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LlamaIndex has introduced a new feature that allows users to fine-tune a linear adapter on top of query embeddings from any model, such as sentence_transformers and OpenAI, to optimize retrieval over specific data and queries without needing to re-embed documents. This method, inspired by Vespa's concept of "freezing" document embeddings, involves the use of EmbeddingAdapterFinetuneEngine, which transforms only the query embeddings while keeping document embeddings fixed. The fine-tuning process is straightforward, can be performed on various embedding models, and is shown to result in modest performance improvements in retrieval metrics like hit-rate and mean reciprocal rank. Despite the modest gains compared to directly fine-tuning sentence_transformers, this approach offers a cost-effective and flexible solution for users looking to enhance retrieval performance without extensive reprocessing of document embeddings.

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