Data Classification

Classification with embeddings means assigning a label — spam or not, urgent or routine, billing versus technical — by using vectors as the input features. Because an embedding already encodes meaning, a remarkably simple classifier on top of it works well: embed your labeled examples, embed the new item, and either check which labeled examples it lands nearest to (k-nearest neighbors) or train a lightweight model like logistic regression on the vectors.

This approach matters because it sits in the sweet spot between rule-based systems and LLM calls. Rules are brittle; sending every item through a large model is slow and costs tokens. Embedding-based classification is fast, cheap, and needs surprisingly little labeled data — often a few dozen examples per class — making it ideal for routing support tickets, moderating content, tagging documents by sensitivity, or deciding which prompt template an agent should use. It’s also how many production systems triage before involving an LLM at all.

The workflow you’ll build: embed a labeled dataset with any model from this module, split into train and test sets, fit a classifier in a few lines of scikit-learn (LogisticRegression().fit(X_train, y_train)), and evaluate accuracy. For the zero-training variant, embed one description per category and classify by highest cosine similarity. Both patterns reuse your existing embedding pipeline — no new infrastructure, just a different question asked of the same vectors.