Add experimental natural-language query parser prototype for malariagen_data

[CharithKalasi]

Overview

This PR introduces an experimental prototype for translating natural-language queries into structured API calls using the malariagen_data package.

The goal is to explore approaches for lowering the barrier to accessing genomic data, particularly for users without programming experience (e.g. public health practitioners and entomologists).

What’s Included

• A lightweight rule-based NLP parser for common genomic queries
• Structured query representation (ParsedQuery)
• Mapping layer to malariagen_data API calls
• Example workflows demonstrating end-to-end usage
• Basic tests covering parsing and query mapping

Motivation

The malariagen_data API provides powerful access to genomic datasets, but currently requires familiarity with Python and data structures.

This prototype explores how natural-language interfaces could:

• Simplify data access
• Enable faster insights
• Broaden usability of the platform

Example

Input:
"Show SNP frequency in Uganda"

Pipeline:
Natural language → Parsed query → API call → Result

Design Principles

• Non-intrusive: no changes to core API
• Deterministic and transparent parsing
• Easily extensible for future NLP/LLM integration

Scope

This PR is intended as an exploratory prototype.

Future Work

• Ontology-based entity normalization
• LLM-assisted parsing with guardrails
• Interactive interfaces