How to Handle High-Cardinality Column Filtering in Pandas AI with Large Datasets?

[akhilesh-chander]

I'm utilizing Pandas AI to perform natural language queries on large DataFrames (e.g., 100,000+ rows, 20+ columns). One significant challenge I'm encountering is filtering on high-cardinality columns, such as product, which can have thousands of unique values.

Due to the dataset's size, it's impractical to pass the entire DataFrame or all possible filter values to the language model. Consequently, when I execute queries like:

"Show sales for Product X in January"
Pandas AI often:

Applies filters that don't match any records, resulting in empty outputs.

Selects incorrect or irrelevant filters due to limited context.

Fails silently, leading to misleading or zero-result responses.

his behavior undermines the reliability of natural language querying in large-scale, real-world data scenarios.

Question:
How can I effectively manage filtering on high-cardinality columns in Pandas AI when working with large datasets? Specifically:

• Are there methods to inject valid value lists (e.g., known products) without exceeding context limits?
• Can I implement callbacks or hooks to validate or override filters generated by Pandas AI before execution?
• What are best practices for prompt engineering to guide the model toward accurate filtering?
• Are there any plans or existing solutions to enhance high-cardinality column support in Pandas AI?

Any insights or suggestions would be greatly appreciated.