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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.search.searchers;
import com.yahoo.config.subscription.ConfigGetter;
import com.yahoo.prelude.IndexFacts;
import com.yahoo.prelude.IndexModel;
import com.yahoo.prelude.SearchDefinition;
import com.yahoo.search.Query;
import com.yahoo.search.Result;
import com.yahoo.search.query.QueryTree;
import com.yahoo.search.query.parser.Parsable;
import com.yahoo.search.query.parser.ParserEnvironment;
import com.yahoo.search.result.ErrorMessage;
import com.yahoo.search.searchchain.Execution;
import com.yahoo.search.yql.YqlParser;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.vespa.config.search.AttributesConfig;
import org.junit.jupiter.api.Test;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertNull;
/**
* @author arnej
*/
public class ValidateNearestNeighborTestCase {
ValidateNearestNeighborSearcher searcher;
@SuppressWarnings("deprecation")
public ValidateNearestNeighborTestCase() {
searcher = new ValidateNearestNeighborSearcher(
ConfigGetter.getConfig(AttributesConfig.class,
"raw:" +
"attribute[9]\n" +
"attribute[0].name simple\n" +
"attribute[0].datatype INT32\n" +
"attribute[1].name dvector\n" +
"attribute[1].datatype TENSOR\n" +
"attribute[1].tensortype tensor(x[3])\n" +
"attribute[2].name fvector\n" +
"attribute[2].datatype TENSOR\n" +
"attribute[2].tensortype tensor<float>(x[3])\n" +
"attribute[3].name sparse\n" +
"attribute[3].datatype TENSOR\n" +
"attribute[3].tensortype tensor(x{})\n" +
"attribute[4].name matrix\n" +
"attribute[4].datatype TENSOR\n" +
"attribute[4].tensortype tensor(x[3],y[1])\n" +
"attribute[5].name threetypes\n" +
"attribute[5].datatype TENSOR\n" +
"attribute[5].tensortype tensor(x[42])\n" +
"attribute[6].name threetypes\n" +
"attribute[6].datatype TENSOR\n" +
"attribute[6].tensortype tensor(x[3])\n" +
"attribute[7].name threetypes\n" +
"attribute[7].datatype TENSOR\n" +
"attribute[7].tensortype tensor(x{})\n" +
"attribute[8].name mixeddvector\n" +
"attribute[8].datatype TENSOR\n" +
"attribute[8].tensortype tensor(a{},x[3])\n"
));
}
private static final TensorType tt_dense_dvector_42 = TensorType.fromSpec("tensor(x[42])");
private static final TensorType tt_dense_dvector_3 = TensorType.fromSpec("tensor(x[3])");
private static final TensorType tt_dense_dvector_2 = TensorType.fromSpec("tensor(x[2])");
private static final TensorType tt_dense_fvector_3 = TensorType.fromSpec("tensor<float>(x[3])");
private static final TensorType tt_dense_matrix_xy = TensorType.fromSpec("tensor(x[3],y[1])");
private static final TensorType tt_sparse_vector_x = TensorType.fromSpec("tensor(x{})");
private Tensor makeTensor(TensorType tensorType) {
return makeTensor(tensorType, 3);
}
private Tensor makeTensor(TensorType tensorType, int numCells) {
Tensor.Builder tensorBuilder = Tensor.Builder.of(tensorType);
double dv = 1.0;
String tensorDimension = "x";
for (long label = 0; label < numCells; label++) {
tensorBuilder.cell()
.label(tensorDimension, label)
.value(dv);
dv += 1.0;
}
return tensorBuilder.build();
}
private Tensor makeMatrix(TensorType tensorType) {
Tensor.Builder tensorBuilder = Tensor.Builder.of(tensorType);
double dv = 1.0;
String tensorDimension = "x";
for (long label = 0; label < 3; label++) {
tensorBuilder.cell()
.label("y", 0L)
.label(tensorDimension, label)
.value(dv);
dv += 1.0;
}
return tensorBuilder.build();
}
private String makeQuery(String attributeTensor, String queryTensor) {
return "select * from sources * where [{targetHits:1}]nearestNeighbor(" + attributeTensor + ", " + queryTensor + ")";
}
@Test
void testValidQueryDoubleVectors() {
String q = makeQuery("dvector", "qvector");
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertNull(r.hits().getError());
}
@Test
void testValidQueryFloatVectors() {
String q = makeQuery("fvector", "qvector");
Tensor t = makeTensor(tt_dense_fvector_3);
Result r = doSearch(searcher, q, t);
assertNull(r.hits().getError());
}
@Test
void testValidQueryDoubleVectorAgainstFloatVector() {
String q = makeQuery("dvector", "qvector");
Tensor t = makeTensor(tt_dense_fvector_3);
Result r = doSearch(searcher, q, t);
assertNull(r.hits().getError());
}
@Test
void testValidQueryFloatVectorAgainstDoubleVector() {
String q = makeQuery("fvector", "qvector");
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertNull(r.hits().getError());
}
@Test
void testvalidQueryMixedFieldTensor() {
String q = makeQuery("mixeddvector", "qvector");
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertNull(r.hits().getError());
}
private static void assertErrMsg(String message, Result r) {
assertEquals(ErrorMessage.createIllegalQuery(message), r.hits().getError());
}
static String desc(String field, String qt, int th, String errmsg) {
StringBuilder r = new StringBuilder();
r.append("NEAREST_NEIGHBOR {");
r.append("field=").append(field);
r.append(",queryTensorName=").append(qt);
r.append(",hnsw.exploreAdditionalHits=0");
r.append(",distanceThreshold=Infinity");
r.append(",approximate=true");
r.append(",targetHits=").append(th);
r.append("} ").append(errmsg);
return r.toString();
}
@Test
void testMissingTargetNumHits() {
String q = "select * from sources * where nearestNeighbor(dvector,qvector)";
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("dvector", "qvector", 0, "has invalid targetHits 0: Must be >= 1"), r);
}
@Test
void testMissingQueryTensor() {
String q = makeQuery("dvector", "foo");
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("dvector", "foo", 1, "requires a tensor rank feature named 'query(foo)' but this is not present"), r);
}
@Test
void testWrongTensorType() {
String q = makeQuery("dvector", "qvector");
Tensor t = makeTensor(tt_dense_dvector_2, 2);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("dvector", "qvector", 1, "field type tensor(x[3]) does not match query type tensor(x[2])"), r);
}
@Test
void testNotAttribute() {
String q = makeQuery("foo", "qvector");
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("foo", "qvector", 1, "field is not an attribute"), r);
}
@Test
void testWrongAttributeType() {
String q = makeQuery("simple", "qvector");
Tensor t = makeTensor(tt_dense_dvector_3);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("simple", "qvector", 1, "field is not a tensor"), r);
}
@Test
void testSeveralAttributesWithSameName() {
String q = makeQuery("threetypes", "qvector");
Tensor t1 = makeTensor(tt_dense_fvector_3);
Result r1 = doSearch(searcher, q, t1);
assertNull(r1.hits().getError());
Tensor t2 = makeTensor(tt_dense_dvector_42, 42);
Result r2 = doSearch(searcher, q, t2);
assertNull(r2.hits().getError());
Tensor t3 = makeTensor(tt_dense_dvector_2, 2);
Result r3 = doSearch(searcher, q, t3);
assertErrMsg(desc("threetypes", "qvector", 1, "field type tensor(x[42]) does not match query type tensor(x[2])"), r3);
}
@Test
void testSparseTensor() {
String q = makeQuery("sparse", "qvector");
Tensor t = makeTensor(tt_sparse_vector_x);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("sparse", "qvector", 1, "field type tensor(x{}) is not supported by nearest neighbor searcher"), r);
}
@Test
void testMatrix() {
String q = makeQuery("matrix", "qvector");
Tensor t = makeMatrix(tt_dense_matrix_xy);
Result r = doSearch(searcher, q, t);
assertErrMsg(desc("matrix", "qvector", 1, "field type tensor(x[3],y[1]) is not supported by nearest neighbor searcher"), r);
}
private static Result doSearch(ValidateNearestNeighborSearcher searcher, String yqlQuery, Tensor qTensor) {
QueryTree queryTree = new YqlParser(new ParserEnvironment()).parse(new Parsable().setQuery(yqlQuery));
Query query = new Query();
query.getModel().getQueryTree().setRoot(queryTree.getRoot());
query.getRanking().getFeatures().put("query(qvector)", qTensor);
SearchDefinition searchDefinition = new SearchDefinition("document");
IndexFacts indexFacts = new IndexFacts(new IndexModel(searchDefinition));
return new Execution(searcher, Execution.Context.createContextStub(indexFacts)).search(query);
}
}
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