Batching embeddings for ingestion
Producing embeddings one item at a time is slow and underuses GPU/CPU. Batching means sending multiple texts (or images) to the embedding model in one forward pass and writing multiple vectors to the vector database in one request, which improves throughput and often reduces cost. Pipeline design: read batch → embed batch → write batch.
Summary
- Batch embedding calls (many texts per forward pass) and batch VDB writes (bulk upsert) to improve throughput and reduce cost.
- Batch size limited by model context, GPU memory, API limits; for long docs chunk then batch at chunk level.
- Pipeline: read batch → embed batch → write batch; for streaming use small batches (e.g. 8–32) for latency.
- Batching does not change embedding quality; use same tokenization in batched and single-item flows. Variable-length: pad to max in batch or use dynamic padding.
- Use bulk upsert APIs for the VDB to reduce round-trips and let the DB optimize index updates.
Batching embedding and VDB writes
Embedding APIs and local models both benefit from batching: instead of 1,000 requests of one sentence each, send 50 requests of 20 sentences (or whatever the model’s max batch size allows). GPU utilization goes up and latency per embedding goes down. Batch size is limited by model context length and GPU memory—tune so that you don’t OOM or hit API limits. For very long documents, you may still chunk first, then batch at the chunk level.
On the VDB side, use bulk upsert or batch insert APIs instead of inserting one vector at a time. That reduces round-trips and lets the database optimize index updates. Pipeline design: read a batch of raw items → generate embeddings in a batch → write the batch of (id, vector, metadata) to the VDB. For streaming or real-time ingestion, use small batches (e.g. 8–32) to keep latency low while still gaining some batching benefit.
Practical tips and trade-offs
Check the provider’s limit for embedding API calls (e.g. 100–200 texts per request). Larger batches improve throughput up to the limit; very large can hit timeouts or rate limits. Batching does not change embedding quality—same model and inputs produce the same vectors; use the same model and tokenization in batched and single-item flows.
For variable-length texts: pad to max length in the batch (or use dynamic padding in frameworks). Mask padding in the model so it doesn’t affect the output. Many APIs accept a list of strings and handle this internally. Monitor latency and throughput to choose batch sizes that meet your SLAs; see measuring latency and throughput (QPS). When writing to the VDB, use bulk upsert so each request sends many points; see what is a point in a VDB and the role of tokenization for consistency.
Frequently Asked Questions
What batch size for embedding API calls?
Check the provider’s limit (e.g. 100–200 texts per request). Larger batches improve throughput up to the limit; very large can hit timeouts or rate limits.
Does batching change embedding quality?
No. Same model and inputs produce the same vectors; batching only changes how many are computed per call. Use the same model and tokenization in batched and single-item flows.
How do I batch with variable-length texts?
Pad to max length in the batch (or use dynamic padding in frameworks). Mask padding in the model so it doesn’t affect the output. Many APIs accept a list of strings and handle this internally.
Should I batch VDB upserts?
Yes. Use bulk upsert or batch insert APIs instead of one point per request to reduce round-trips and let the DB optimize index updates.