I am setting up an Elasticsearch cluster for searching vectors associated to an id.
For example,
Given data:
Parent id / Object id / vectors
P1 / BD / 123, 125, 235 ... 10304, 50305
P1 / DF / 125, 235, 240 ... 10305, 10306
P1 / ED / 123, 235, 350 ... 10010, 10344
...
P2 / AB / 125, 535, 740 ... 9315, 10306
P2 / VC / 133, 435, 350 ... 3010, 20344
P2 / RF / 113, 353, 390 ... 10110, 30344
...
There are millions of parents
hundreds of objects in a parent
1000 vectors in an object
So basically I want to
- index all of the vectors
- given input P999, search for similar parents from the cluster by finding the most number of similar objects. (similar objects: at least 50 vector matches)
Here's a sample result I expect
Input:
P999 / HH / xxx, xxx ...
P999 / YH / xxx, xxx ...
P999 / GJ / xxx, xxx ...
...
Output:
[result sorted desc]
P20 has 60 similar objects
P4 has 45 similar objects
P501 has 41 similar objects
...
similar objects: at least 50 vector matches
To achieve this,
I need
- Good schema
- A query that stores vectors
- A query that searches a list of similar objects in desc order
And I need some helps on these three.
- Schema
curl -XPOST url/vectors -d '{
"mappings" : {
"properties": {
"object_id":{"type":"text"},
"parent_id":{"type":"text"},
"vectors":{"type":"text"}
}
}
}'
- insert query
curl -XPUT url/vectors -d '{
"parent_id":"P1",
"object_id":"BD",
"vectors":"123, 125, 235 ... 10304, 50305"}
}'
- search query
curl -XGET url/vectors -d '{
"size":10000,
"query" {
"function_score": {
"functions": [
{
???
}
],
"qurey": {
"bool": {
"should": [
{ "terms"{"vectors":["111"] },
{ "terms"{"vectors":["222"] },
...
{ "terms"{"vectors":["333"] },
{ "terms"{"vectors":["444"] }
]
}
},
"minimum_should_match": "50",
}
},
"from": 0,
"sort":
[
{
"_score": {
"order": "desc"
}
}
]
}'
And my questions are
- In my schema mapping, is this a right way to store vectors?
- In my search query, I need some help on [???] part to get the expected results. And I am not even sure I am on the right track. Would you correct my query if wrong?
Thanks