Collection¶

Constructor¶

Attributes¶

Methods¶

APIs References¶

class pymilvus.Collection(name, schema=None, using='default', shards_num=2, **kwargs)¶

This is a class corresponding to collection in milvus.

__init__(name, schema=None, using='default', shards_num=2, **kwargs)¶

Constructs a collection by name, schema and other parameters. Connection information is contained in kwargs.

Parameters

• name (str) -- the name of collection

• schema (class schema.CollectionSchema) -- the schema of collection

• using (str) -- Milvus link of create collection

• shards_num (int) -- How wide to scale collection. Corresponds to how many active datanodes can be used on insert.

• kwargs --

  • consistency_level (str/int) --

  Which consistency level to use when searching in the collection. For details, see https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md. Note: this parameter can be overwritten by the same parameter specified in search.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()

>>> fields = [
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128)
... ]
>>> description="This is a new collection description."
>>> schema = CollectionSchema(fields=fields, description=description)
>>> collection = Collection(name="test_collection_init", schema=schema)
>>> collection.name
'test_collection_init'
>>> collection.description
'This is a new collection description.'
>>> collection.is_empty
True
>>> collection.num_entities
0

property schema¶

Returns the schema of the collection.

Return schema.CollectionSchema

Schema of the collection.

property description¶

Returns a text description of the collection.

Return str

Collection description text, returned when the operation succeeds.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> fields = [
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128)
... ]
>>> description="This is an example text description."
>>> schema = CollectionSchema(fields=fields, description=description)
>>> collection = Collection(name="test_collection_description", schema=schema)
>>> collection.description
'This is an example text description.'

property name¶

Returns the collection name.

Return str

The collection name, returned when the operation succeeds.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> fields = [
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128)
... ]
>>> schema = CollectionSchema(fields)
>>> collection = Collection("test_collection_name", schema)
>>> collection.name
'test_collection_name'

property is_empty¶

Whether the collection is empty. This method need to call num_entities.

Return bool

• True: The collection is empty.

• False: The collection is gfghnot empty.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_is_empty", schema)
>>> collection.is_empty
True
>>> collection.insert([[1], [[1.0, 2.0]]])

>>> collection.is_empty
False

property num_entities¶

Returns the number of entities in the collection.

Return int

Number of entities in the collection.

Raises

CollectionNotExistException -- If the collection does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_num_entities", schema)
>>> collection.num_entities
0
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> collection.num_entities
2

property primary_field¶

Returns the primary field of the collection.

Return schema.FieldSchema

The primary field of the collection.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("film_length", DataType.INT64, description="length in miniute"),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_primary_field", schema)
>>> collection.primary_field.name
'film_id'

drop(timeout=None, **kwargs)¶

Drops the collection together with its index files.

Parameters

timeout --

• timeout (float) --

  An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs.

Raises

CollectionNotExistException -- If the collection does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_drop", schema)
>>> utility.has_collection("test_collection_drop")
True
>>> collection.drop()
>>> utility.has_collection("test_collection_drop")
False

load(partition_names=None, timeout=None, **kwargs)¶

Load the collection from disk to memory.

Parameters

• partition_names (list[str]) -- The specified partitions to load.

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or error occurs.

• kwargs --

  • _async (bool) -- Indicate if invoke asynchronously.

Raises

• CollectionNotExistException -- If the collection does not exist.

• ParamError -- If the parameters are invalid.

• BaseException -- If the specified field, index or partition does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_load", schema)
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> collection.load()

release(timeout=None, **kwargs)¶

Releases the collection from memory.

Parameters

timeout --

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs.

Raises

• CollectionNotExistException -- If collection does not exist.

• BaseException -- If collection has not been loaded to memory.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_release", schema)
>>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]])
>>> collection.load()
>>> collection.release()

insert(data, partition_name=None, timeout=None, **kwargs)¶

Insert data into the collection.

Parameters

• data (list-like(list, tuple) object or pandas.DataFrame) -- The specified data to insert, the dimension of data needs to align with column number

• partition_name (str) -- The partition name which the data will be inserted to, if partition name is not passed, then the data will be inserted to "_default" partition

• timeout --

  • timeout (float) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs.

Returns

A MutationResult object contains a property named insert_count represents how many

entities have been inserted into milvus and a property named primary_keys is a list of primary keys of the inserted entities. :rtype: MutationResult

Raises

• CollectionNotExistException -- If the specified collection does not exist.

• ParamError -- If input parameters are invalid.

• BaseException -- If the specified partition does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_insert", schema)
>>> data = [
...     [random.randint(1, 100) for _ in range(10)],
...     [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> collection.num_entities
10

delete(expr, partition_name=None, timeout=None, **kwargs)¶

Delete entities with an expression condition. And return results to show how many entities will be deleted.

Parameters

• expr (str) -- The expression to specify entities to be deleted

• partition_name (str) -- Name of partitions that contain entities

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Returns

A MutationResult object contains a property named delete_count represents how many entities will be deleted.

Return type

MutationResult

Raises

• RpcError -- If gRPC encounter an error

• ParamError -- If parameters are invalid

• BaseException -- If the return result from server is not ok

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("film_date", DataType.INT64),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_query", schema)
>>> # insert
>>> data = [
...     [i for i in range(10)],
...     [i + 2000 for i in range(10)],
...     [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> collection.num_entities

>>> expr = "film_id in [ 0, 1 ]"
>>> res = collection.delete(expr)
>>> assert len(res) == 2
>>> print(f"- Deleted entities: {res}")
- Delete results: [0, 1]

search(data, anns_field, param, limit, expr=None, partition_names=None, output_fields=None, timeout=None, round_decimal=-1, **kwargs)¶

Conducts a vector similarity search with an optional boolean expression as filter.

Parameters

• data (list[list[float]]) -- The vectors of search data, the length of data is number of query (nq), the dim of every vector in data must be equal to vector field's of collection.

• anns_field (str) -- The vector field used to search of collection.

• param (dict) -- The parameters of search, such as nprobe.

• limit (int) -- The max number of returned record, also known as topk.

• expr (str) -- The boolean expression used to filter attribute.

• partition_names (list[str]) -- The names of partitions to search.

• output_fields (list[str]) -- The fields to return in the search result, not supported now.

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur.

• round_decimal (int) -- The specified number of decimal places of returned distance

• kwargs --

  • _async (bool) -- Indicate if invoke asynchronously. When value is true, method returns a SearchFuture object; otherwise, method returns results from server directly.

  • _callback (function) -- The callback function which is invoked after server response successfully. It functions only if _async is set to True.

  • consistency_level (str/int) -- Which consistency level to use when searching in the collection. See details in https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md. Note: this parameter will overwrite the same parameter specified when user created the collection, if no consistency level was specified, search will use the consistency level when you create the collection.

  • guarantee_timestamp (int) -- This function instructs Milvus to see all operations performed before a provided timestamp. If no such timestamp is provided, then Milvus will search all operations performed to date. Note: only used in Customized consistency level.

  • graceful_time (int) -- Only used in bounded consistency level. If graceful_time is set, PyMilvus will use current timestamp minus the graceful_time as the guarantee_timestamp. This option is 5s by default if not set.

  • travel_timestamp (int) -- Users can specify a timestamp in a search to get results based on a data view at a specified point in time.

Returns

SearchResult: SearchResult is iterable and is a 2d-array-like class, the first dimension is the number of vectors to query (nq), the second dimension is the number of limit(topk).

Return type

SearchResult

Raises

• RpcError -- If gRPC encounter an error.

• ParamError -- If parameters are invalid.

• DataTypeNotMatchException -- If wrong type of param is passed.

• BaseException -- If the return result from server is not ok.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_search", schema)
>>> # insert
>>> data = [
...     [i for i in range(10)],
...     [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> collection.num_entities
10
>>> collection.load()
>>> # search
>>> search_param = {
...     "data": [[1.0, 1.0]],
...     "anns_field": "films",
...     "param": {"metric_type": "L2"},
...     "limit": 2,
...     "expr": "film_id > 0",
... }
>>> res = collection.search(**search_param)
>>> assert len(res) == 1
>>> hits = res[0]
>>> assert len(hits) == 2
>>> print(f"- Total hits: {len(hits)}, hits ids: {hits.ids} ")
- Total hits: 2, hits ids: [8, 5]
>>> print(f"- Top1 hit id: {hits[0].id}, distance: {hits[0].distance}, score: {hits[0].score} ")
- Top1 hit id: 8, distance: 0.10143111646175385, score: 0.10143111646175385

query(expr, output_fields=None, partition_names=None, timeout=None, **kwargs)¶

Query with a set of criteria, and results in a list of records that match the query exactly.

Parameters

• expr (str) -- The query expression

• output_fields (list[str]) -- A list of fields to return

• partition_names (list[str]) -- Name of partitions that contain entities

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

• kwargs --

  • consistency_level (str/int) -- Which consistency level to use during a query on the collection. For details, see https://github.com/milvus-io/milvus/blob/master/docs/developer_guides/how-guarantee-ts-works.md. Note: this parameter will overwrite the same parameter specified when user created the collection, if no consistency level was specified, query will use the consistency level when you create the collection.

  • guarantee_timestamp (int) -- This function instructs Milvus to see all operations performed before a provided timestamp. If no such timestamp is specified, Milvus queries all operations performed to date. Note: only used in Customized consistency level.

  • graceful_time (int) -- Only used in bounded consistency level. If graceful_time is set, PyMilvus will use current timestamp minus the graceful_time as the guarantee_timestamp. This option is 5s by default if not set.

  • travel_timestamp (int) -- Users can specify a timestamp in a search to get results based on a data view at a specified point in time.

Returns

A list that contains all results

Return type

list

Raises

• RpcError -- If gRPC encounter an error

• ParamError -- If parameters are invalid

• DataTypeNotMatchException -- If wrong type of param is passed

• BaseException -- If the return result from server is not ok

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> import random
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("film_date", DataType.INT64),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_query", schema)
>>> # insert
>>> data = [
...     [i for i in range(10)],
...     [i + 2000 for i in range(10)],
...     [[random.random() for _ in range(2)] for _ in range(10)],
... ]
>>> collection.insert(data)
>>> collection.num_entities
10
>>> collection.load()
>>> # query
>>> expr = "film_id in [ 0, 1 ]"
>>> res = collection.query(expr, output_fields=["film_date"])
>>> assert len(res) == 2
>>> print(f"- Query results: {res}")
- Query results: [{'film_id': 0, 'film_date': 2000}, {'film_id': 1, 'film_date': 2001}]

property partitions¶

Return all partitions of the collection.

Return list[Partition]

List of Partition object, return when operation is successful.

Raises

CollectionNotExistException -- If collection doesn't exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_partitions", schema)
>>> collection.partitions
[{"name": "_default", "description": "", "num_entities": 0}]

partition(partition_name) → pymilvus.orm.partition.Partition¶

Return the partition corresponding to name. Return None if not existed.

Parameters

partition_name (str) -- The name of the partition to get.

Return Partition

Partition object corresponding to partition_name.

Raises

• CollectionNotExistException -- If collection doesn't exist.

• BaseException -- If partition doesn't exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()

>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_partition", schema)
>>> collection.partition("_default")
{"name": "_default", "description": "", "num_entities": 0}
>>> collection.partition("partition")

create_partition(partition_name, description='')¶

Create the partition corresponding to name if not existed.

Parameters

• partition_name (str) -- The name of the partition to create.

• description (str) -- The description of the partition corresponding to name.

Return Partition

Partition object corresponding to partition_name.

Raises

• CollectionNotExistException -- If collection doesn't exist.

• BaseException -- If partition doesn't exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_create_partition", schema)
>>> collection.create_partition("comedy", description="comedy films")
{"name": "comedy", "collection_name": "test_collection_create_partition", "description": ""}
>>> collection.partition("comedy")
{"name": "comedy", "collection_name": "test_collection_create_partition", "description": ""}

has_partition(partition_name, timeout=None) → bool¶

Checks if a specified partition exists.

Parameters

• partition_name (str) -- The name of the partition to check

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Return bool

Whether a specified partition exists.

Raises

CollectionNotExistException -- If collection doesn't exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_has_partition", schema)
>>> collection.create_partition("comedy", description="comedy films")
{"name": "comedy", "description": "comedy films", "num_entities": 0}
>>> collection.has_partition("comedy")
True
>>> collection.has_partition("science_fiction")
False

drop_partition(partition_name, timeout=None, **kwargs)¶

Drop the partition and its corresponding index files.

Parameters

• partition_name (str) -- The name of the partition to drop.

• timeout --

  • timeout (float) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs.

Raises

• CollectionNotExistException -- If collection doesn't exist.

• BaseException -- If partition doesn't exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_drop_partition", schema)
>>> collection.create_partition("comedy", description="comedy films")
{"name": "comedy", "description": "comedy films", "num_entities": 0}
>>> collection.has_partition("comedy")
True
>>> collection.drop_partition("comedy")
>>> collection.has_partition("comedy")
False

property indexes¶

Returns all indexes of the collection.

Return list[Index]

List of Index objects, returned when this operation is successful.

Raises

CollectionNotExistException -- If the collection does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_indexes", schema)
>>> collection.indexes
[]

index() → pymilvus.orm.index.Index¶

Fetches the index object of the of the specified name.

Return Index

Index object corresponding to index_name.

Raises

• CollectionNotExistException -- If the collection does not exist.

• BaseException -- If the specified index does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_index", schema)
>>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
>>> collection.create_index("films", index)
Status(code=0, message='')
>>> collection.indexes
[]
>>> collection.index()

create_index(field_name, index_params, timeout=None, **kwargs) → pymilvus.orm.index.Index¶

Creates index for a specified field. Return Index Object.

Parameters

• field_name (str) -- The name of the field to create an index for.

• index_params (dict) -- The indexing parameters.

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Raises

• CollectionNotExistException -- If the collection does not exist.

• ParamError -- If the index parameters are invalid.

• BaseException -- If field does not exist.

• BaseException -- If the index has been created.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_create_index", schema)
>>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
>>> collection.create_index("films", index)
Status(code=0, message='')
>>> collection.index()

has_index(timeout=None) → bool¶

Checks whether a specified index exists.

Parameters

timeout (float) -- An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur

Return bool

Whether the specified index exists.

Raises

CollectionNotExistException -- If the collection does not exist.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_has_index", schema)
>>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
>>> collection.create_index("films", index)
>>> collection.has_index()
True

drop_index(timeout=None, **kwargs)¶

Drop index and its corresponding index files.

Parameters

timeout --

• timeout (float) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs. Optional. A duration of time in seconds.

Raises

• CollectionNotExistException -- If the collection does not exist.

• BaseException -- If the index does not exist or has been dropped.

Example

>>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
>>> connections.connect()
>>> schema = CollectionSchema([
...     FieldSchema("film_id", DataType.INT64, is_primary=True),
...     FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2)
... ])
>>> collection = Collection("test_collection_has_index", schema)
>>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
>>> collection.create_index("films", index)
>>> collection.has_index()
True
>>> collection.drop_index()
>>> collection.has_index()
False