Comments (5)
I added a test to make sure it's working in #178 . The test creates a mapping like the one above, indexes 100 docs with this mapping, runs a count request to count the number of fields with each field, makes sure the result for each request is 100, then runs a query targeting each of the vector fields and makes sure it returns some neighbors. It's possible there's still some edge case that I hadn't thought of.
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@zeppelinen Coincidentally I also ran into a closely related issue when updating to es 7.9.2. I'll keep you posted. I should have some to look into it more this weekend and early next week.
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@zeppelinen Which version are you using? I was trying to reproduce the issue but was actually able to make an index with the following mapping:
{
"issue-176" : {
"mappings" : {
"properties" : {
"df0" : {
"type" : "elastiknn_dense_float_vector",
"similarity" : "boolean",
"elastiknn" : {
"dims" : 10
}
},
"df1" : {
"type" : "elastiknn_dense_float_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "lsh",
"similarity" : "angular",
"dims" : 10,
"L" : 10,
"k" : 1
}
},
"df2" : {
"type" : "elastiknn_dense_float_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "lsh",
"similarity" : "angular",
"dims" : 10,
"L" : 10,
"k" : 1
}
},
"df3" : {
"type" : "elastiknn_dense_float_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "lsh",
"similarity" : "l2",
"dims" : 10,
"L" : 21,
"k" : 2,
"w" : 3
}
},
"df4" : {
"type" : "elastiknn_dense_float_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "permutation_lsh",
"dims" : 10,
"k" : 22,
"repeating" : false
}
},
"id" : {
"type" : "keyword",
"store" : true
},
"sb0" : {
"type" : "elastiknn_sparse_bool_vector",
"similarity" : "boolean",
"elastiknn" : {
"dims" : 10
}
},
"sb1" : {
"type" : "elastiknn_sparse_bool_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "sparse_indexed",
"dims" : 10
}
},
"sb2" : {
"type" : "elastiknn_sparse_bool_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "lsh",
"similarity" : "jaccard",
"dims" : 10,
"L" : 10,
"k" : 2
}
},
"sb3" : {
"type" : "elastiknn_sparse_bool_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "lsh",
"similarity" : "jaccard",
"dims" : 10,
"L" : 10,
"k" : 2
}
},
"sb4" : {
"type" : "elastiknn_sparse_bool_vector",
"similarity" : "boolean",
"elastiknn" : {
"model" : "lsh",
"similarity" : "hamming",
"dims" : 10,
"L" : 10,
"k" : 3
}
}
}
}
}
}
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I tested same configuration again using plugin from official project releases page and it works just fine.
Looks like my environment is broken. I'd blame adoptopenjdk docker container used for build.
Thank you @alexklibisz for your great work. I continue testing, hope will be back with more useful feedback.
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Related Issues (20)
- can't create a mapping HOT 1
- Try quick select algorithm for KthGreatest implementation HOT 4
- Try resampling vectors to speed up L2LshModel
- Try getting rid of HashAndFreq to minimize allocations HOT 1
- Try re-using threadlocal arrays in ArrayHitCounter HOT 2
- Try caching the query vector's FloatVector segments when computing distance HOT 2
- Get Fashion Mnist 96% recall up to 200 queries/second HOT 2
- Try using a byte array in ArrayHitCounter instead of a short array
- Try Lucene VectorUtil instead/alongside PanamaFloatVectorOps HOT 1
- Try index sorting to reduce number of shards/segments accessed HOT 2
- Kibana does not show the data of elastiknn_sparse_bool_vector HOT 1
- Q&A: Scale effects HOT 2
- Support range queries (neighbors within some distance) HOT 1
- Try using Lucene IntIntHashMap to speedup and reduce memory usage of top-K counting HOT 1
- Hope to support version 7.17.20, later 7.17.x can be downloaded HOT 1
- a problem about hybrid search HOT 3
- cannot create runtime field during seach HOT 1
- Using bitnami/elasticsearch: 8.14.1 add elastiknn I start an error HOT 1
- Support for index patterns
- MatchHashesAndScoreQuery could be emitting documents that had zero hits
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