An embedding pipeline is the end-to-end process that takes raw source data and turns it into searchable vectors inside a database. It typically chains together several stages: loading and cleaning the source data, splitting it into chunks, running each chunk through an embedding model, attaching metadata, and inserting the resulting vectors into the index.
Building and maintaining this pipeline is a significant part of the engineering work behind any retrieval system — and a major source of AI plumbing. Each stage has decisions and failure modes: how to chunk, which model to call, how to handle rate limits and retries, how to keep the index in sync as source data changes, and how to re-embed everything if you switch models.
Because this work is repetitive, many vector databases now offer integrated vectorization, where the database handles embedding generation automatically on ingest. This collapses much of the pipeline into the database itself, letting teams send raw text and let the system produce and store the vectors, removing a large chunk of custom plumbing.