The capabilities of vector search are impressive, but it isn’t a perfect technology. In fact, without big domain-specific datasets to fine-tune models on, a traditional search still has some advantages. (View Highlight)
We repeatedly see that vector search unlocks incredible and intelligent retrieval but struggles to adapt to new domains. Whereas traditional search can cope with new domains but is fundamentally limited to a set performance level. (View Highlight)
Vector search or dense retrieval has been shown to significantly outperform traditional methods when the embedding models have been fine-tuned on the target domain. However, this changes when we try using these models for “out-of-domain” tasks. (View Highlight)
That means if we have a large amount of data covering a specific domain like “Medical question-answering”, we can fine-tune an embedding model. With that embedding model, we can create dense vectors and get outstanding vector search performance. (View Highlight)
Combining dense and sparse search takes work. In the past, engineering teams needed to run different solutions for dense and sparse search engines and another system to combine results in a meaningful way. (View Highlight)
The Pinecone approach to hybrid search uses a single hybrid index. It enables search across any modality; text, audio, images, etc. Finally, the weighting of dense vs. sparse can be chosen via the alpha parameter, making it easy to adjust. (View Highlight)
