version: 2

models:
  - name: < model name >
    columns:
      - name: < column name >
        tests:
          - elementary.unstructured_data_validation:
              expectation_prompt: "Description of what the text should contain or represent"
              llm_model_name: "model_name"
Beta Feature: Unstructured data validation tests is currently in beta. The functionality and interface may change in future releases.Version Requirement: This feature requires Elementary dbt package version 0.18.0 or above.

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Validating Unstructured Data with Elementary

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What is Unstructured Data Validation?

Elementary’s elementary.unstructured_data_validation test allows you to validate unstructured data using AI and LLM language models. Instead of writing complex code, you can simply describe what you expect from your data in plain English, and Elementary will check if your data meets those expectations. For example, you can verify that customer feedback comments are in English, product descriptions contain required information, or support tickets follow a specific format or a sentiment.

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How It Works

Elementary leverages the AI and LLM capabilities built directly into your data warehouse. When you run a validation test:
  1. Your unstructured data stays within your data warehouse
  2. The warehouse’s built-in AI and LLM functions analyze the data
  3. Elementary reports whether each text value meets your expectations

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Required Setup for Each Data Warehouse

Before you can use Elementary’s unstructured data validations, you need to set up AI and LLM capabilities in your data warehouse:

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Snowflake

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Databricks

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BigQuery

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Redshift

  • Support coming soon

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Data Lakes

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Using the Validation Test

The test requires two main parameters:
  • expectation_prompt: Describe what you expect from the text in plain English
  • llm_model_name: Specify which AI model to use (see recommendations above for each warehouse)
This test works with any column containing unstructured text data such as descriptions, comments, or other free-form text fields. It can also be applied to structured columns that can be converted to strings, enabling natural language data validations.
version: 2

models:
  - name: < model name >
    columns:
      - name: < column name >
        tests:
          - elementary.unstructured_data_validation:
              expectation_prompt: "Description of what the text should contain or represent"
              llm_model_name: "model_name"

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Usage Examples

Here are some powerful ways you can apply unstructured data validations:

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Validating Structure

models:
  - name: medicine_prescriptions
    description: "A table containing medicine prescriptions."
    columns:
      - name: doctor_notes
        description: "A column containing the doctor notes on the prescription"
        tests:
          - elementary.unstructured_data_validation:
              expectation_prompt: "The prescription has to include a limited time period and recommendations to the patient"
              llm_model_name: "claude-3-5-sonnet"
Test fails if: A doctor’s note does not specify a time period or lacks recommendations for the patient.

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Validating Sentiment

models:
  - name: customer_feedback
    description: "A table containing customer feedback."
    columns:
      - name: negative_feedbacks
        description: "A column containing negative feedbacks about our product."
        tests:
          - elementary.unstructured_data_validation:
              expectation_prompt: "The customer feedback's sentiment has to be negative"
              llm_model_name: "claude-3-5-sonnet"
Test fails if: Any feedback in negative_feedbacks is not actually negative.

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Validating Similarities
Coming Soon

models:
  - name: summerized_pdfs
    description: "A table containing a summary of our ingested PDFs."
    columns:
      - name: pdf_summary
        description: "A column containing the main PDF's content summary."
        tests:
          - elementary.validate_similarity:
              to: ref('pdf_source_table')
              column: pdf_content
              match_by: pdf_name
Test fails if: A PDF summary does not accurately represent the original PDF’s content. The validation will use the pdf name as the key to match a summary from the pdf_summary table to the pdf_content in the pdf_source_table. 
models:
  - name: jobs
    columns:
      - name: job_title
        tests:
          - elementary.validate_similarity:
              column: job_description
Test fails if: The job title does not align with the job description.

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Accepted Categories
Coming Soon

models:
  - name: support_tickets
    description: "A table containing customer support tickets."
    columns:
      - name: issue_description
        description: "A column containing customer-reported issues."
        tests:
          - elementary.accepted_categories:
              categories: ['billing', 'technical_support', 'account_access', 'other']
Test fails if: A support ticket does not fall within the predefined categories.

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Accepted Entities
Coming Soon

models:
  - name: news_articles
    description: "A table containing news articles."
    columns:
      - name: article_text
        description: "A column containing full article text."
        tests:
          - elementary.extract_and_validate_entities:
              entities:
                organization: 
                  required: true
                  accepted_values: ['Google', 'Amazon', 'Microsoft', 'Apple']
                location:
                  required: false
                  accepted_values: {{ run_query('select zip_code from locations') }}
Test fails if:
  • The required entity (e.g., organization) is missing.
  • Extracted entities do not match the expected values.

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Compare Numeric Values
Coming Soon

models:
  - name: board_meeting_summaries
    description: "A table containing board meeting summary texts."
    columns:
      - name: meeting_notes
        description: "A column containing the full summary of the board meeting."
        tests:
          - elementary.extract_and_validate_numbers:
              entities:
                revenue:
                  compare_with: ref('crm_financials')
                  column: sum(revenue)
                  required: true
                net_profit:
                  compare_with: ref('crm_financials')
                  column: sum(net_profit)
                customer_count:
                  compare_with: ref('crm_customers')
                  column: count(customers)
                  required: true
Test fails if:
  • Required entities are missing
  • The numerical entities do not match the structured CRM data