$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
type: command

tags:
    Preview: ""

version: 0.0.77
name: llm_rag_register_mlindex_asset
display_name: LLM - Register Vector Index Asset
is_deterministic: true

description: |
  Registers a MLIndex yaml and supporting files as an AzureML data asset

inputs:
  storage_uri:
    type: uri_folder
    mode: direct
    description: Folder containing MLIndex to be registered.
  asset_name:
    type: string
    optional: true
    description: "Name of the MLIndex registered dataset"

outputs:
  asset_id:
    type: uri_file
    description: "Asset ID of the newly created data asset"

environment: azureml:llm-rag-embeddings@latest
code: '../src'
command: >-
  python -m azureml.rag.tasks.register_mlindex
  --storage-uri '${{inputs.storage_uri}}'
  $[[--asset-name '${{inputs.asset_name}}']]
  --output-asset-id ${{outputs.asset_id}}
