevance_level" VARCHAR,
                    "relevance_score" DOUBLE
                ),
                "text_data" STRUCT(
                    "excerpt" STRUCT(
                        "highlights" STRUCT(
                            "begin_offset_inclusive" BIGINT,
                            "end_offset_exclusive" BIGINT
                        )[],
                        "text" VARCHAR
                    ),
                    "title" STRUCT(
                        "highlights" STRUCT(
                            "begin_offset_inclusive" BIGINT,
                            "end_offset_exclusive" BIGINT
                        )[],
                        "text" VARCHAR
                    )
                ),
                "type" VARCHAR
            )
        ),
        "reference" STRUCT(
            "content_reference" STRUCT(
                "content_arn" VARCHAR,
                "content_id" VARCHAR,
                "knowledge_base_arn" VARCHAR,
                "knowledge_base_id" VARCHAR
            ),
            "generative_reference" STRUCT(
                "generation_id" VARCHAR,
                "model_id" VARCHAR
            )
        )
    ),
    "document" STRUCT(
        "content_reference" STRUCT(
            "content_arn" VARCHAR,
            "content_id" VARCHAR,
            "knowledge_base_arn" VARCHAR,
            "knowledge_base_id" VARCHAR
        ),
        "excerpt" STRUCT(
            "highlights" STRUCT(
                "begin_offset_inclusive" BIGINT,
                "end_offset_exclusive" BIGINT
            )[],
            "text" VARCHAR
        ),
        "title" STRUCT(
            "highlights" STRUCT(
                "begin_offset_inclusive" BIGINT,
                "end_offset_exclusive" BIGINT
            )[],
            "text" VARCHAR
        )
    ),
    "recommendation_id" VARCHAR,
    "relevance_level" VARCHAR,
    "relevance_score" DOUBLE,
    "type" VARCHAR
)[]
Show child fields
recommendations[]
Show child fields
recommendations[].data

Summary of the recommended content.

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recommendations[].data.details

Details about the data.

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recommendations[].data.details.content_data

Details about the content data.

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recommendations[].data.details.content_data.ranking_data

Details about the content ranking data.

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recommendations[].data.details.content_data.ranking_data.relevance_level

The relevance score of the content.

recommendations[].data.details.content_data.ranking_data.relevance_score

The relevance level of the recommendation.

recommendations[].data.details.content_data.text_data

Details about the content text data.

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recommendations[].data.details.content_data.text_data.excerpt

The text of the document.

Show child fields
recommendations[].data.details.content_data.text_data.excerpt.highlights[]
Show child fields
recommendations[].data.details.content_data.text_data.excerpt.highlights[].begin_offset_inclusive

The offset for the start of the highlight.

recommendations[].data.details.content_data.text_data.excerpt.highlights[].end_offset_exclusive

The offset for the end of the highlight.

recommendations[].data.details.content_data.text_data.excerpt.text

Text in the document.

recommendations[].data.details.content_data.text_data.title

The text of the document.

Show child fields
recommendations[].data.details.content_data.text_data.title.highlights[]
Show child fields
recommendations[].data.details.content_data.text_data.title.highlights[].begin_offset_inclusive

The offset for the start of the highlight.

recommendations[].data.details.content_data.text_data.title.highlights[].end_offset_exclusive

The offset for the end of the highlight.

recommendations[].data.details.content_data.text_data.title.text

Text in the document.

recommendations[].data.details.generative_data

Details about the generative data.

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recommendations[].data.details.generative_data.completion

The LLM response.

recommendations[].data.details.generative_data.ranking_data

Details about the generative content ranking data.

Show child fields
recommendations[].data.details.generative_data.ranking_data.relevance_level

The relevance score of the content.

recommendations[].data.details.generative_data.ranking_data.relevance_score

The relevance level of the recommendation.

recommendations[].data.details.source_content_data

Details about the content data.

Show child fields
recommendations[].data.details.source_content_data.id

The identifier of the source content.

recommendations[].data.details.source_content_data.ranking_data

Details about the source content ranking data.

Show child fields
recommendations[].data.details.source_content_data.ranking_data.relevance_level

The relevance score of the content.

recommendations[].data.details.source_content_data.ranking_data.relevance_score

The relevance level of the recommendation.

recommendations[].data.details.source_content_data.text_data

Details about the source content text data.

Show child fields
recommendations[].data.details.source_content_data.text_data.excerpt

The text of the document.

Show child fields
recommendations[].data.details.source_content_data.text_data.excerpt.highlights[]
Show child fields
recommendations[].data.details.source_content_data.text_data.excerpt.highlights[].begin_offset_inclusive

The offset for the start of the highlight.

recommendations[].data.details.source_content_data.text_data.excerpt.highlights[].end_offset_exclusive

The offset for the end of the highlight.

recommendations[].data.details.source_content_data.text_data.excerpt.text

Text in the document.

recommendations[].data.details.source_content_data.text_data.title

The text of the document.

Show child fields
recommendations[].data.details.source_content_data.text_data.title.highlights[]
Show child fields
recommendations[].data.details.source_content_data.text_data.title.highlights[].begin_offset_inclusive

The offset for the start of the highlight.

recommendations[].data.details.source_content_data.text_data.title.highlights[].end_offset_exclusive

The offset for the end of the highlight.

recommendations[].data.details.source_content_data.text_data.title.text

Text in the document.

recommendations[].data.details.source_content_data.type

The type of the source content.

recommendations[].data.reference

Reference information about the content.

Show child fields
recommendations[].data.reference.content_reference

Reference information about the content.

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recommendations[].data.reference.content_reference.content_arn

The Amazon Resource Name (ARN) of the content.

recommendations[].data.reference.content_reference.content_id

The identifier of the content.

recommendations[].data.reference.content_reference.knowledge_base_arn

The Amazon Resource Name (ARN) of the knowledge base.

recommendations[].data.reference.content_reference.knowledge_base_id

The identifier of the knowledge base. This should not be a QUICK_RESPONSES type knowledge base.

recommendations[].data.reference.generative_reference

Reference information about the generative content.

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recommendations[].data.reference.generative_reference.generation_id

The identifier of the LLM model.

recommendations[].data.reference.generative_reference.model_id

The identifier of the LLM model.

recommendations[].document

The recommended document.

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recommendations[].document.content_reference

A reference to the content resource.

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recommendations[].document.content_reference.content_arn

The Amazon Resource Name (ARN) of the content.

recommendations[].document.content_reference.content_id

The identifier of the content.

recommendations[].document.content_reference.knowledge_base_arn

The Amazon Resource Name (ARN) of the knowledge base.

recommendations[].document.content_reference.knowledge_base_id

The identifier of the knowledge base. This should not be a QUICK_RESPONSES type knowledge base.

recommendations[].document.excerpt

The excerpt from the document.

Show child fields
recommendations[].document.excerpt.highlights[]
Show child fields
recommendations[].document.excerpt.highlights[].begin_offset_inclusive

The offset for the start of the highlight.

recommendations[].document.excerpt.highlights[].end_offset_exclusive

The offset for the end of the highlight.

recommendations[].document.excerpt.text

Text in the document.

recommendations[].document.title

The title of the document.

Show child fields
recommendations[].document.title.highlights[]
Show child fields
recommendations[].document.title.highlights[].begin_offset_inclusive

The offset for the start of the highlight.

recommendations[].document.title.highlights[].end_offset_exclusive

The offset for the end of the highlight.

recommendations[].document.title.text

Text in the document.

recommendations[].recommendation_id

The identifier of the recommendation.

recommendations[].relevance_level

The relevance level of the recommendation.

recommendations[].relevance_score

The relevance score of the recommendation.

recommendations[].type

The type of recommendation.

triggers

The triggers corresponding to recommendations.

STRUCT(
    "data" STRUCT(
        "query" STRUCT(
            "text" VARCHAR
        )
    ),
    "id" VARCHAR,
    "recommendation_ids" VARCHAR[],
    "source" VARCHAR,
    "type" VARCHAR
)[]
Show child fields
triggers[]
Show child fields
triggers[].data

A union type containing information related to the trigger.

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triggers[].data.query

Data associated with the QUERY RecommendationTriggerType.

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triggers[].data.query.text

The text associated with the recommendation trigger.

triggers[].id

The identifier of the recommendation trigger.

triggers[].recommendation_ids[]
triggers[].source

The source of the recommendation trigger.

  • ISSUE_DETECTION: The corresponding recommendations were triggered by a Contact Lens issue.

  • RULE_EVALUATION: The corresponding recommendations were triggered by a Contact Lens rule.

triggers[].type

The type of recommendation trigger.