sentence embedding model - all-mpnet-base-v2 Test dataset - Kaggle
I used the data uploader in kibana
GET fashion_clothing_products-ori/_search
GET fashion_clothing_products-ori/_mapping
GET fashion_clothing_products-ori/_count
PUT fashion_clothing_products-vector
{
"mappings": {
"_meta": {
"created_by": "file-data-visualizer"
},
"properties": {
"Description": {
"type": "text"
},
"Gender": {
"type": "keyword"
},
"NumImages": {
"type": "long"
},
"Price_inr": {
"type": "long"
},
"PrimaryColor": {
"type": "keyword"
},
"ProductBrand": {
"type": "keyword"
},
"ProductID": {
"type": "long"
},
"ProductName": {
"type": "text"
},
"text_embedding": {
"properties": {
"is_truncated": {
"type": "boolean"
},
"model_id": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"predicted_value": {
"type": "dense_vector",
"dims": 768,
"index": true,
"similarity": "dot_product"
}
}
}
}
}
}
- You could just do this on ingest also
POST _reindex?wait_for_completion=false
{
"source": {
"index": "fashion_clothing_products-ori"
},
"dest": {
"index": "fashion_clothing_products-vector",
"pipeline": "overstock-all-mpnet-base-v2"
}
}
GET fashion_clothing_products-vector/_search
GET fashion_clothing_products-vector/_count
POST _ml/trained_models/sentence-transformers__all-mpnet-base-v2/_infer
{
"docs": [
{
"text_field": "Rugged red travel suitcase"
},
{
"text_field": "Men's lightweight workout shirt"
}
]
}
Expand for full -> POST fashion_clothing_products-vector/_search json
POST fashion_clothing_products-vector/_search
{
"_source": {
"excludes": [
"text_embedding"
]
},
"knn": {
"field": "text_embedding.predicted_value",
"k": 5,
"num_candidates": 10,
"query_vector": [
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}
Expand for full -> POST fashion_clothing_products-vector/_search json
POST fashion_clothing_products-vector/_search
{
"_source": {
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},
"knn": {
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