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The semantic data layer is an experimental feature, suggested to advanced users.
PandasAI 3.0 introduces a new feature: the semantic layer, which allows you to turn raw data into semantic-enhanced and clean dataframes, making it easier to work with and analyze your data.

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What’s the Semantic Layer?

The semantic layer allows you to turn raw data into dataframes you can ask questions to as conversational AI dashboards. It serves several important purposes:
  1. Data configuration: Define how your data should be loaded and processed
  2. Semantic information: Add context and meaning to your data columns
  3. Data transformation: Specify how data should be cleaned and transformed

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How to start using the Semantic Layer?

In order to use the semantic layer, you need to create a new schema for each dataset you want to work with. If you want to learn more about how to create a semantic layer schema, check out how to create a semantic layer schema.