An embedding pipeline turns a large, changing corpus into vectors in a search index, and at scale it becomes an engineering problem, not a one-off…
See why embedding a real corpus is an ongoing system.
Embedding a handful of documents is a script. Embedding millions, that keep changing, is a system. At scale you face throughput limits (embedding millions of chunks takes real time and money), a corpus that updates constantly (new docs, edits, deletions), and the need to keep the search index consistent with the source of truth.
So an embedding pipeline is a continuous process, not a one-time job: it ingests documents, chunks them, embeds the chunks in bulk, writes them to the index, and repeats as data changes. Designing it well is what keeps retrieval fast, fresh, and affordable.
Embed fast and cheaply with batching and parallelism.
The first lever is batching: send many texts per embedding call instead of one at a time. Embedding models and hardware are far more efficient in batches, cutting both per-item overhead and cost dramatically. Tune the batch size to the model and memory.
Then parallelize: run multiple workers embedding different shards of the corpus at once, respecting provider rate limits or your own GPU capacity. For an initial bulk load of millions of chunks, batching plus parallel workers is the difference between hours and days.
Keep the index current without re-embedding everything.
Re-embedding the whole corpus on every change is wasteful. Instead, update incrementally: detect which documents are new, changed, or deleted — often via content hashes or update timestamps — and embed only those, adding or replacing their vectors and removing vectors for deleted content.
Keeping the index in sync with the source of truth is the hard part. Deletions must propagate so stale vectors don't surface in results, and updates must replace old vectors rather than duplicate them. A common pattern triggers the pipeline from a change feed or runs it on a schedule that diffs against what's already indexed.
Design updates to be idempotent — re-running the pipeline on the same document should not create duplicates. Key each vector by a stable document/chunk id so a re-run overwrites rather than appends.
Plan for model changes and avoid the scaling traps.
One event forces a full rebuild: changing the embedding model (or the chunking). Vectors from different models aren't comparable, so the entire corpus must be re-embedded and the index rebuilt — a costly operation you should plan for. A safe approach builds the new index alongside the old and swaps over once it's ready, so search stays up.
Because re-embedding is expensive, choose your embedding model and dimensions deliberately up front, and store enough metadata (source ids, model version) to manage migrations.
Watch for: embedding one item per call instead of batching (slow and expensive); re-embedding everything on each run instead of incrementally; letting deletions leave orphaned vectors that pollute results; non-idempotent updates that duplicate vectors; and not tracking which model version produced the index, so a model change becomes chaos. Treat the pipeline as production software with monitoring and retries.
An embedding pipeline turns a large, changing corpus into indexed vectors as an ongoing system. Throughput comes from batching many texts per call and parallel workers. Keep the index fresh with incremental, idempotent updates — embed only new or changed docs, propagate deletions, and key vectors by stable ids to avoid duplicates. Plan for the costly re-embed a model or chunking change forces, ideally building a new index in parallel and swapping. Track the model version and treat the pipeline as production software.
Design an embedding pipeline for 5 million document chunks that change daily. Describe how you'd bulk-embed the initial load, how you'd handle daily additions, edits, and deletions without duplicates, and your plan for the day you switch embedding models.
Why is embedding a large, changing corpus a pipeline rather than a script?
Millions of changing chunks require continuous ingestion, bulk embedding, and index synchronization, which is a system-level concern.
What is the main lever for embedding throughput?
Batching and parallelism turn an hours-or-days bulk embed into an efficient operation by using hardware and rate limits fully.
How should an embedding pipeline handle changing source data?
Incremental, idempotent updates keyed by stable ids avoid wasteful re-embedding and prevent duplicate or orphaned vectors.
What forces a full re-embed of the corpus?
A model or chunking change invalidates existing vectors, requiring a rebuild; building alongside the old index and swapping keeps search available.