What if your embeddings could see the whole document?
Now they can with voyage-context-3, our contextualized chunk embedding model.
Learn what contextualized embeddings are, when to use them, and how to evaluate them on your data in our latest tutorial! Link below 👇
💡 Unlike traditional embeddings, which encode chunks in isolation, contextualized chunk embeddings encode both chunk-level detail and document-level context.
💡Contextualized chunk embeddings are particularly valuable for long, unstructured documents—such as legal contracts, financial documents, research papers, and technical documentation—in which queries often depend on document-level context.
Learn more in our tutorial: https://t.co/v1NOvJ9xKK
embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
Chunks embedded in isolation lose the document context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → that queries often depend on. This explains the contextualized alternative and how to test whether it helps on your own corpus.
