Introducing support for sparse-dense embeddings for better search result

What this post added

Introduces support for sparse-dense embeddings, enabling hybrid search by combining keyword-aware semantic search with traditional keyword search. This allows for flexible ingestion of sparse or dense data into any index, simple integration via REST API or SDK, and supports data from any dense or sparse model, including advanced use cases like multimodal search and boosting. The post details how to upsert and query sparse-dense vectors, including specific pod type and metric requirements, and provides capacity projections for s1 and p1 pods.