hweller1

 searchQ.filter = filter;
        }
else if(beds || rooms) {
  let filter = { "$and" : []} 
  
   if (beds) {
     filter.$and.push({"beds" : {"$gte" : parseInt(beds) }})
   }
   if (rooms)
   {

hweller1

{$vectorSearch:{
    "index": "default",
    "queryVector": embedding,
    "path": "doc_embedding",
    "k": 100,
    "numCandidates": 1000
    }
}

hweller1

{$vectorSearch:{
    "index": "default",
    "queryVector": embedding,
    "path": "doc_embedding",
    "filter" : { "$and" : [{"beds": {"$gte" : 1}} , "score": {"$gte" : 91}}]},
    "k": 100,
    "numCandidates": 1000
    }
}

hweller1

{
"$vectorSearch": {
        "index":"default",
        "queryVector": vector_query,
        "path": "embedding",
        "limit": 5,
        "numCandidates": 50
    }
  },

hweller1

{
  "type": "vectorSearch,
  "fields": [{
    "path": "embedding",
    "dimensions": 768, # the dimension of `mpnet-base` model    
    "similarity": "euclidean",
    "type": "vector"
  }]
}

hweller1

{
  "type": "vectorSearch,
  "fields": [{
    "path": "embedding",
    "dimensions": 1536,
    "similarity": "cosine",
    "type": "vector"
  }]
}

hweller1

{
  "type": "vectorSearch,
  "fields": [{
    "path": "plot_embedding_hf",
    "dimensions": 384,
    "similarity": "dotProduct",
    "type": "vector"
  }]
}

hweller1

{
  "type": "vectorSearch,
  "fields": [{
    "path": "plot_embedding",
    "dimensions": 1536,
    "similarity": "cosine",
    "type": "vector"
  }]
}

hweller1

$search": {
	            "index":'1M_sphere_index',
	            "knnBeta": {
	            "path": "vector",
	            "vector": embedding.tolist(),
	            "k": k * multiplier,
	            # "filter":{
          		# 	"equals":{
	            # 	"path":"low_card",
		        #     "value":1

hweller1

def compute_overlap(exact_result_set: List, approx_result_set: List) -> float:
	# each result set is a list of urls, order not considered
	return len(exact_result_set.intersection(set(approx_result_set)) / len(set(exact_result_set))