Summary
In this chapter, we have analyzed and practiced how you can use Jina’s advanced features, such as chunking, modality, and the advanced HNSWPostgreSQL
Executor, in order to tackle the most difficult goals of neural search. We implemented solutions for arbitrary hierarchical depth data representation, cross-modality searching, and non-blocking data updates. Chunking allowed us to reflect on some data’s properties of having multiple levels of semantic meaning, such as sentences in a paragraph or video clips in longer films. Cross-modal searching opens up one of the main advantages of neural search – its data universality. This means that you can search with any data for any type of data, as long as you use the correct model for the data type. Finally, the HNSWPostgreSQL
Executor allows us to build a live system where users can both search and index at the same time, with the data being kept in sync.