Hi all,
I have a set of extracted terms, with associated relevancy scores and other
metadata, from each document. I'd like to scale _score by the relevancy of
the matched terms. It seems to me there are at least two approaches for
solving this problem:
- Nested Document:
In this case, my mapping would look like:
{
"contentDocument": {
"properties": {
"content": {
"type": "string"
},
"terms": {
"type": "nested",
"fields": {
"text": {
"type": "string",
},
"relevance": {
"type": "float"
}
}
}
}
}
}
Then I could query using:
{
"query": {
"nested": {
"score_mode": "max",
"path": "terms",
"query": {
"function_score": {
"boost_mode": "replace",
"score_mode": "multiply",
"query": {
"match": {
"terms.text": ""
}
},
"functions": [
{
"field_value_factor": {
"field": "terms.relevance"
}
}
]
}
}
}
}
}
This seems to work as expected on the small prototype I've built.
- Parent/Child Documents:
In this case, my mapping would look like:
{
"contentDocument": {
"properties": {
"content": {
"type": "string"
}
}
}
}
{
"termDocument": {
"_parent": {
"type": "contentDocument"
},
"properties": {
"text": {
"type": "string"
},
"relevance": {
"type": "float"
}
}
}
}
Then I could query using:
{
"query": {
"has_child": {
"type": "termDocument",
"score_mode": "max",
"query": {
"function_score": {
"boost_mode": "replace",
"score_mode": "multiply",
"query": {
"match": {
"text": ""
}
},
"functions": [
{
"field_value_factor": {
"field": "termDocument.relevance"
}
}
]
}
}
}
}
}
This also seems to work in the prototype.
So, both options seem to work, which is great! However, I'm not sure if
there are any performance (or other) concerns with approaches? We will have
millions of documents (and associated terms), so we need our solution to
scale well. It seems to me that the nested approach is conceptually more
straightforward, so I'm leaning in that directly, but wanted to get input
for larger ES community.
Please let me know if there is any other options that might work better!
I've also considered using payloads:
However, I'm not sure that will work for us as there is metadata, other
than relevancy, I'd like to store about each term.
Thank you!
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