In order to reproduce this you need a mongoDB collection with vectors and vector search index. In this case I am referring to a collection where the embeddings are computed using https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2.
You should be able to reproduce it even with a "fake" vector search collection.
This is the pipeline I used:
[
{
$vectorSearch:
{
queryVector: [
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0.00997952651232481, 0.4473152458667755
],
path: "vector",
numCandidates: 100,
index: "vector_search_index",
limit: 10
}
},
{
$addFields: {
score:
{
$meta: "vectorSearchScore"
}
}
},
{
$match: {
score:
{
$gt: 0.7
}
}
}
]
Attaching also a screenshot from compass showing the computed view. 