Embeddings and Guardrails for Classifying Supplier Invoice Fields in Node.js

The article explains how to classify supplier invoice fields by comparing new text against labeled examples using embedding similarity. It recommends accepting a label only when the similarity score clears a threshold and beats the runner-up by a set margin, otherwise routing the item to human review. To control costs, it suggests caching reference embeddings and embedding each input once per attempt, while noting that the vectors serve as evidence rather than a final verdict. For auditability, it advises storing a logical key that prevents retries from creating duplicate decision records, along with a compact set of metadata and a text digest. It also discusses retention trade-offs, since raw invoice text may expire while scores and digests remain.