Vector store index query engine

VectorStoreIndex is a type of index within LlamaIndex that implements vector embeddings to represent and retrieve information from documents. These documents with similar meanings will have embeddings that are closer together in the vector space, as we explored in the previous chapter. However, this time, the VectorStoreIndex does not automatically use the existing Deep Lake vector store. It can create a new in-memory vector index, re-embed the documents, and create a new index structure. We will take this approach further in Chapter 4, Multimodal Modular RAG for Drone Technology, when we implement a dataset that contains no indexes or embeddings.

There is no silver bullet to deciding which indexing method is suitable for your project! The best way to make a choice is to test the vector, tree, list, and keyword indexes introduced in this chapter.

We will first create the vector store index:

from llama_index.core import VectorStoreIndex...