Schema-aware chunking beats flattening for JSON RAG

The interesting part here is not Chroma itself but the ingestion design problem: naive flattening usually throws away structure, while schema-aware chunking preserves the relationships retrieval actually needs.

• –Chroma is built for embeddings plus metadata filtering, so JSON records usually work best when transformed into semantically meaningful slices instead of dumped in raw
• –The poster’s success with key-wise chunking matches a common RAG pattern: chunk by logical entity or field group, not by arbitrary token windows
• –Flattening can help for normalization, but doing it blindly on heterogeneous schemas often destroys parent-child context and makes retrieval noisier
• –Varying schemas across many files point toward a preprocessing layer that maps each source into a consistent intermediate shape before embedding
• –For AI developers, this is a practical reminder that retrieval quality is often won or lost in data modeling long before vector search enters the loop