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

tags:
    Preview: ""

version: 0.0.73
name: llm_rag_generate_embeddings
display_name: LLM - Generate Embeddings
is_deterministic: true

description: |
  Generates embeddings vectors for data chunks read from `chunks_source`.

  `chunks_source` is expected to contain `csv` files containing two columns:
  - "Chunk" - Chunk of text to be embedded
  - "Metadata" - JSON object containing metadata for the chunk

  If `embeddings_container` is supplied, input chunks are compared to existing chunks in the Embeddings Container and only changed/new chunks are embedded, existing chunks being reused.

inputs:
  chunks_source:
    type: uri_folder
    description: "Folder containing chunks to be embedded."
  # If adding to previously generated Embeddings
  embeddings_container:
    type: uri_folder
    optional: true
    mode: direct
    description: "Folder containing previously generated embeddings. Should be parent folder of the 'embeddings' output path used for for this component. Will compare input data to existing embeddings and only embed changed/new data, reusing existing chunks."
  # Embeddings settings
  embeddings_model:
    type: string
    optional: True
    description: "The model to use to embed data. E.g. 'hugging_face://model/sentence-transformers/all-mpnet-base-v2' or 'azure_open_ai://deployment/{deployment_name}/model/{model_name}'"
  batch_size:
    type: integer
    default: 100
    description: "Batch size to use when embedding data"
  num_workers:
    type: integer
    default: -1
    description: "Number of workers to use when embedding data. -1 means use half all available CPUs"

  deployment_validation:
    type: uri_file
    description: "Uri file containing information on if the Azure OpenAI deployments, if used, have been validated"
    optional: True

outputs:
  embeddings:
    type: uri_folder
    description: "Where to save data with embeddings. This should be a subfolder of previous embeddings if supplied, typically named using '${name}'. e.g. /my/prev/embeddings/${name}"
    mode: rw_mount

environment: azureml:llm-rag-embeddings@latest
code: '../src'

command: >-
  python -m azureml.rag.tasks.embed
  --chunks_source '${{inputs.chunks_source}}'
  $[[--embeddings_model ${{inputs.embeddings_model}}]]
  $[[--embeddings_container '${{inputs.embeddings_container}}']]
  --output '${{outputs.embeddings}}'
  --batch_size ${{inputs.batch_size}}
  --num_workers ${{inputs.num_workers}}
