{
 
		'title': 'The Reported Mortality Rate of Coronavirus Is Not Important',
		'title_vector': [0.041732933, 0.013779674, -0.027564144, ..., 0.030096486],
		'var_array': ['https://medium.com/swlh/the-reported-mortality-rate-of-coronavirus-is-not-important-369989c8d912', 'The Startup'],
		'int_array': [13, 1100, 18]
 
}

import pandas as pd
 
# 加载下载的数据集的前100个实体
df = pd.read_csv('New_Medium_Data.csv', nrows=100)
for i in range(100):
	df['title_vector'][i] = eval(df['title_vector'][i])
 
# 将指定字段转换为数组
df['var_array'] = df[['link', 'publication']].values.tolist()
df['int_array'] = df[['reading_time', 'claps', 'responses']].values.tolist()
 
# 删除原始列
df = df.drop(columns=['link', 'publication', 'reading_time', 'claps', 'responses'])
 
# 将DataFrame转换为字典列表
data = df.to_dict('records')

    I provide the content of the markdown document that needs to be translated. Only the headings, paragraphs, and list content in markdown syntax need to be translated. Words in camel case and underscore do not need to be translated. Please retain the punctuations of md syntax. After you finish translating, replace the original content and return the result to me.
    
    When defining an array field, specify the following arguments for elements in the array field:

    - `element_type`: (Required) Data type of elements in an array. Valid values: `DataType.Int8`, `DataType.Int16`, `DataType.Int32`, `DataType.Int64`, `DataType.VARCHAR`, `DataType.BOOL`, `DataType.FLOAT`, and `DataType.DOUBLE`.
    - `max_capacity`: (Required) Maximum number of elements that an array field can contain. Value range: [1, 4,096].
    - `max_length`: Maximum length of strings for each VARCHAR element in an array field. This argument is required when `element_type` is set to `DataType.VARCHAR`. Value range: [1, 65,535].

    ```python
    # Define array fields

    from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType

    connections.connect(host='localhost', port='19530')

    # 1. define fields
    fields = [
        FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=False, max_length=100),
        FieldSchema(name='title', dtype=DataType.VARCHAR, max_length=512),
        FieldSchema(name='title_vector', dtype=DataType.FLOAT_VECTOR, dim=768),
        # define ARRAY field with VARCHAR elements
        FieldSchema(name='var_array', dtype=DataType.ARRAY, element_type=DataType.VARCHAR, max_capacity=900, max_length=1000),
        # define ARRAY field with INT64 elements
        FieldSchema(name='int_array', dtype=DataType.ARRAY, element_type=DataType.INT64, max_capacity=900)
    ]

    # 2. enable dynamic schema in schema definition
    schema = CollectionSchema(
            fields, 
            "The schema for a medium news collection", 
            enable_dynamic_field=True # Optional, defaults to 'False'.
    )

    # 3. reference the schema in a collection
    collection = Collection("medium_articles_with_array", schema)

    # 4. index the vector field
    index_params = {
        "index_type": "AUTOINDEX",
        "metric_type": "L2",
        "params": {}
    }

    collection.create_index(
      field_name="title_vector", 
      index_params=index_params
    )

    # 5. load the collection
    collection.load()
    ```

    ## Insert field values

    Once the collection is created, you can insert the processed data into it.

    <div class="alert note">

    If `auto_id` is set to `True` for a collection, insert data without the primary key field. Otherwise, an error can occur during data insert.

    </div>

    ```python
    # Insert field values

    # 1. insert data
    collection.insert(data)

    # 2. call the flush API to make inserted data immediately available for search
    collection.flush()

    print("Entity counts: ", collection.num_entities)

    # Output
    # Entity counts:  100
    ```

    ## Search or query with array fields

# 1. 使用 `int_array` 进行搜索
result = collection.search(
    data=data[0]['title_vector'],
    anns_field='title_vector',
    param={"metric_type": "L2", "params": {"nprobe": 10}},
    limit=3,
    expr='10 < int_array[0] < 20',
    output_fields=['title','int_array']
)
 
for hits in result:
    print("匹配的 ID：", hits.ids)
    print("与查询向量的距离：", hits.distances)
    print("匹配的文章：")
    for hit in hits:
        print(
            "标题：", 
            hit.entity.get("title"), 
            "，阅读时间：", 
            hit.entity.get("int_array")[0]
        )

# 2. 使用 `var_array` 进行查询
result = collection.query(
    expr='var_array[1] == "The Startup"',
    output_fields=['title','var_array']
)
 
for hits in result:
    print("匹配的 ID：", hits.id)
    print("匹配的文章：")
    for hit in hits:
        print(
            "标题：",
            hit.entity.get("title"),
            "，出版物：",
            hit.entity.get("var_array")[1]
        )

# 3. 使用 array_contains 检查数组是否包含特定元素
 
collection.query(
    expr='array_contains(int_array, 10)',
    output_fields=['title','int_array']
)