pokemon-data-pipeline

To build a Korean Pokémon JSON dataset from PokeAPI, use a Node.js 18+ fetch-based build script to extract the ko locale, normalize evolution chains, and merge species, types, and abilities into flat learning-ready JSON bundles.

The best way to consolidate PokeAPI data into a single offline JSON bundle is running a data pipeline that merges multiple endpoints, applies disk caching, and outputs normalized files under _workspace/data for large-scale offline embedding.

Yes, you need Node.js 18+ to run this data pipeline because the build script relies on the native fetch API for requests, alongside disk caching and concurrency limits with retry logic to reliably generate the JSON outputs.

Evolution-chain normalization works by fetching related PokeAPI endpoints, extracting the Korean locale with sensible fallbacks, cleaning flavor text, and restructuring the data into flat, embed-ready JSON formats for consistent IDs and labels.

Yes, you can cache PokeAPI requests locally to speed up large-scale JSON bundling by utilizing the pipeline's built-in disk caching, which prevents thousands of slow, redundant requests during dataset generation.

Your PokeAPI data pipeline might fail when fetching large numbers of Pokémon species due to rate limiting or network timeouts, which is why the build script implements concurrency limits alongside retry and backoff logic for reliable processing.