🌱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:
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
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