milvus-java-sdk search fail

[WangBoys]

milvus version: 0.10.4
java sdk version: 11

package com.mozhe;
/*

• Licensed to the Apache Software Foundation (ASF) under one
• or more contributor license agreements. See the NOTICE file
• distributed with this work for additional information
• regarding copyright ownership. The ASF licenses this file
• to you under the Apache License, Version 2.0 (the
• "License"); you may not use this file except in compliance
• with the License. You may obtain a copy of the License at
• http://www.apache.org/licenses/LICENSE-2.0
• Unless required by applicable law or agreed to in writing,
• software distributed under the License is distributed on an
• "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
• KIND, either express or implied. See the License for the
• specific language governing permissions and limitations
• under the License.
  */

import com.google.common.util.concurrent.ListenableFuture;
import com.google.gson.JsonObject;
import io.milvus.client.*;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.SplittableRandom;
import java.util.concurrent.ExecutionException;
import java.util.stream.Collectors;
import java.util.stream.DoubleStream;

// This is a simple example demonstrating how to use Milvus Java SDK.
// For detailed API document, please refer to
// https://milvus-io.github.io/milvus-sdk-java/javadoc/io/milvus/client/package-summary.html
// You can also find more information on https://milvus.io/
public class MilvusClientExample {

// Helper function that generates random vectors
static List<List<Float>> generateVectors(int vectorCount, int dimension) {
    SplittableRandom splitcollectionRandom = new SplittableRandom();
    List<List<Float>> vectors = new ArrayList<>(vectorCount);
    for (int i = 0; i < vectorCount; ++i) {
        splitcollectionRandom = splitcollectionRandom.split();
        DoubleStream doubleStream = splitcollectionRandom.doubles(dimension);
        List<Float> vector = doubleStream.boxed().map(Double::floatValue).collect(Collectors.toList());
        vectors.add(vector);
    }
    return vectors;
}

// Helper function that normalizes a vector if you are using IP (Inner Product)
// as your metric
// type
static List<Float> normalizeVector(List<Float> vector) {
    float squareSum = vector.stream().map(x -> x * x).reduce((float) 0, Float::sum);
    final float norm = (float) Math.sqrt(squareSum);
    vector = vector.stream().map(x -> x / norm).collect(Collectors.toList());
    return vector;
}

public static void main(String[] args) throws InterruptedException {

    // You may need to change the following to the host and port of your Milvus
    // server
    String host = "192.168.61.245";
    int port = 19530;
    if (args.length >= 2) {
        host = args[0];
        port = Integer.parseInt(args[1]);
    }

    ConnectParam connectParam = new ConnectParam.Builder().withHost(host).withPort(port).build();
    MilvusClient client = new MilvusGrpcClient(connectParam);

    // Create a collection with the following collection mapping
    final String collectionName = "test_1000W_1222"; // collection name
    // final int dimension = 1536; // dimension of each vector
    // final int indexFileSize = 1024; // maximum size (in MB) of each index file
    // final MetricType metricType = MetricType.L2; // we choose IP (Inner Product)
    // as our metric type
    // CollectionMapping collectionMapping =
    // new CollectionMapping.Builder(collectionName, dimension)
    // .withIndexFileSize(indexFileSize)
    // .withMetricType(metricType)
    // .build();
    // Response createCollectionResponse =
    // client.createCollection(collectionMapping);

    // Check whether the collection exists
    // HasCollectionResponse hasCollectionResponse =
    // client.hasCollection(collectionName);
    // System.out.println("HasCollection: " +
    // hasCollectionResponse.getResponse().getMessage());

    // Get collection info
    // GetCollectionInfoResponse getCollectionInfoResponse =
    // client.getCollectionInfo(collectionName);

    // Insert randomly generated vectors to collection
    // final int vectorCount = 100000;
    // List<List<Float>> vectors = generateVectors(vectorCount, dimension);
    // vectors =
    // vectors.stream().map(MilvusClientExample::normalizeVector).collect(Collectors.toList());
    // InsertParam insertParam =
    // new InsertParam.Builder(collectionName).withFloatVectors(vectors).build();
    // InsertResponse insertResponse = client.insert(insertParam);
    // // Insert returns a list of vector ids that you will be using (if you did not
    // supply them
    // // yourself) to reference the vectors you just inserted
    // List<Long> vectorIds = insertResponse.getVectorIds();
    //
    // // Flush data in collection
    // Response flushResponse = client.flush(collectionName);
    //
    // // Get current entity count of collection
    // CountEntitiesResponse ountEntitiesResponse =
    // client.countEntities(collectionName);

    // Create index for the collection
    // We choose IVF_SQ8 as our index type here. Refer to IndexType javadoc for a
    // complete explanation of different index types
    // final IndexType indexType = IndexType.IVF_PQ;
    // Each index type has its optional parameters you can set. Refer to the Milvus
    // documentation
    // for how to set the optimal parameters based on your needs.
    // JsonObject indexParamsJson = new JsonObject();
    // indexParamsJson.addProperty("nlist", 2048);
    // Index index = new Index.Builder(collectionName,
    // indexType).withParamsInJson(indexParamsJson.toString()).build();
    // Response createIndexResponse = client.createIndex(index);

    // Get index info for your collection
    // GetIndexInfoResponse getIndexInfoResponse =
    // client.getIndexInfo(collectionName);
    // System.out.format("Index Info: %s\n",
    // getIndexInfoResponse.getIndex().get().toString());

    // Get collection info
    // Response getCollectionStatsResponse =
    // client.getCollectionStats(collectionName);
    // if (getCollectionStatsResponse.ok()) {
    // Collection info is sent back with JSON type string
    // String jsonString = getCollectionStatsResponse.getMessage();
    // System.out.format("Collection Stats: %s\n", jsonString);
    // }

    // Check whether a partition exists in collection
    // Obviously we do not have partition "tag" now
    // HasPartitionResponse testHasPartition = client.hasPartition(collectionName,
    // "tag");
    // if (testHasPartition.ok() && testHasPartition.hasPartition()) {
    // throw new AssertionError("Wrong results!");
    // }

    // Search vectors
    // Searching the first 5 vectors of the vectors we just inserted

    HashMap<String, List> map = PythonUtils.genBatch();
    List<List<Double>> vectors = map.get("vectors");
    List<String> names = map.get("names");

    final int searchBatchSize = 5;
    List<List<Double>> vectorsToSearch = vectors.subList(0, searchBatchSize);
    final long topK = 10;
    // Based on the index you created, the available search parameters will be
    // different. Refer to
    // the Milvus documentation for how to set the optimal parameters based on your
    // needs.
    JsonObject searchParamsJson = new JsonObject();
    searchParamsJson.addProperty("nprobe", 64);
    SearchParam searchParam = new SearchParam.Builder(collectionName).withBinaryVectors(vectorsToSearch)
            .withTopK(topK).withParamsInJson(searchParamsJson.toString()).build();

    SearchResponse searchResponse = client.search(searchParam);
    if (searchResponse.ok()) {
        List<List<SearchResponse.QueryResult>> queryResultsList = searchResponse.getQueryResultsList();
        final double epsilon = 0.001;
        for (int i = 0; i < searchBatchSize; i++) {
            // Since we are searching for vector that is already present in the collection,
            // the first result vector should be itself and the distance (inner product)
            // should be
            // very close to 1 (some precision is lost during the process)
            SearchResponse.QueryResult firstQueryResult = queryResultsList.get(i).get(0);
            if (Math.abs(1 - firstQueryResult.getDistance()) > epsilon) {
                throw new AssertionError("Wrong results!");
            }
        }
    }
    // You can also get result ids and distances separately
    List<List<Long>> resultIds = searchResponse.getResultIdsList();
    List<List<Float>> resultDistances = searchResponse.getResultDistancesList();

    System.out.println(resultIds);

    // You can send search request asynchronously, which returns a ListenableFuture
    // object
    ListenableFuture<SearchResponse> searchResponseFuture = client.searchAsync(searchParam);
    try {
        // Get search response immediately. Obviously you will want to do more
        // complicated stuff with
        // ListenableFuture
        SearchResponse searchResponse2 = searchResponseFuture.get();
    } catch (ExecutionException e) {
        e.printStackTrace();
    }

    // Delete the first 5 vectors you just searched
    // Response deleteByIdsResponse = client.deleteEntityByID(collectionName,
    // vectorIds.subList(0, searchBatchSize));
    // flushResponse = client.flush(collectionName);

    // Try to get the corresponding vector of the first id you just deleted.
    // GetEntityByIDResponse getEntityByIDResponse =
    // client.getEntityByID(collectionName,
    // vectorIds.subList(0, searchBatchSize));
    // Obviously you won't get anything
    // if (!getEntityByIDResponse.getFloatVectors().get(0).isEmpty()) {
    // throw new AssertionError("This can never happen!");
    // }

    // Compact the collection, erase deleted data from disk and rebuild index in
    // background (if
    // the data size after compaction is still larger than indexFileSize). Data was
    // only
    // soft-deleted until you call compact.
    // Response compactResponse = client.compact(collectionName);

    // Drop index for the collection
    // Response dropIndexResponse = client.dropIndex(collectionName);

    // Drop collection
    // Response dropCollectionResponse = client.dropCollection(collectionName);

    // Disconnect from Milvus server
    client.close();
}

}

Error info:

Exception in thread "main" java.lang.ClassCastException: class java.lang.Double cannot be cast to class java.lang.Float (java.lang.Double and java.lang.Float are in module java.base of loader 'bootstrap')
at com.google.protobuf.FloatArrayList.add(FloatArrayList.java:45)
at com.google.protobuf.AbstractMessageLite$Builder.addAllCheckingNulls(AbstractMessageLite.java:381)
at com.google.protobuf.AbstractMessageLite$Builder.addAll(AbstractMessageLite.java:434)
at io.milvus.grpc.RowRecord$Builder.addAllFloatData(RowRecord.java:611)
at io.milvus.client.AbstractMilvusGrpcClient.buildRowRecordList(MilvusGrpcClient.java:1332)
at io.milvus.client.AbstractMilvusGrpcClient.search(MilvusGrpcClient.java:639)
at io.milvus.client.MilvusGrpcClient.search(MilvusGrpcClient.java:49)
at com.mozhe.MilvusClientExample.main(MilvusClientExample.java:175)

[sahuang]

In our original example: List<List<Float>> vectorsToSearch = vectors.subList(0, searchBatchSize);
What you are using: List<List<Double>> vectors = map.get("vectors");

This will raise the error: class java.lang.Double cannot be cast to class java.lang.Float