🌱How to vector search

A Guide On Using Vector Search

Assumed Knowledge: Vectors Target Audience: General audience Reading Time: 1 minute

Vector Search is reliant largely on index libraries and open-source models. Each vector search guide follows a template of:

Process of indexing and searching vectors

The steps/materials can be summarised in the following way: Data: Obtain the data in a way that can be processed into a numerical representation and fed through the necessary model. Encode: Feed the data's numerical representation into a model and extract the vector which can be indexed and searched. Index: Indexing the vectors (from which the data has been encoded) in an efficient way that allows for retrieval. Search: Search the vectors that have been indexed by using a variety of Nearest Neighbor algorithms, filters, chunking and queries.

The Difficulties of Vector Search While the above process appears simple, there are a lot of difficulties with actually using vector search for production. These difficulties include:

• Deploying your index and search for production

• Usage of vectors to optimise search results

• Optimising the way search is being done on the vectors

• Optimising the matching of user intent and products