gistfile1.txt

{
  "yql": "select id from vector where ([{\"targetHits\": 100, \"approximate\":true}]nearestNeighbor(doc_embedding,q1_embedding);",
  "hits": 100
  "ranking.features.query(q1_embedding): [0.21,0.12,....],
  "ranking.features.query(q2_embedding): [0.21,0.12,....],
  "ranking.features.query(threshold): 0.01,
  "ranking.profile": "threshold-something-else" 
}


schema:

document vector {
  field doc_embedding type tensor<float>(d[128]) {
    attribute {
      distance-metric: euclidean 
     }
     index {
     hnsw {
       max-links-per-node: 16
       neighbors-to-explore-at-insert: 500
     }
  }
}

rank-profile threshold-someting-else {
   #Calculate L2 distance between q2_embedding and doc_embedding
   function other_distance() {
     expression: sqrt(sum(map(query(q2_embedding) - attribute(doc_embedding), f(x)(x * x))))
   }
   first-phase { #Only targetHits from q1_embedding, vector gets exposed to first phase when using approximate
       rank-score-drop-limit:0
       expression: if(other_distance() > query(threshold),-1,closeness(field,vector))
   }
}