FastRAG/blueprint.yaml at master · cnvrg/FastRAG · GitHub

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---
title: FastRAG
version: 1.0.0
description: "This blueprint enables using RAG with ElasticSearch and MinIO"
long_description: "This blueprint uses Elasticsearch as document store and MinIO for updating the document store for RAG. You can connect with external LLM for content generation."
author: "cnvrg"
author_email: "libhub@cnvrg.io"

# At least one tag [inference, training, data] must be present
tags:
  - training

# List of tasks (libraries)
tasks:
  - title: Listen
    top: 300
    left: 0

    # Type must be according to the flow task conventions (data, exec, deploy)
    type: exec

    # The library data
    library: listener
    library_version: 1.0.0

    # The flattened library parameters (in this case we have training library)
    command: python updator.py

    requirements:
      cpu: 3.5
      memory: 8
      gpu: 0
      hpu: 0

    image: cnvrg:v6.0
    language: python3

    # The "arguments" property changes to "params", rest remains the same.
    # Params in the blueprint yaml can be used to override the arguments in the library.
    params:
      - key: storage_solution
        type: 'categorical'
        values:
          - 's3'
      - key: bucket_name
        type: 'categorical'
        values:
          - ''
      - key: index
        type: 'categorical'
        values:
          - ''
      - key: port
        type: 'categorical'
        values:
          - ''
      - key: host
        type: 'categorical'
        values:
          - ''
      - key: api_link
        type: 'categorical'
        values:
          - ''
      - key: queue_url
        type: 'categorical'
        values:
          - ''
      - key: region_name
        type: 'categorical'
        values:
          - ''

      - key: scheme
        type: 'categorical'
        values:
          - ''


  - title: Inference
    top: 400
    left: 500

    type: deploy

    library: rag-endpoint
    library-version: 1.0.0

    kind: webservice

    requirements:
      cpu: 3.5
      memory: 8
      gpu: 0
      hpu: 0

    image: cnvrg:v6.0
    language: python3

    accept_files: false

    file_name: predict.py
    function_name: query
    prep_file: ''
    prep_function: ''
    input_example:
      query: text
    input_schema:
      query: text
    output_schema: {}


relations: []