marcelroed/gigatoken

Embedding pipelines do not feed whole documents straight into the model. They first use the embedding model’s tokenizer to measure and split text into token-bounded, overlapping chunks, then embed each chunk and store the vectors. Tokenizer must match the model. Tools like marcelroed/gigatoken accelerate this preparation at corpus scale but do not create embeddings themselves.