You should not have to think about chunking.
voyage-context-4 makes that possible.
With auto-chunking, no 32K ceiling, and built-in overlap support, it delivers 2.1% better retrieval than voyage-context-3 at 33% lower cost.
A strong fit for retrieval over long documents and agent context engineering.
Learn more:
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
chunking — Splitting documents into passages small enough to embed and retrieve individually — the step that quietly determines whether retrieval works at all.
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
Why it matters
chunkingSplitting documents into passages small enough to embed and retrieve individually — the step that quietly determines whether retrieval works at all.Full definition → strategy is a standing tuning burden in RAGRetrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.Full definition → pipelines. This model absorbs it and reports better retrieval at lower cost, so long documents can be indexed without hand-tuned splits.
