Cortex Analyst 의미 체계 모델 사양¶

# Name and description of the semantic model.
name: <name>
description: <string>

# A semantic model can contain one or more logical tables.
tables:

  # A logical table on top of a base table.
  - name: <name>
    description: <string>

    # The fully qualified name of the base table.
    base_table:
      database: <database>
      schema: <schema>
      table: <base table name>

    # Dimension columns in the logical table.
    dimensions:
      - name: <name>
        synonyms: <array of strings>
        description: <string>
        expr: <SQL expression>
        data_type: <data type>
        unique: <boolean>
        cortex_search_service:
          service: <string>
          literal_column: <string>
          database: <string>
          schema: <string>
        is_enum: <boolean>

    # Time dimension columns in the logical table.
    time_dimensions:
      - name: <name>
        synonyms:  <array of strings>
        description: <string>
        expr: <SQL expression>
        data_type: <data type>
        unique: <boolean>

    # Measure columns in the logical table.
    measures:
      - name: <name>
        synonyms: <array of strings>
        description: <string>
        expr: <SQL expression>
        data_type: <data type>
        default_aggregation: <aggregate function>

    # Commonly used filters over the logical table.
    filters:
      - name: <name>
        synonyms: <array of strings>
        description: <string>
        expr: <SQL expression>

# Example questions and queries that answer them
verified_queries:

# Verified Query (1 of n)
- name:                         # A descriptive name of the query.
  question:                     # The natural language question that this query answers.
  verified_at:                  # Optional: Time (in seconds since the UNIX epoch, January 1, 1970) when the query was verified.
  verified_by:                  # Optional: Name of the person who verified the query.
  use_as_onboarding_question:   # Optional: Marks this question as an onboarding question for the end user.
  sql:                          # The SQL query for answering the question.

# Name and description of the semantic model.
name: Sales Data
description: This semantic model can be used for asking questions over the sales data.

# A semantic model can contain one or more tables.
tables:

  # A logical table on top of the 'sd_data' base table.
  - name: sales_data
    description: A logical table capturing daily sales information across different store locations and product categories.

    # The fully qualified name of the base table.
    base_table:
      database: sales
      schema: public
      table: sd_data

    # Dimension columns in the logical table.
    dimensions:
      - name: product_category
        synonyms:
          - "item_category"
          - "product_type"
        description: The category of the product sold.
        expr: cat
        data_type: NUMBER
        unique: false
        sample_values:
          - "501"
          - "544"

      - name: store_country
        description: The country where the sale took place.
        expr: cntry
        data_type: TEXT
        unique: false
        sample_values:
          - "USA"
          - "GBR"

      - name: sales_channel
        synonyms:
          - "channel"
          - "distribution_channel"
        description: The channel through which the sale was made.
        expr: chn
        data_type: TEXT
        unique: false
        sample_values:
          - "FB"
          - "GOOGLE"

    # Time dimension columns in the logical table.
    time_dimensions:
      - name: sale_timestamp
        synonyms:
          - "time_of_sale"
          - "transaction_time"
        description: The time when the sale occurred. In UTC.
        expr: dt
        data_type: TIMESTAMP
        unique: false

    # Measure columns in the logical table.
    measures:
      - name: sales_amount
        synonyms:
          - "revenue"
          - "total_sales"
        description: The total amount of money generated from the sale.
        expr: amt
        data_type: NUMBER
        default_aggregation: sum

      - name: sales_tax
        description: The sales tax paid for this sale.
        expr: amt * 0.0975
        data_type: NUMBER
        default_aggregation: sum

      - name: units_sold
        synonyms:
          - "quantity_sold"
          - "number_of_units"
        description: The number of units sold in the transaction.
        expr: unts
        data_type: NUMBER
        default_aggregation: sum

      - name: cost
        description: The cost of the product sold.
        expr: cst
        data_type: NUMBER
        default_aggregation: sum

      - name: profit
        synonyms:
          - "earnings"
          - "net income"
        description: The profit generated from a sale.
        expr: amt - cst
        data_type: NUMBER
        default_aggregation: sum

    # A table can define commonly used filters over it. These filters can then be referenced in user questions directly.
    filters:
      - name: north_america
        synonyms:
          - "North America"
          - "N.A."
          - "NA"
        description: "A filter to restrict only to north american countries"
        expr: cntry IN ('canada', 'mexico', 'usa')