1. Download and convert to onnx any Hugging Face sentence transformer model(s)
2. Declare the model(s) with an id in services.xml
=> id=”bert”
3. Use the model(s) id to declare tensor field(s) built from any text field(s)
=> indexing: input myTextField | embed bert
4. Deploy the application package on docker or Vespa cloud
5. Use the model(s) id to embed the query text
=> input.query(myEmbedding)=embed(bert, “Hello world”)
6. Use ranking(s) to build mixed BM25/ANN expressions
Read the post from Vespa blog: https://blog.vespa.ai/text-embedding-made-simple/
WPSOLR with Vespa: https://www.wpsolr.com
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