Contextual Embedding Beyond the Gold Passage
Perplexity, Wednesday, September 30th, 2026
Perplexity released a contextual embedding model trained to retrieve answer passages plus their supporting context.
Perplexity introduced pplx-embed-v2-context-9b-preview, a contextual embedding model trained to retrieve both answer chunks and the supporting context needed to understand them, rather than a single gold passage.
Training uses Perplexity's context compression model as a teacher, aggregating its token-level predictions into chunk-level relevance scores.
The model produces one embedding per chunk at no extra inference cost, supports 1024-dimensional and int8 embeddings, and reports state-of-the-art results on the new context-bench benchmark.