Search Iterator

The ANN Search has a maximum limit on the number of entities that can be recalled in a single query, and simply using basic ANN Search may not meet the demands of large-scale retrieval. For ANN Search requests where topK exceeds 16,384, it is advisable to consider using the SearchIterator. This section will introduce how to use the SearchIterator and related considerations.

Overview

A Search request returns search results, while a SearchIterator returns an iterator. You can call the next() method of this iterator to get the search results.

Specifically, you can use the SearchIterators as follows:

1. Create a SearchIterator and set the number of entities to return per search request and the total number of entities to return.

2. Call the next() method of the SearchIterator in a loop to get the search result in a paginated manner.

3. Call the close() method of the iterator to end the loop if the next() method returns an empty result.

Create SearchIterator

The following code snippet demonstrates how to create a SearchIterator.

from pymilvus import connections, Collection

connections.connect(
    uri="http://localhost:19530",
    token="root:Milvus"
)

# create iterator
query_vectors = [
    [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]]

collection = Collection("iterator_collection")

iterator = collection.search_iterator(
    data=query_vectors,
    anns_field="vector",
    param={"metric_type": "L2", "params": {"nprobe": 16}},
    # highlight-next-line
    batch_size=50,
    output_fields=["color"],
    # highlight-next-line
    limit=20000
)

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.orm.iterator.SearchIterator;
import io.milvus.v2.common.IndexParam.MetricType;
import io.milvus.v2.service.vector.request.data.FloatVec;

import java.util.*;

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .token("root:Milvus")
        .build());

FloatVec queryVector = new FloatVec(new float[]{0.3580376395471989f, -0.6023495712049978f, 0.18414012509913835f, -0.26286205330961354f, 0.9029438446296592f});
SearchIterator searchIterator = client.searchIterator(SearchIteratorReq.builder()
        .collectionName("iterator_collection")
        .vectors(Collections.singletonList(queryVector))
        .vectorFieldName("vector")
        .batchSize(500L)
        .outputFields(Lists.newArrayList("color"))
        .topK(20000)
        .metricType(IndexParam.MetricType.COSINE)
        .build());

In the above examples, you have set the number of entities to return per search (batch_size/batchSize) to 50, and the total number of entities to return (topK) to 20,000.

Use SearchIterator

Once the SearchIterator is ready, you can call its next() method to get the search results in a paginated manner.

results = []

while True:
    # highlight-next-line
    result = iterator.next()
    if not result:
        # highlight-next-line
        iterator.close()
        break
    
    for hit in result:
        results.append(hit.to_dict())

import io.milvus.response.QueryResultsWrapper;

while (true) {
    List<QueryResultsWrapper.RowRecord> res = searchIterator.next();
    if (res.isEmpty()) {
        searchIterator.close();
        break;
    }

    for (QueryResultsWrapper.RowRecord record : res) {
        System.out.println(record);
    }
}

In the above code examples, you have created an infinite loop and called the next() method in the loop to store the search results in a variable and closed the iterator when the next() returns nothing.