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|
// This file is autogenerated, DO NOT EDIT
// search/retriever.asciidoc:452
[source, python]
----
resp = client.search(
index="movies",
size=10,
retriever={
"rescorer": {
"rescore": {
"window_size": 50,
"query": {
"rescore_query": {
"script_score": {
"query": {
"match_all": {}
},
"script": {
"source": "cosineSimilarity(params.queryVector, 'product-vector_final_stage') + 1.0",
"params": {
"queryVector": [
-0.5,
90,
-10,
14.8,
-156
]
}
}
}
}
}
},
"retriever": {
"rrf": {
"rank_window_size": 100,
"retrievers": [
{
"standard": {
"query": {
"sparse_vector": {
"field": "plot_embedding",
"inference_id": "my-elser-model",
"query": "films that explore psychological depths"
}
}
}
},
{
"standard": {
"query": {
"multi_match": {
"query": "crime",
"fields": [
"plot",
"title"
]
}
}
}
},
{
"knn": {
"field": "vector",
"query_vector": [
10,
22,
77
],
"k": 10,
"num_candidates": 10
}
}
]
}
}
}
},
)
print(resp)
----
|
