Exploring Existing Embedding Solutions: Do I Really Need One?
Once I wrapped my head around embeddings, I started looking into how existing products were implementing them. Some tools offer out-of-the-box solutions for generating embeddings and searching them efficiently. Things like Pinecone or Weaviate promise seamless integrations, but they come with their own costs and complexities. I also explored how some apps leverage pre-built embeddings from models like GPT or BERT, which seem like solid options when you need high-quality, general-purpose embeddings.
The more I researched, though, the more I realized that most of these solutions were either too expensive or overkill for what I wanted to do with Empirical. I don’t need a huge database of embeddings just yet—just enough to keep things smooth and contextually aware for a relatively small set of users.
