README.md

milvus

Schema Define, Collection

from config import MILVUS_HOST, MILVUS_PORT, VECTOR_DIMENSION, METRIC_TYPE

# Field Schema define

from pymilvus import CollectionSchema, FieldSchema, DataType

book_id = FieldSchema( name="book_id", dtype=DataType.INT64, is_primary=True, auto_id=True )

book_intro = FieldSchema( name="book_intro", dtype=DataType.FLOAT_VECTOR, dim=2 )

# Collection Schema define(필드 스키마 주입)

schema = CollectionSchema( fields=[book_id, book_name, word_count, book_intro], description="Test book search" )

collection_name = "book"

# Create Collection

from pymilvus import Collection

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

# Check collection exists

from pymilvus import utility

utility.list_collections() # select all collection utility.has_collection("book")

utility.drop_collection(“book”) # drop collection

Drop collection

utility.drop_collection("book")

Partition

Create Partition

from pymilvus import Collection

collection.create_partition(“novel”) // default partition존재, 그뒤에 “name”: “novel” 파티션 추가

Drop Partition

collection.drop_partition("novel")

Check collection details

collection.schema # Return the schema.CollectionSchema of the collection.

collection.description # Return the description of the collection.

collection.name # Return the name of the collection.

collection.is_empty # Return the boolean value that indicates if the collection is empty.

collection.num_entities # Return the number of entities in the collection.

collection.primary_field # Return the schema.FieldSchema of the primary key field.

collection.partitions # Return the list[Partition] object.

collection.indexes # Return the list[Index] object.

use

• spring : mongodb에서 img 다운로드
• milvus : 다운받은 img, 거리벡터 변환 후 저장 -> 업로드 된 사진과 유사성 검사(distance로 비교)