Champ chaîne de caractères

Dans Milvus, VARCHAR est le type de données utilisé pour stocker des données de type chaîne.

Lorsque vous définissez un champ VARCHAR, deux paramètres sont obligatoires :

• Définir datatype sur DataType.VARCHAR.

• Spécifiez le paramètre max_length, qui définit le nombre maximum d'octets que le champ VARCHAR peut stocker. La plage valide pour max_length est comprise entre 1 et 65 535.

Ajout d'un champ VARCHAR

Pour stocker des données de type chaîne dans Milvus, définissez un champ VARCHAR dans votre schéma de collection. Vous trouverez ci-dessous un exemple de définition d'un schéma de collecte avec deux champs VARCHAR:

• varchar_field1VARCHAR : stocke jusqu'à 100 octets, autorise les valeurs nulles et a une valeur par défaut de "Unknown".

• varchar_field2Le champ : stocke jusqu'à 200 octets, autorise les valeurs nulles, mais n'a pas de valeur par défaut.

# 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
    ]
}"

Définir les paramètres d'indexation

L'indexation permet d'améliorer les performances de recherche et de requête. Dans Milvus, l'indexation est obligatoire pour les champs vectoriels mais facultative pour les champs scalaires.

L'exemple suivant crée des index sur le champ vectoriel embedding et le champ scalaire varchar_field1, tous deux utilisant le type d'index AUTOINDEX. Avec ce type, Milvus sélectionne automatiquement l'index le plus approprié en fonction du type de données. Vous pouvez également personnaliser le type d'index et les paramètres pour chaque champ. Pour plus de détails, reportez-vous à Index Explained.

# 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"
        }
    ]'

Créer une collection

Une fois le schéma et l'index définis, créez une collection comprenant des champs de type chaîne.

# 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" \
-d "{
    \"collectionName\": \"my_collection\",
    \"schema\": $schema,
    \"indexParams\": $indexParams
}"
## {"code":0,"data":{}}

Insérer des données

Après avoir créé la collection, insérez les entités qui correspondent au schéma.

# 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" \
--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]}}

Requête avec des expressions de filtre

Après avoir inséré des entités, utilisez la méthode query pour récupérer les entités qui correspondent aux expressions de filtrage spécifiées.

Pour récupérer les entités dont le nom varchar_field1 correspond à la chaîne "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" \
-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"}]}

Pour récupérer les entités dont l'adresse varchar_field2 est null :

# 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" \
-d '{
    "collectionName": "my_collection",
    "filter": "varchar_field2 is null",
    "outputFields": ["varchar_field1", "varchar_field2"]
}'

Pour récupérer les entités où varchar_field1 a la valeur "Unknown", utilisez l'expression suivante. La valeur par défaut de varchar_field1 étant "Unknown", le résultat attendu devrait inclure les entités dont varchar_field1 est explicitement défini à "Unknown" ou dont varchar_field1 est défini à null.

# 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" \
-d '{
    "collectionName": "my_collection",
    "filter": "varchar_field1 == \"Unknown\"",
    "outputFields": ["varchar_field1", "varchar_field2"]
}'

Recherche vectorielle avec des expressions de filtrage

En plus du filtrage de base des champs scalaires, vous pouvez combiner des recherches de similarité vectorielle avec des filtres de champs scalaires. Par exemple, le code suivant montre comment ajouter un filtre de champ scalaire à une recherche vectorielle :

# 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" \
-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"}]}