In Cyyrus, the schema is the heart of the dataset generation process. It defines the structure, properties, and types of the dataset. The schema is written in YAML and is used to generate synthetic data that adheres to the specified structure.

1

Define Your Tasks

First step is to define the tasks that will be used in the dataset generation process. These tasks can include parsing, extraction, or something else.

tasks:
    # Define the Graph QA Task
    parse_graphs:
        task_type: generation # Define the type of task
        task_properties: # Incase the task requires any properties define them here
        model: gpt-4o-mini
        prompt: What are the insights from the graph
        api_key: $OPENAI_API_KEY
2

Define Your Types

Once the tasks are defined, the next step is to define the types that will be used in the dataset. These types can include objects, arrays, or any other data type.

# Define the primitive types
types:
    customer_name:
        type: string
    customer_address:
        type: string
    invoice_id:
        type: string
    total_amount:
        type: float
    paid:
        type: boolean
3

Define Your Columns

Columns are the attributes of the dataset. They can be linked to tasks to define the structure of the dataset.

# Define the columns
columns:
    # Define the parsed invoice column
    parsed_invoice:
        task_id: invoice_parsing # Associate a task_id with the column

    # Define the customer info column
    customer_info:
        task_id: extract_customer_info
        task_input: [parsed_invoice]
        # Define the input for the task.

    # Define the invoice items column
    invoice_items:
        task_id: extract_invoice_items
        task_input: [parsed_invoice]

    # Define the invoice qna column
    invoice_qna:
        task_id: create_invoice_qna
        task_input: [invoice_items, customer_info]
        # Ensures the task is executed after the task_input is available

Think of task_inputs as a way to define the order of availability of columns. If a columns requires the output of another columns, it can be defined as a task_input. Internally task_inputs are parsed as a directed acyclic graph to ensure the order of execution.

4

Define Your Dataset

Datasets are defined by the metadata, splits, attributes, and shuffle properties.

# Define the properties of the dataset
dataset:
    # Define the metadata of the dataset
    metadata:
        name: Invoice Dataset
        description: Dataset containing the invoice data
        tags: [invoice, financial, document]
        license: CC-BY-NC-SA
        languages: [en]

    # Define how to shuffle the dataset
    shuffle:
        seed: 42

    # Define the splits of the dataset
    splits:
        train: 0.8
        test: 0.2
        seed: 42

    # Define the attributes of the dataset
    attributes:
        required_columns: [invoice_items, customer_info]
        unique_columns: []
        flatten_columns: [invoice_items, invoice_qna]
        exclude_columns: [parsed_invoice]
        nulls: include