Amazon Kendra Retrieve Passages for RAG

arazzo: 1.0.1
info:
  title: Amazon Kendra Retrieve Passages for RAG
  summary: Retrieve semantically relevant passages for a question and run a parallel ranked query to enrich a RAG context.
  description: >-
    Builds the context for a retrieval-augmented-generation prompt by calling
    Retrieve to pull the most relevant text passages for an input question, then
    running a standard Query for the same text to capture ranked document results
    and any extractive answers. Combining both gives a downstream LLM both long
    passages and short ranked answers. Each step inlines its request, including
    the AWS JSON protocol X-Amz-Target header, so the flow can be read and
    executed without opening the underlying OpenAPI description.
  version: 1.0.0
sourceDescriptions:
- name: kendraApi
  url: ../openapi/amazon-kendra-openapi.yml
  type: openapi
workflows:
- workflowId: retrieve-passages-for-rag
  summary: Retrieve passages and run a ranked query for the same question.
  description: >-
    Retrieves relevant passages for an input question and runs a parallel ranked
    query, assembling both into a combined context for RAG.
  inputs:
    type: object
    required:
    - indexId
    - queryText
    properties:
      indexId:
        type: string
        description: The identifier of the index to retrieve from.
      queryText:
        type: string
        description: The question to retrieve passages and ranked results for.
      attributeFilter:
        type: object
        description: Optional attribute filter to scope results.
      pageSize:
        type: integer
        description: Optional number of results per page.
  steps:
  - stepId: retrievePassages
    description: >-
      Retrieve the most relevant text passages for the input question for use as
      grounding context in a RAG prompt.
    operationId: Retrieve
    parameters:
    - name: IndexId
      in: path
      value: $inputs.indexId
    - name: X-Amz-Target
      in: header
      value: AWSKendraFrontendService.Retrieve
    requestBody:
      contentType: application/json
      payload:
        QueryText: $inputs.queryText
        AttributeFilter: $inputs.attributeFilter
        PageSize: $inputs.pageSize
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      queryId: $response.body#/QueryId
      resultItems: $response.body#/ResultItems
  - stepId: rankedQuery
    description: >-
      Run a standard ranked query for the same question to capture extractive
      answers and ranked document results alongside the retrieved passages.
    operationId: Query
    parameters:
    - name: IndexId
      in: path
      value: $inputs.indexId
    - name: X-Amz-Target
      in: header
      value: AWSKendraFrontendService.Query
    requestBody:
      contentType: application/json
      payload:
        QueryText: $inputs.queryText
        AttributeFilter: $inputs.attributeFilter
        PageSize: $inputs.pageSize
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      queryId: $response.body#/QueryId
      resultItems: $response.body#/ResultItems
      totalResults: $response.body#/TotalNumberOfResults
  outputs:
    retrievedPassages: $steps.retrievePassages.outputs.resultItems
    rankedResults: $steps.rankedQuery.outputs.resultItems
    totalResults: $steps.rankedQuery.outputs.totalResults