Contextualized Chunk Embeddings: Combining Local Detail with Global Context

Large documents pose a challenge for language model (LLM) applications due to inefficient context windows, but chunking addresses this by dividing documents into smaller segments for more precise information retrieval. This process, however, often leads to context loss, which can hinder the accuracy of responses if the broader document information is missed. Voyage AI introduces voyage-context-3, a contextualized chunk embedding model that encodes both the chunk content and the global document-level context in a single vector, improving retrieval accuracy without manual data augmentation. Contextualized chunk embeddings are particularly useful for complex, unstructured documents like legal contracts, where queries demand comprehensive document-level context often lost in traditional chunking methods. The model delivers superior retrieval performance over standard embeddings, as demonstrated by higher recall and mean reciprocal rank in evaluations using legal contract datasets. This approach allows for more effective information retrieval, maintaining document context and enhancing the utility of LLMs in processing and understanding extensive, detail-rich documents.

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