feature: support milvus-client iterator

examples/iterator/iterator.py

@@ -0,0 +1,99 @@
+from pymilvus.milvus_client.milvus_client import MilvusClient
+from pymilvus import (
+    FieldSchema, CollectionSchema, DataType,
+)
+import numpy as np
+
+collection_name = "test_milvus_client_iterator"
+prepare_new_data = True
+clean_exist = True
+
+USER_ID = "id"
+AGE = "age"
+DEPOSIT = "deposit"
+PICTURE = "picture"
+DIM = 8
+NUM_ENTITIES = 10000
+rng = np.random.default_rng(seed=19530)
+
+
+def test_query_iterator(milvus_client: MilvusClient):
+    # test query iterator
+    expr = f"10 <= {AGE} <= 25"
+    output_fields = [USER_ID, AGE]
+    queryIt = milvus_client.query_iterator(collection_name, filter=expr, batch_size=50, output_fields=output_fields)
+    page_idx = 0
+    while True:
+        res = queryIt.next()
+        if len(res) == 0:
+            print("query iteration finished, close")
+            queryIt.close()
+            break
+        for i in range(len(res)):
+            print(res[i])
+        page_idx += 1
+        print(f"page{page_idx}-------------------------")
+
+def test_search_iterator(milvus_client: MilvusClient):
+    vector_to_search = rng.random((1, DIM), np.float32)
+    search_iterator = milvus_client.search_iterator(collection_name, data=vector_to_search, batch_size=100, anns_field=PICTURE)
+
+    page_idx = 0
+    while True:
+        res = search_iterator.next()
+        if len(res) == 0:
+            print("query iteration finished, close")
+            search_iterator.close()
+            break
+        for i in range(len(res)):
+            print(res[i])
+        page_idx += 1
+        print(f"page{page_idx}-------------------------")
+
+
+def main():
+    milvus_client = MilvusClient("http://localhost:19530")
+    if milvus_client.has_collection(collection_name) and clean_exist:
+        milvus_client.drop_collection(collection_name)
+        print(f"dropped existed collection{collection_name}")
+
+    if not milvus_client.has_collection(collection_name):
+        fields = [
+            FieldSchema(name=USER_ID, dtype=DataType.INT64, is_primary=True, auto_id=False),
+            FieldSchema(name=AGE, dtype=DataType.INT64),
+            FieldSchema(name=DEPOSIT, dtype=DataType.DOUBLE),
+            FieldSchema(name=PICTURE, dtype=DataType.FLOAT_VECTOR, dim=DIM)
+        ]
+        schema = CollectionSchema(fields)
+        milvus_client.create_collection(collection_name, dimension=DIM, schema=schema)
+
+    if prepare_new_data:
+        entities = []
+        for i in range(NUM_ENTITIES):
+            entity = {
+                USER_ID: i,
+                AGE: (i % 100),
+                DEPOSIT: float(i),
+                PICTURE: rng.random((1, DIM))[0]
+            }
+            entities.append(entity)
+        milvus_client.insert(collection_name, entities)
+        milvus_client.flush(collection_name)
+        print(f"Finish flush collections:{collection_name}")
+
+    index_params = milvus_client.prepare_index_params()
+
+    index_params.add_index(
+        field_name=PICTURE,
+        index_type='IVF_FLAT',
+        metric_type='L2',
+        params={"nlist": 1024}
+    )
+    milvus_client.create_index(collection_name, index_params)
+    milvus_client.load_collection(collection_name)
+    test_query_iterator(milvus_client=milvus_client)
+    test_search_iterator(milvus_client=milvus_client)
+
+
+if __name__ == '__main__':
+    main()

pymilvus/client/utils.py

@@ -379,3 +379,26 @@ def is_scipy_sparse(cls, data: Any):
     "csr_array",
     "spmatrix",
 ]
+
+
+def is_sparse_vector_type(data_type: DataType) -> bool:
+    return data_type == data_type.SPARSE_FLOAT_VECTOR
+
+
+dense_vector_type_set = {DataType.FLOAT_VECTOR, DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR}
+
+
+def is_dense_vector_type(data_type: DataType) -> bool:
+    return data_type in dense_vector_type_set
+
+
+def is_float_vector_type(data_type: DataType):
+    return is_sparse_vector_type(data_type) or is_dense_vector_type(data_type)
+
+
+def is_binary_vector_type(data_type: DataType):
+    return data_type == DataType.BINARY_VECTOR
+
+
+def is_vector_type(data_type: DataType):
+    return is_float_vector_type(data_type) or is_binary_vector_type(data_type)

pymilvus/milvus_client/milvus_client.py

@@ -13,15 +13,19 @@
     OmitZeroDict,
     construct_cost_extra,
 )
+from pymilvus.client.utils import is_vector_type
 from pymilvus.exceptions import (
     DataTypeNotMatchException,
+    ErrorCode,
     MilvusException,
     ParamError,
     PrimaryKeyException,
 )
 from pymilvus.orm import utility
 from pymilvus.orm.collection import CollectionSchema
 from pymilvus.orm.connections import connections
+from pymilvus.orm.constants import FIELDS, METRIC_TYPE, TYPE, UNLIMITED
+from pymilvus.orm.iterator import QueryIterator, SearchIterator
 from pymilvus.orm.types import DataType
 
 from .index import IndexParams
@@ -480,6 +484,126 @@ def query(
 
         return res
 
+    def query_iterator(
+        self,
+        collection_name: str,
+        batch_size: Optional[int] = 1000,
+        limit: Optional[int] = UNLIMITED,
+        filter: Optional[str] = "",
+        output_fields: Optional[List[str]] = None,
+        partition_names: Optional[List[str]] = None,
+        timeout: Optional[float] = None,
+        **kwargs,
+    ):
+        if filter is not None and not isinstance(filter, str):
+            raise DataTypeNotMatchException(message=ExceptionsMessage.ExprType % type(filter))
+
+        conn = self._get_connection()
+        # set up schema for iterator
+        try:
+            schema_dict = conn.describe_collection(collection_name, timeout=timeout, **kwargs)
+        except Exception as ex:
+            logger.error("Failed to describe collection: %s", collection_name)
+            raise ex from ex
+
+        return QueryIterator(
+            connection=conn,
+            collection_name=collection_name,
+            batch_size=batch_size,
+            limit=limit,
+            expr=filter,
+            output_fields=output_fields,
+            partition_names=partition_names,
+            schema=schema_dict,
+            timeout=timeout,
+            **kwargs,
+        )
+
+    def search_iterator(
+        self,
+        collection_name: str,
+        data: Union[List[list], list],
+        batch_size: Optional[int] = 1000,
+        filter: Optional[str] = None,
+        limit: Optional[int] = UNLIMITED,
+        output_fields: Optional[List[str]] = None,
+        search_params: Optional[dict] = None,
+        timeout: Optional[float] = None,
+        partition_names: Optional[List[str]] = None,
+        anns_field: Optional[str] = None,
+        round_decimal: int = -1,
+        **kwargs,
+    ):
+        if filter is not None and not isinstance(filter, str):
+            raise DataTypeNotMatchException(message=ExceptionsMessage.ExprType % type(filter))
+
+        conn = self._get_connection()
+        # set up schema for iterator
+        try:
+            schema_dict = conn.describe_collection(collection_name, timeout=timeout, **kwargs)
+        except Exception as ex:
+            logger.error("Failed to describe collection: %s", collection_name)
+            raise ex from ex
+        # if anns_field is not provided
+        # if only one vector field, use to search
+        # if multiple vector fields, raise exception and abort
+        if anns_field is None or anns_field == "":
+            vec_field = None
+            fields = schema_dict[FIELDS]
+            vec_field_count = 0
+            for field in fields:
+                if is_vector_type(field[TYPE]):
+                    vec_field_count += 1
+                    vec_field = field
+            if vec_field is None:
+                raise MilvusException(
+                    code=ErrorCode.UNEXPECTED_ERROR,
+                    message="there should be at least one vector field in milvus collection",
+                )
+            if vec_field_count > 1:
+                raise MilvusException(
+                    code=ErrorCode.UNEXPECTED_ERROR,
+                    message="must specify anns_field when there are more than one vector field",
+                )
+            anns_field = vec_field["name"]
+            if anns_field is None or anns_field == "":
+                raise MilvusException(
+                    code=ErrorCode.UNEXPECTED_ERROR,
+                    message=f"cannot get anns_field name for search iterator, got:{anns_field}",
+                )
+        # set up metrics type for search_iterator which is mandatory
+        if search_params is None:
+            search_params = {}
+        if METRIC_TYPE not in search_params:
+            indexes = conn.list_indexes(collection_name)
+            for index in indexes:
+                if anns_field == index.index_name:
+                    params = index.params
+                    for param in params:
+                        if param.key == METRIC_TYPE:
+                            search_params[METRIC_TYPE] = param.value
+        if METRIC_TYPE not in search_params:
+            raise MilvusException(
+                ParamError, f"Cannot set up metrics type for anns_field:{anns_field}"
+            )
+
+        return SearchIterator(
+            connection=self._get_connection(),
+            collection_name=collection_name,
+            data=data,
+            ann_field=anns_field,
+            param=search_params,
+            batch_size=batch_size,
+            limit=limit,
+            expr=filter,
+            partition_names=partition_names,
+            output_fields=output_fields,
+            timeout=timeout,
+            round_decimal=round_decimal,
+            schema=schema_dict,
+            **kwargs,
+        )
+
     def get(
         self,
         collection_name: str,

pymilvus/orm/constants.py

@@ -29,6 +29,7 @@
 MILVUS_LIMIT = "limit"
 BATCH_SIZE = "batch_size"
 ID = "id"
+TYPE = "type"
 METRIC_TYPE = "metric_type"
 PARAMS = "params"
 DISTANCE = "distance"