创建集合

from pymilvus import CollectionSchema, FieldSchema, DataType
book_id = FieldSchema(
  name="book_id",
  dtype=DataType.INT64,
  is_primary=True,
)
book_name = FieldSchema(
  name="book_name",
  dtype=DataType.VARCHAR,
  max_length=200,
)
word_count = FieldSchema(
  name="word_count",
  dtype=DataType.INT64,
)
book_intro = FieldSchema(
  name="book_intro",
  dtype=DataType.FLOAT_VECTOR,
  dim=2
)
schema = CollectionSchema(
  fields=[book_id, book_name, word_count, book_intro],
  description="Test book search"
)
collection_name = "book"
 

import { DataType } from "@zilliz/milvus2-sdk-node";
const params = {
  collection_name: "book",
  description: "Test book search",
  fields: [
    {
      name: "book_intro",
      description: "",
      data_type: DataType.FloatVector,
      dim: 2,
    },
    {
      name: "book_id",
      data_type: DataType.Int64,
      is_primary_key: true,
      description: "",
    },
    {
      name: "book_name",
      data_type: DataType.VarChar,
      max_length: 256,
      description: "",
    },
    {
      name: "word_count",
      data_type: DataType.Int64,
      description: "",
    },
  ],
};
 

var (
    collectionName = "book"
    )
schema := &entity.Schema{
  CollectionName: collectionName,
  Description:    "Test book search",
  Fields: []*entity.Field{
    {
      Name:       "book_id",
      DataType:   entity.FieldTypeInt64,
      PrimaryKey: true,
      AutoID:     false,
    },
    {
      Name:       "word_count",
      DataType:   entity.FieldTypeInt64,
      PrimaryKey: false,
      AutoID:     false,
    },
    {
      Name:     "book_intro",
      DataType: entity.FieldTypeFloatVector,
      TypeParams: map[string]string{
          "dim": "2",
      },
    },
  },
}
 

FieldType fieldType1 = FieldType.newBuilder()
        .withName("book_id")
        .withDataType(DataType.Int64)
        .withPrimaryKey(true)
        .withAutoID(false)
        .build();
FieldType fieldType2 = FieldType.newBuilder()
        .withName("word_count")
        .withDataType(DataType.Int64)
        .build();
FieldType fieldType3 = FieldType.newBuilder()
        .withName("book_intro")
        .withDataType(DataType.FloatVector)
        .withDimension(2)
        .build();
CreateCollectionParam createCollectionReq = CreateCollectionParam.newBuilder()
        .withCollectionName("book")
        .withDescription("Test book search")
        .withShardsNum(2)
        .addFieldType(fieldType1)
        .addFieldType(fieldType2)
        .addFieldType(fieldType3)
        .build();
 

create collection -c book -f book_id:INT64:book_id -f word_count:INT64:word_count -f book_intro:FLOAT_VECTOR:2 -p book_id
 

curl -X 'POST' 
  'http://localhost:9091/api/v1/collection' 
  -H 'accept: application/json' 
  -H 'Content-Type: application/json' 
  -d '{
    "collection_name": "book",
    "schema": {
      "autoID": false,
      "description": "Test book search",
      "fields": [
        {
          "name": "book_id",
          "description": "book id",
          "is_primary_key": true,
          "autoID": false,
          "data_type": 5
        },
        {
          "name": "word_count",
          "description": "count of words",
          "is_primary_key": false,
          "data_type": 5
        },
        {
          "name": "book_intro",
          "description": "embedded vector of book introduction",
          "data_type": 101,
          "is_primary_key": false,
          "type_params": [
            {
              "key": "dim",
              "value": "2"
            }
          ]
        }
      ],
      "name": "book"
    }
  }'
 

{}
 

from pymilvus import Collection
collection = Collection(
    name=collection_name,
    schema=schema,
    using='default',
    shards_num=2
    )
 

await milvusClient.createCollection(params);
 

err = milvusClient.CreateCollection(
    context.Background(), // ctx
    schema,
    2, // shardNum
)
if err != nil {
    log.Fatal("failed to create collection:", err.Error())
}
 

milvusClient.createCollection(createCollectionReq);
 

# Follow the previous step.
 

# Follow the previous step.