DocArray offers two ways of storing your data, each of which have their own documentation sections:
- Document Store for simple long-term storage
- Document Index for fast retrieval using vector similarity
This section covers the following three topics:
A Document Index lets you store your documents and search through them using vector similarity.
This is useful if you want to store a bunch of data, and at a later point retrieve documents that are similar to a query that you provide. Relevant concrete examples are neural search applications, augmenting LLMs and chatbots with domain knowledge (Retrieval-Augmented Generation)]), or recommender systems.
Currently, DocArray supports the following vector indexes. Some of them wrap vector databases (Weaviate, Qdrant, ElasticSearch) and act as a client for them, while others use a vector search library locally (HNSWLib, Exact NN search):