Conduct a Vector Query

This topic describes how to conduct a vector query.

Unlike a vector similarity search, a vector query retrieves vectors via scalar filtering based on boolean expression. Milvus supports many data types in the scalar fields and a variety of boolean expressions. The boolean expression filters on scalar fields or the primary key field, and it retrieves all results that match the filters.

The following example shows how to perform a vector query on a 2000-row dataset of book ID (primary key), word count (scalar field), and book introduction (vector field), simulating the situation where you query for certain books based on their IDs.

Load collection

All search and query operations within Milvus are executed in memory. Load the collection to memory before conducting a vector query.

from pymilvus import Collection
collection = Collection("book")      # Get an existing collection.
collection.load()

await milvusClient.collectionManager.loadCollection({
  collection_name: "book",
});

err := milvusClient.LoadCollection(
  context.Background(),   // ctx
  "book",                 // CollectionName
  false                   // async
)
if err != nil {
  log.Fatal("failed to load collection:", err.Error())
}

milvusClient.loadCollection(
  LoadCollectionParam.newBuilder()
    .withCollectionName("book")
    .build()
);

load -c book

# See the following step.

Conduct a vector query

The following example filters the vectors with certain book_id values, and returns the book_id field and book_intro of the results.

Milvus supports setting consistency level specifically for a query. The example in this topic sets the consistency level as Strong. You can also set the consistency level as Bounded, Session or Eventually. See Consistency for more information about the four consistency levels in Milvus.

res = collection.query(
  expr = "book_id in [2,4,6,8]", 
  output_fields = ["book_id", "book_intro"],
  consistency_level="Strong"
)

const results = await milvusClient.dataManager.query({
  collection_name: "book",
  expr: "book_id in [2,4,6,8]",
  output_fields: ["book_id", "book_intro"],
});

queryResult, err := milvusClient.Query(
	context.Background(),                                   // ctx
	"book",                                                 // CollectionName
	"",                                                     // PartitionName
	entity.NewColumnInt64("book_id", []int64{2,4,6,8}),     // expr
	[]string{"book_id", "book_intro"}                       // OutputFields
)
if err != nil {
	log.Fatal("fail to query collection:", err.Error())
}

List<String> query_output_fields = Arrays.asList("book_id", "word_count");
QueryParam queryParam = QueryParam.newBuilder()
  .withCollectionName("book")
  .withConsistencyLevel(ConsistencyLevelEnum.STRONG)
  .withExpr("book_id in [2,4,6,8]")
  .withOutFields(query_output_fields)
  .build();
R<QueryResults> respQuery = milvusClient.query(queryParam);

query

collection_name: book

The query expression: book_id in [2,4,6,8]

Name of partitions that contain entities(split by "," if multiple) []:

A list of fields to return(split by "," if multiple) []: book_id, book_intro

timeout []:

curl -X 'POST' \
  'http://localhost:9091/api/v1/query' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "collection_name": "book",
    "output_fields": ["book_id", "book_intro"],
    "vectors": [ [0.1,0.2] ],
    "expr": "book_id in [2,4,6,8]"
  }'

{
  "status":{},
  "fields_data":[
    {
      "type":5,
      "field_name":"book_id",
      "Field":{"Scalars":{"Data":{"LongData":{"data":[6,8,2,4]}}}},
      "field_id":100
    },
    {
      "type":101,
      "field_name":"book_intro",
      "Field":{"Vectors":{"dim":2,"Data":{"FloatVector":{"data":[6,1,8,1,2,1,4,1]}}}},
      "field_id":102
    }
  ]
}

Check the returned results.

sorted_res = sorted(res, key=lambda k: k['book_id'])
sorted_res

console.log(results.data)

fmt.Printf("%#v\n", queryResult)
for _, qr := range queryResult {
	fmt.Println(qr.IDs)
}

QueryResultsWrapper wrapperQuery = new QueryResultsWrapper(respQuery.getData());
System.out.println(wrapperQuery.getFieldWrapper("book_id").getFieldData());
System.out.println(wrapperQuery.getFieldWrapper("word_count").getFieldData());

# Milvus CLI automatically returns the entities with the pre-defined output fields.

# See the output of the previous step.

What's next

• Learn more basic operations of Milvus:

  • Conduct a vector search
  • Conduct a hybrid search
  • Search with Time Travel

• Explore API references for Milvus SDKs:

  • PyMilvus API reference
  • Node.js API reference
  • Go API reference
  • Java API reference