📄️ Semantic Search
We can't have a technical event without talking about AI, ML, and LLMs these days. So, let's talk about semantic search.
📄️ Create vectors from your data
In order to search your data, you will need to create embeddings for your documents. Embeddings are vectors that represent your documents in a vector space. The vector space is a multi-dimensional space where each dimension represents a feature of your documents.
📄️ Create Vector Search indexes
To start using Vector Search, you must configure another search index on your database. The process is similar to what you have done in the last section.
📄️ Construct vector search queries
Just as you did in the previous step, you can explore the search results using an aggregation pipeline.
📄️ Add semantic search to your application
You now know everything you need to add full-text search capabilities to your application.