Description:
I’m working on an e-commerce site where products are indexed in Elasticsearch. Users search through fields like brand
, publisher
, product_name
, and category
. I typically give the highest boost to the name
field when matching search queries. However, some product names are unique, and I want to adjust the boost based on a pre-determined score for each product’s name
field that I store during indexing.
Here’s how I’ve structured my Elasticsearch index for scoring:
json
Copy code
"properties": {
"fieldScores": {
"type": "nested",
"properties": {
"name": {
"properties": {
"value": {
"type": "double",
"default": 1
},
"weight": {
"type": "double",
"default": 1
}
}
}
}
}
}
Now, I want to adjust the boost during the search based on the `fieldScores.name.value` from the indexed data.
For example:
1. **Product 1**: Product name is "cotton swab", category is "books", brand is "xyz", and `fieldScores.name.value` = 0.
2. **Product 2**: Product name is "cotton swab 100 pieces", category is "cotton swabs", brand is "gök", and `fieldScores.name.value` = 1.
3. **Product 3**: Product name is "cotton swab 3000 pieces", category is "cotton swabs", brand is "gök", and `fieldScores.name.value` = 1.
When a user searches for "cotton swab", the first product should not receive any boost from the `name` field despite the exact match because `fieldScores.name.value` is 0. However, the second and third products should receive a boost from the `name` field as their `fieldScores.name.value` is 1.
I need help constructing an Elasticsearch query that adjusts the boost for the `name` field based on the stored `fieldScores.name.value`. How can I achieve this?