حقل السلسلة

في ميلفوس، VARCHAR هو نوع البيانات المستخدم لتخزين بيانات السلسلة.

عندما تقوم بتعريف حقل VARCHAR ، هناك معلمتان إلزاميتان:

• تعيين datatype إلى DataType.VARCHAR.

• حدد max_length ، الذي يحدد الحد الأقصى لعدد البايتات التي يمكن للحقل VARCHAR تخزينها. النطاق الصالح max_length هو من 1 إلى 65,535 65,535.

إضافة حقل VARCHAR

لتخزين بيانات السلسلة في Milvus، قم بتعريف حقل VARCHAR في مخطط مجموعتك. فيما يلي مثال على تعريف مخطط مجموعة بحقلين VARCHAR:

• varchar_field1: يخزن ما يصل إلى 100 بايت، ويسمح بالقيم الفارغة، وله قيمة افتراضية "Unknown".

• varchar_field2: يخزن حتى 200 بايت، ويسمح بالقيم الفارغة، ولكن ليس له قيمة افتراضية.

# Import necessary libraries
from pymilvus import MilvusClient, DataType

# Define server address
SERVER_ADDR = "http://localhost:19530"

# Create a MilvusClient instance
client = MilvusClient(uri=SERVER_ADDR)

# Define the collection schema
schema = client.create_schema(
    auto_id=False,
    enable_dynamic_fields=True,
)

# Add `varchar_field1` that supports null values with default value "Unknown"
schema.add_field(field_name="varchar_field1", datatype=DataType.VARCHAR, max_length=100, nullable=True, default_value="Unknown")
# Add `varchar_field2` that supports null values without default value
schema.add_field(field_name="varchar_field2", datatype=DataType.VARCHAR, max_length=200, nullable=True)
schema.add_field(field_name="pk", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=3)

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;

import io.milvus.v2.common.DataType;
import io.milvus.v2.service.collection.request.AddFieldReq;
import io.milvus.v2.service.collection.request.CreateCollectionReq;

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
        .uri("http://localhost:19530")
        .build());
        
CreateCollectionReq.CollectionSchema schema = client.createSchema();
schema.setEnableDynamicField(true);

schema.addField(AddFieldReq.builder()
        .fieldName("varchar_field1")
        .dataType(DataType.VarChar)
        .maxLength(100)
        .isNullable(true)
        .defaultValue("Unknown")
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("varchar_field2")
        .dataType(DataType.VarChar)
        .maxLength(200)
        .isNullable(true)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("pk")
        .dataType(DataType.Int64)
        .isPrimaryKey(true)
        .build());

schema.addField(AddFieldReq.builder()
        .fieldName("embedding")
        .dataType(DataType.FloatVector)
        .dimension(3)
        .build());

import { MilvusClient, DataType } from "@zilliz/milvus2-sdk-node";

const client = new MilvusClient({
  address: `http://localhost:19530`
});

const schema = [
  {
    name: "metadata",
    data_type: DataType.JSON,
  },
  {
    name: "pk",
    data_type: DataType.Int64,
    is_primary_key: true,
  },
  {
    name: "varchar_field2",
    data_type: DataType.VarChar,
    max_length: 200,
  },
  {
    name: "varchar_field1",
    data_type: DataType.VarChar,
    max_length: 100,
  },
];

import (
    "context"
    "fmt"

    "github.com/milvus-io/milvus/client/v2/column"
    "github.com/milvus-io/milvus/client/v2/entity"
    "github.com/milvus-io/milvus/client/v2/index"
    "github.com/milvus-io/milvus/client/v2/milvusclient"
)

ctx, cancel := context.WithCancel(context.Background())
defer cancel()

milvusAddr := "localhost:19530"

client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: milvusAddr,
})
if err != nil {
    fmt.Println(err.Error())
    // handle error
}
defer client.Close(ctx)

schema := entity.NewSchema()
schema.WithField(entity.NewField().
    WithName("pk").
    WithDataType(entity.FieldTypeInt64).
    WithIsPrimaryKey(true),
).WithField(entity.NewField().
    WithName("embedding").
    WithDataType(entity.FieldTypeFloatVector).
    WithDim(3),
).WithField(entity.NewField().
    WithName("varchar_field1").
    WithDataType(entity.FieldTypeVarChar).
    WithMaxLength(100).
    WithNullable(true).
    WithDefaultValueString("Unknown"),
).WithField(entity.NewField().
    WithName("varchar_field2").
    WithDataType(entity.FieldTypeVarChar).
    WithMaxLength(200).
    WithNullable(true),
)

export varcharField1='{
    "fieldName": "varchar_field1",
    "dataType": "VarChar",
    "elementTypeParams": {
        "max_length": 100
    },
    "nullable": true
}'

export varcharField2='{
    "fieldName": "varchar_field2",
    "dataType": "VarChar",
    "elementTypeParams": {
        "max_length": 200
    },
    "nullable": true
}'

export primaryField='{
    "fieldName": "pk",
    "dataType": "Int64",
    "isPrimary": true
}'

export vectorField='{
    "fieldName": "embedding",
    "dataType": "FloatVector",
    "elementTypeParams": {
        "dim": 3
    }
}'

export schema="{
    \"autoID\": false,
    \"fields\": [
        $varcharField1,
        $varcharField2,
        $primaryField,
        $vectorField
    ]
}"

تعيين بارامترات الفهرس

تساعد الفهرسة على تحسين أداء البحث والاستعلام. في ميلفوس، الفهرسة إلزامية للحقول المتجهة ولكنها اختيارية للحقول القياسية.

ينشئ المثال التالي فهارس على الحقل المتجه embedding والحقل القياسي varchar_field1 ، وكلاهما يستخدم نوع الفهرس AUTOINDEX. باستخدام هذا النوع، يختار Milvus تلقائيًا الفهرس الأنسب بناءً على نوع البيانات. يمكنك أيضًا تخصيص نوع الفهرس والبارامترات لكل حقل. للحصول على التفاصيل، راجع شرح الفهرس.

# Set index params

index_params = client.prepare_index_params()

# Index `varchar_field1` with AUTOINDEX
index_params.add_index(
    field_name="varchar_field1",
    index_type="AUTOINDEX",
    index_name="varchar_index"
)

# Index `embedding` with AUTOINDEX and specify metric_type
index_params.add_index(
    field_name="embedding",
    index_type="AUTOINDEX",  # Use automatic indexing to simplify complex index settings
    metric_type="COSINE"  # Specify similarity metric type, options include L2, COSINE, or IP
)

import io.milvus.v2.common.IndexParam;
import java.util.*;

List<IndexParam> indexes = new ArrayList<>();
indexes.add(IndexParam.builder()
        .fieldName("varchar_field1")
        .indexName("varchar_index")
        .indexType(IndexParam.IndexType.AUTOINDEX)
        .build());
        
indexes.add(IndexParam.builder()
        .fieldName("embedding")
        .indexType(IndexParam.IndexType.AUTOINDEX)
        .metricType(IndexParam.MetricType.COSINE)
        .build());

indexOption1 := milvusclient.NewCreateIndexOption("my_collection", "embedding",
    index.NewAutoIndex(index.MetricType(entity.IP)))
indexOption2 := milvusclient.NewCreateIndexOption("my_collection", "varchar_field1",
    index.NewInvertedIndex())

const indexParams = [{
    index_name: 'varchar_index',
    field_name: 'varchar_field1',
    index_type: IndexType.AUTOINDEX,
)];

indexParams.push({
    index_name: 'embedding_index',
    field_name: 'embedding',
    metric_type: MetricType.COSINE,
    index_type: IndexType.AUTOINDEX,
});

export indexParams='[
        {
            "fieldName": "varchar_field1",
            "indexName": "varchar_index",
            "indexType": "AUTOINDEX"
        }
    ]'
    
export indexParams='[
        {
            "fieldName": "varchar_field1",
            "indexName": "varchar_index",
            "indexType": "AUTOINDEX"
        },
        {
            "fieldName": "embedding",
            "metricType": "COSINE",
            "indexType": "AUTOINDEX"
        }
    ]'

إنشاء مجموعة

بمجرد تعريف المخطط والفهرس، قم بإنشاء مجموعة تتضمن حقول سلسلة.

# Create Collection
client.create_collection(
    collection_name="my_collection",
    schema=schema,
    index_params=index_params
)

CreateCollectionReq requestCreate = CreateCollectionReq.builder()
        .collectionName("my_collection")
        .collectionSchema(schema)
        .indexParams(indexes)
        .build();
client.createCollection(requestCreate);

err = client.CreateCollection(ctx,
    milvusclient.NewCreateCollectionOption("my_collection", schema).
        WithIndexOptions(indexOption1, indexOption2))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

await client.create_collection({
    collection_name: "my_collection",
    schema: schema,
    index_params: index_params
});

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
-d "{
    \"collectionName\": \"my_collection\",
    \"schema\": $schema,
    \"indexParams\": $indexParams
}"
## {"code":0,"data":{}}

إدراج البيانات

بعد إنشاء المجموعة، أدرج الكيانات التي تطابق المخطط.

# Sample data
data = [
    {"varchar_field1": "Product A", "varchar_field2": "High quality product", "pk": 1, "embedding": [0.1, 0.2, 0.3]},
    {"varchar_field1": "Product B", "pk": 2, "embedding": [0.4, 0.5, 0.6]}, # varchar_field2 field is missing, which should be NULL
    {"varchar_field1": None, "varchar_field2": None, "pk": 3, "embedding": [0.2, 0.3, 0.1]},  # `varchar_field1` should default to `Unknown`, `varchar_field2` is NULL
    {"varchar_field1": "Product C", "varchar_field2": None, "pk": 4, "embedding": [0.5, 0.7, 0.2]},  # `varchar_field2` is NULL
    {"varchar_field1": None, "varchar_field2": "Exclusive deal", "pk": 5, "embedding": [0.6, 0.4, 0.8]},  # `varchar_field1` should default to `Unknown`
    {"varchar_field1": "Unknown", "varchar_field2": None, "pk": 6, "embedding": [0.8, 0.5, 0.3]},  # `varchar_field2` is NULL
    {"varchar_field1": "", "varchar_field2": "Best seller", "pk": 7, "embedding": [0.8, 0.5, 0.3]}, # Empty string is not treated as NULL
]

# Insert data
client.insert(
    collection_name="my_collection",
    data=data
)

import com.google.gson.Gson;
import com.google.gson.JsonObject;
import io.milvus.v2.service.vector.request.InsertReq;
import io.milvus.v2.service.vector.response.InsertResp;

List<JsonObject> rows = new ArrayList<>();
Gson gson = new Gson();
rows.add(gson.fromJson("{\"varchar_field1\": \"Product A\", \"varchar_field2\": \"High quality product\", \"pk\": 1, \"embedding\": [0.1, 0.2, 0.3]}", JsonObject.class));
rows.add(gson.fromJson("{\"varchar_field1\": \"Product B\", \"pk\": 2, \"embedding\": [0.4, 0.5, 0.6]}", JsonObject.class));
rows.add(gson.fromJson("{\"varchar_field1\": null, \"varchar_field2\": null, \"pk\": 3, \"embedding\": [0.2, 0.3, 0.1]}", JsonObject.class));
rows.add(gson.fromJson("{\"varchar_field1\": \"Product C\", \"varchar_field2\": null, \"pk\": 4, \"embedding\": [0.5, 0.7, 0.2]}", JsonObject.class));
rows.add(gson.fromJson("{\"varchar_field1\": null, \"varchar_field2\": \"Exclusive deal\", \"pk\": 5, \"embedding\": [0.6, 0.4, 0.8]}", JsonObject.class));
rows.add(gson.fromJson("{\"varchar_field1\": \"Unknown\", \"varchar_field2\": null, \"pk\": 6, \"embedding\": [0.8, 0.5, 0.3]}", JsonObject.class));
rows.add(gson.fromJson("{\"varchar_field1\": \"\", \"varchar_field2\": \"Best seller\", \"pk\": 7, \"embedding\": [0.8, 0.5, 0.3]}", JsonObject.class));

InsertResp insertR = client.insert(InsertReq.builder()
        .collectionName("my_collection")
        .data(rows)
        .build());

column1, _ := column.NewNullableColumnVarChar("varchar_field1",
    []string{"Product A", "Product B", "Product C", "Unknown", ""},
    []bool{true, true, false, true, false, true, true})
column2, _ := column.NewNullableColumnVarChar("varchar_field2",
    []string{"High quality product", "Exclusive deal", "Best seller"},
    []bool{true, false, false, false, true, false, true})

_, err = client.Insert(ctx, milvusclient.NewColumnBasedInsertOption("my_collection").
    WithInt64Column("pk", []int64{1, 2, 3, 4, 5, 6, 7}).
    WithFloatVectorColumn("embedding", 3, [][]float32{
        {0.1, 0.2, 0.3},
        {0.4, 0.5, 0.6},
        {0.2, 0.3, 0.1},
        {0.5, 0.7, 0.2},
        {0.6, 0.4, 0.8},
        {0.8, 0.5, 0.3},
        {0.8, 0.5, 0.3},
    }).
    WithColumns(column1, column2),
)
if err != nil {
    fmt.Println(err.Error())
    // handle err
}

const data = [
  {
    varchar_field1: "Product A",
    varchar_field2: "High quality product",
    pk: 1,
    embedding: [0.1, 0.2, 0.3],
  },
  {
    varchar_field1: "Product B",
    varchar_field2: "Affordable price",
    pk: 2,
    embedding: [0.4, 0.5, 0.6],
  },
  {
    varchar_field1: "Product C",
    varchar_field2: "Best seller",
    pk: 3,
    embedding: [0.7, 0.8, 0.9],
  },
];

await client.insert({
  collection_name: "my_collection",
  data: data,
});

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/insert" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
--data '{
    "data": [
        {"varchar_field1": "Product A", "varchar_field2": "High quality product", "pk": 1, "embedding": [0.1, 0.2, 0.3]},
        {"varchar_field1": "Product B", "pk": 2, "embedding": [0.4, 0.5, 0.6]},
        {"varchar_field1": null, "varchar_field2": null, "pk": 3, "embedding": [0.2, 0.3, 0.1]},  
        {"varchar_field1": "Product C", "varchar_field2": null, "pk": 4, "embedding": [0.5, 0.7, 0.2]},  
        {"varchar_field1": null, "varchar_field2": "Exclusive deal", "pk": 5, "embedding": [0.6, 0.4, 0.8]},  
        {"varchar_field1": "Unknown", "varchar_field2": null, "pk": 6, "embedding": [0.8, 0.5, 0.3]},  
        {"varchar_field1": "", "varchar_field2": "Best seller", "pk": 7, "embedding": [0.8, 0.5, 0.3]}  
    ],
    "collectionName": "my_collection"
}'

## {"code":0,"cost":0,"data":{"insertCount":3,"insertIds":[1,2,3]}}

الاستعلام باستخدام تعبيرات التصفية

بعد إدراج الكيانات، استخدم الأسلوب query لاسترداد الكيانات التي تطابق تعبيرات التصفية المحددة.

لاسترداد الكيانات التي يتطابق فيها varchar_field1 مع السلسلة "Product A":

# Filter `varchar_field1` with value "Product A"
filter = 'varchar_field1 == "Product A"'

res = client.query(
    collection_name="my_collection",
    filter=filter,
    output_fields=["varchar_field1", "varchar_field2"]
)

print(res)

# Example output:
# data: [
#     "{'varchar_field1': 'Product A', 'varchar_field2': 'High quality product', 'pk': 1}"
# ]

import io.milvus.v2.service.vector.request.QueryReq;
import io.milvus.v2.service.vector.response.QueryResp;

String filter = "varchar_field1 == \"Product A\"";
QueryResp resp = client.query(QueryReq.builder()
        .collectionName("my_collection")
        .filter(filter)
        .outputFields(Arrays.asList("varchar_field1", "varchar_field2"))
        .build());

System.out.println(resp.getQueryResults());

// Output
//
// [QueryResp.QueryResult(entity={varchar_field1=Product A, varchar_field2=High quality product, pk=1})]

filter := "varchar_field1 == \"Product A\""
queryResult, err := client.Query(ctx, milvusclient.NewQueryOption("my_collection").
    WithFilter(filter).
    WithOutputFields("varchar_field1", "varchar_field2"))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}
fmt.Println("varchar_field1", queryResult.GetColumn("varchar_field1").FieldData().GetScalars())
fmt.Println("varchar_field2", queryResult.GetColumn("varchar_field2").FieldData().GetScalars())

// Output
//
// varchar_field1 string_data:{data:"Product A"}
// varchar_field2 string_data:{data:"High quality product"}

await client.query({
    collection_name: 'my_collection',
    filter: 'varchar_field1 == "Product A"',
    output_fields: ['varchar_field1', 'varchar_field2']
});

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
-d '{
    "collectionName": "my_collection",
    "filter": "varchar_field1 == \"Product A\"",
    "outputFields": ["varchar_field1", "varchar_field2"]
}'
## {"code":0,"cost":0,"data":[{"pk":1,"varchar_field1":"Product A","varchar_field2":"High quality product"}]}

لاسترداد الكيانات التي يكون فيها varchar_field2 فارغًا:

# Filter entities where `varchar_field2` is null
filter = 'varchar_field2 is null'

res = client.query(
    collection_name="my_collection",
    filter=filter,
    output_fields=["varchar_field1", "varchar_field2"]
)

print(res)

# Example output:
# data: [
#     "{'varchar_field1': 'Product B', 'varchar_field2': None, 'pk': 2}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 3}",
#     "{'varchar_field1': 'Product C', 'varchar_field2': None, 'pk': 4}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 6}"
# ]

String filter = "varchar_field2 is null";
QueryResp resp = client.query(QueryReq.builder()
        .collectionName("my_collection")
        .filter(filter)
        .outputFields(Arrays.asList("varchar_field1", "varchar_field2"))
        .build());

System.out.println(resp.getQueryResults());

// Output
//
// [
//    QueryResp.QueryResult(entity={varchar_field1=Product B, varchar_field2=null, pk=2}),
//    QueryResp.QueryResult(entity={varchar_field1=Unknown, varchar_field2=null, pk=3}),
//    QueryResp.QueryResult(entity={varchar_field1=Product C, varchar_field2=null, pk=4}),
//    QueryResp.QueryResult(entity={varchar_field1=Unknown, varchar_field2=null, pk=6})
// ]

filter = "varchar_field2 is null"
queryResult, err = client.Query(ctx, milvusclient.NewQueryOption("my_collection").
    WithFilter(filter).
    WithOutputFields("varchar_field1", "varchar_field2"))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}
fmt.Println("varchar_field1", queryResult.GetColumn("varchar_field1"))
fmt.Println("varchar_field2", queryResult.GetColumn("varchar_field2"))

await client.query({
    collection_name: 'my_collection',
    filter: 'varchar_field2 is null',
    output_fields: ['varchar_field1', 'varchar_field2']
});

# restful
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
-d '{
    "collectionName": "my_collection",
    "filter": "varchar_field2 is null",
    "outputFields": ["varchar_field1", "varchar_field2"]
}'

لاسترداد الكيانات حيث varchar_field1 له القيمة "Unknown" ، استخدم التعبير التالي أدناه. نظرًا لأن القيمة الافتراضية لـ varchar_field1 هي "Unknown" ، يجب أن تتضمن النتيجة المتوقعة كيانات ذات varchar_field1 تم تعيينها صراحةً إلى "Unknown" أو مع تعيين varchar_field1 إلى لا شيء.

# Filter entities with `varchar_field1` with value `Unknown`
filter = 'varchar_field1 == "Unknown"'

res = client.query(
    collection_name="my_collection",
    filter=filter,
    output_fields=["varchar_field1", "varchar_field2"]
)

print(res)

# Example output:
# data: [
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 3}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': 'Exclusive deal', 'pk': 5}",
#     "{'varchar_field1': 'Unknown', 'varchar_field2': None, 'pk': 6}"
# ]

String filter = "varchar_field1 == \"Unknown\"";
QueryResp resp = client.query(QueryReq.builder()
        .collectionName("my_collection")
        .filter(filter)
        .outputFields(Arrays.asList("varchar_field1", "varchar_field2"))
        .build());

System.out.println(resp.getQueryResults());

// Output
// 
// [
//    QueryResp.QueryResult(entity={varchar_field1=Unknown, varchar_field2=null, pk=3}),
//    QueryResp.QueryResult(entity={varchar_field1=Unknown, varchar_field2=Exclusive deal, pk=5}),
//    QueryResp.QueryResult(entity={varchar_field1=Unknown, varchar_field2=null, pk=6})
// ]

filter = "varchar_field1 == \"Unknown\""
queryResult, err = client.Query(ctx, milvusclient.NewQueryOption("my_collection").
    WithFilter(filter).
    WithOutputFields("varchar_field1", "varchar_field2"))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}
fmt.Println("varchar_field1", queryResult.GetColumn("varchar_field1"))
fmt.Println("varchar_field2", queryResult.GetColumn("varchar_field2"))

// node
await client.query({
    collection_name: 'my_collection',
    filter: 'varchar_field1 == "Unknown"',
    output_fields: ['varchar_field1', 'varchar_field2']
});

# restful
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
-d '{
    "collectionName": "my_collection",
    "filter": "varchar_field1 == \"Unknown\"",
    "outputFields": ["varchar_field1", "varchar_field2"]
}'

البحث المتجه مع تعبيرات التصفية

بالإضافة إلى تصفية الحقل القياسي الأساسي، يمكنك دمج عمليات البحث عن تشابه المتجهات مع مرشحات الحقل القياسي. على سبيل المثال، يوضح الرمز التالي كيفية إضافة عامل تصفية الحقل القياسي إلى بحث متجه:

# Search with string filtering

# Filter `varchar_field2` with value "Best seller"
filter = 'varchar_field2 == "Best seller"'

res = client.search(
    collection_name="my_collection",
    data=[[0.3, -0.6, 0.1]],
    limit=5,
    search_params={"params": {"nprobe": 10}},
    output_fields=["varchar_field1", "varchar_field2"],
    filter=filter
)

print(res)

# Example output:
# data: [
#     "[{'id': 7, 'distance': -0.04468163847923279, 'entity': {'varchar_field1': '', 'varchar_field2': 'Best seller'}}]"
# ]

import io.milvus.v2.service.vector.request.SearchReq;
import io.milvus.v2.service.vector.response.SearchResp;

String filter = "varchar_field2 == \"Best seller\"";
SearchResp resp = client.search(SearchReq.builder()
        .collectionName("my_collection")
        .annsField("embedding")
        .data(Collections.singletonList(new FloatVec(new float[]{0.3f, -0.6f, 0.1f})))
        .topK(5)
        .outputFields(Arrays.asList("varchar_field1", "varchar_field2"))
        .filter(filter)
        .build());

System.out.println(resp.getSearchResults());

// Output
//
// [[SearchResp.SearchResult(entity={varchar_field1=, varchar_field2=Best seller}, score=-0.04468164, id=7)]]

queryVector := []float32{0.3, -0.6, 0.1}
filter = "varchar_field2 == \"Best seller\""

annParam := index.NewCustomAnnParam()
annParam.WithExtraParam("nprobe", 10)
resultSets, err := client.Search(ctx, milvusclient.NewSearchOption(
    "my_collection", // collectionName
    5,                       // limit
    []entity.Vector{entity.FloatVector(queryVector)},
).WithANNSField("embedding").
    WithFilter(filter).
    WithAnnParam(annParam).
    WithOutputFields("varchar_field1", "varchar_field2"))
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

for _, resultSet := range resultSets {
    fmt.Println("IDs: ", resultSet.IDs.FieldData().GetScalars())
    fmt.Println("Scores: ", resultSet.Scores)
    fmt.Println("varchar_field1: ", resultSet.GetColumn("varchar_field1"))
    fmt.Println("varchar_field2: ", resultSet.GetColumn("varchar_field2"))
}

await client.search({
    collection_name: 'my_collection',
    data: [0.3, -0.6, 0.1],
    limit: 5,
    output_fields: ['varchar_field1', 'varchar_field2'],
    filter: 'varchar_field2 == "Best seller"'
    params: {
       nprobe:10
    }
});

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
-d '{
    "collectionName": "my_collection",
    "data": [
        [0.3, -0.6, 0.1]
    ],
    "limit": 5,
    "searchParams":{
        "params":{"nprobe":10}
    },
    "outputFields": ["varchar_field1", "varchar_field2"],
    "filter": "varchar_field2 == \"Best seller\""
}'

## {"code":0,"cost":0,"data":[{"distance":-0.2364331,"id":1,"varchar_field1":"Product A","varchar_field2":"High quality product"}]}