前提條件

• 已在本地客戶端成功安裝了PyMilvus庫，并將其更新至當前最新版本。

  如果您尚未在本地客戶端安裝PyMilvus庫，或者需要將其更新至當前最新版本，您可以執行以下命令。

  pip install --upgrade pymilvus

• 已創建Milvus實例，請參見詳情快速創建Milvus實例。

• 已創建指定向量維度和度量方法的Collection。詳細代碼如下所示。

  from pymilvus import MilvusClient
  
  client = MilvusClient(
      uri="http://c-xxxx.milvus.aliyuncs.com:19530",  # Milvus實例的公網地址。
      token="<yourUsername>:<yourPassword>",  # 登錄Milvus實例的用戶名和密碼。
      db_name="default"  # 待連接的數據庫名稱，本文示例為默認的default。
  )
  
  client.create_collection(
      collection_name="<yourCollectionname>",  # 指定待創建的集合的名稱，本文示例為milvus_collection。
      dimension=5,  # 指定集合中向量的維度，本文示例為5。
      metric_type="IP"  # 指定用于計算向量相似度的度量方法。本示例中是 "IP"（Inner Product，內積）。
  )
  

插入數據

在插入數據之前，您需要按照格式將數據構建為一個字典列表，其中列表的每個字典代表一個實體（Entity），并且包含了集合中預定義的字段以及任何動態字段。

# Insert some data
data = [
    {"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682"},
    {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025"},
    {"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781"},
    {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298"},
    {"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794"},
    {"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222"},
    {"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392"},
    {"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510"},
    {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381"},
    {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976"}
]

res = client.insert(
    collection_name="milvus_collection",
    data=data
)

print(res)

更新數據

# Upsert some data
data = [
    {"id": 0, "vector": [-0.619954382375778, 0.4479436794798608, -0.17493894838751745, -0.4248030059917294, -0.8648452746018911], "color": "black_9898"},
    {"id": 1, "vector": [0.4762662251462588, -0.6942502138717026, -0.4490002642657902, -0.628696575798281, 0.9660395877041965], "color": "red_7319"},
    {"id": 2, "vector": [-0.8864122635045097, 0.9260170474445351, 0.801326976181461, 0.6383943392381306, 0.7563037341572827], "color": "white_6465"},
    {"id": 3, "vector": [0.14594326235891586, -0.3775407299900644, -0.3765479013078812, 0.20612075380355122, 0.4902678929632145], "color": "orange_7580"},
    {"id": 4, "vector": [0.4548498669607359, -0.887610217681605, 0.5655081329910452, 0.19220509387904117, 0.016513983433433577], "color": "red_3314"},
    {"id": 5, "vector": [0.11755001847051827, -0.7295149788999611, 0.2608115847524266, -0.1719167007897875, 0.7417611743754855], "color": "black_9955"},
    {"id": 6, "vector": [0.9363032158314308, 0.030699901477745373, 0.8365910312319647, 0.7823840208444011, 0.2625222076909237], "color": "yellow_2461"},
    {"id": 7, "vector": [0.0754823906014721, -0.6390658668265143, 0.5610517334334937, -0.8986261118798251, 0.9372056764266794], "color": "white_5015"},
    {"id": 8, "vector": [-0.3038434006935904, 0.1279149203380523, 0.503958664270957, -0.2622661156746988, 0.7407627307791929], "color": "purple_6414"},
    {"id": 9, "vector": [-0.7125086947677588, -0.8050968321012257, -0.32608864121785786, 0.3255654958645424, 0.26227968923834233], "color": "brown_7231"}
]

res = client.upsert(
    collection_name='milvus_collection',
    data=data
)

print(res)

刪除數據

Milvus支持以下兩種刪除Entity的方法：

• 按照過濾條件刪除數據

  # Delete entities
  res = client.delete(
      collection_name="milvus_collection",
      filter="id in [4,5,6]"
  )
  
  print(res)
  

• 按照Entity主鍵ID刪除數據

  res = client.delete(
      collection_name="milvus_collection",
      ids=[8, 9],
      partition_name="partitionA"  # 可選，如果需要在特定分區中刪除。
  )
  
  print(res)