[2.4] support the report value in the dml and dql request

examples/hello_cost.py

@@ -0,0 +1,187 @@
+# hello_milvus.py demonstrates the basic operations of PyMilvus, a Python SDK of Milvus.
+# 1. connect to Milvus
+# 2. create collection
+# 3. insert data
+# 4. create index
+# 5. search, query, and hybrid search on entities
+# 6. delete entities by PK
+# 7. drop collection
+import time
+
+import numpy as np
+from pymilvus import (
+    connections,
+    utility,
+    FieldSchema, CollectionSchema, DataType,
+    Collection,
+)
+
+fmt = "\n=== {:30} ===\n"
+search_latency_fmt = "search latency = {:.4f}s"
+num_entities, dim = 10, 8
+
+#################################################################################
+# 1. connect to Milvus
+# Add a new connection alias `default` for Milvus server in `localhost:19530`
+# Actually the "default" alias is a buildin in PyMilvus.
+# If the address of Milvus is the same as `localhost:19530`, you can omit all
+# parameters and call the method as: `connections.connect()`.
+#
+# Note: the `using` parameter of the following methods is default to "default".
+print(fmt.format("start connecting to Milvus"))
+connections.connect("default", host="localhost", port="19530")
+
+collection_name = "hello_cost"
+has = utility.has_collection(collection_name)
+print(f"Does collection {collection_name} exist in Milvus: {has}")
+
+#################################################################################
+# 2. create collection
+# We're going to create a collection with 3 fields.
+# +-+------------+------------+------------------+------------------------------+
+# | | field name | field type | other attributes |       field description      |
+# +-+------------+------------+------------------+------------------------------+
+# |1|    "pk"    |   VarChar  |  is_primary=True |      "primary field"         |
+# | |            |            |   auto_id=False  |                              |
+# +-+------------+------------+------------------+------------------------------+
+# |2|  "random"  |    Double  |                  |      "a double field"        |
+# +-+------------+------------+------------------+------------------------------+
+# |3|"embeddings"| FloatVector|     dim=8        |  "float vector with dim 8"   |
+# +-+------------+------------+------------------+------------------------------+
+fields = [
+    FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100),
+    FieldSchema(name="random", dtype=DataType.DOUBLE),
+    FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim)
+]
+
+schema = CollectionSchema(fields, f"{collection_name} is the simplest demo to introduce the APIs")
+
+print(fmt.format(f"Create collection `{collection_name}`"))
+hello_milvus = Collection(collection_name, schema, consistency_level="Strong")
+
+################################################################################
+# 3. insert data
+# We are going to insert 3000 rows of data into `hello_milvus`
+# Data to be inserted must be organized in fields.
+#
+# The insert() method returns:
+# - either automatically generated primary keys by Milvus if auto_id=True in the schema;
+# - or the existing primary key field from the entities if auto_id=False in the schema.
+
+print(fmt.format("Start inserting entities"))
+rng = np.random.default_rng(seed=19530)
+entities = [
+    # provide the pk field because `auto_id` is set to False
+    [str(i) for i in range(num_entities)],
+    rng.random(num_entities).tolist(),  # field random, only supports list
+    rng.random((num_entities, dim)),    # field embeddings, supports numpy.ndarray and list
+]
+
+insert_result = hello_milvus.insert(entities)
+# OUTPUT:
+# insert result: (insert count: 10, delete count: 0, upsert count: 0, timestamp: 449296288881311748, success count: 10, err count: 0, cost: 1);
+# insert cost: 1
+print(f"insert result: {insert_result};\ninsert cost: {insert_result.cost}")
+
+hello_milvus.flush()
+print(f"Number of entities in Milvus: {hello_milvus.num_entities}")  # check the num_entities
+
+################################################################################
+# 4. create index
+# We are going to create an IVF_FLAT index for hello_milvus collection.
+# create_index() can only be applied to `FloatVector` and `BinaryVector` fields.
+print(fmt.format("Start Creating index IVF_FLAT"))
+index = {
+    "index_type": "IVF_FLAT",
+    "metric_type": "L2",
+    "params": {"nlist": 128},
+}
+
+hello_milvus.create_index("embeddings", index)
+
+################################################################################
+# 5. search, query, and hybrid search
+# After data were inserted into Milvus and indexed, you can perform:
+# - search based on vector similarity
+# - query based on scalar filtering(boolean, int, etc.)
+# - hybrid search based on vector similarity and scalar filtering.
+#
+
+# Before conducting a search or a query, you need to load the data in `hello_milvus` into memory.
+print(fmt.format("Start loading"))
+hello_milvus.load()
+
+# -----------------------------------------------------------------------------
+# search based on vector similarity
+print(fmt.format("Start searching based on vector similarity"))
+vectors_to_search = entities[-1][-2:]
+search_params = {
+    "metric_type": "L2",
+    "params": {"nprobe": 10},
+}
+
+start_time = time.time()
+result = hello_milvus.search(vectors_to_search, "embeddings", search_params, limit=3, output_fields=["random"])
+end_time = time.time()
+
+# OUTPUT:
+# search result: data: ['["id: 8, distance: 0.0, entity: {\'random\': 0.9007387227368949}", "id: 0, distance: 0.49515748023986816, entity: {\'random\': 0.6378742006852851}", "id: 2, distance: 0.5305156707763672, entity: {\'random\': 0.1321158395732429}"]', '["id: 9, distance: 0.0, entity: {\'random\': 0.4494463384561439}", "id: 8, distance: 0.558194100856781, entity: {\'random\': 0.9007387227368949}", "id: 2, distance: 0.7718868255615234, entity: {\'random\': 0.1321158395732429}"]'], cost: 21;
+# search cost: 21
+print(f"search result: {result};\nsearch cost: {result.cost}")
+print(search_latency_fmt.format(end_time - start_time))
+
+# -----------------------------------------------------------------------------
+# query based on scalar filtering(boolean, int, etc.)
+print(fmt.format("Start querying with `random > 0.5`"))
+
+start_time = time.time()
+result = hello_milvus.query(expr="random > 0.5", output_fields=["random", "embeddings"])
+end_time = time.time()
+
+# OUTPUT:
+# query result: data: ["{'random': 0.6378742006852851, 'embeddings': [0.18477614, 0.42930314, 0.40345728, 0.3957196, 0.6963897, 0.24356908, 0.42512414, 0.5724385], 'pk': '0'}", "{'random': 0.744296470467782, 'embeddings': [0.8349225, 0.6614872, 0.98359716, 0.15854438, 0.30939594, 0.23553558, 0.1950739, 0.80361205], 'pk': '4'}", "{'random': 0.6025374094941409, 'embeddings': [0.36677808, 0.218786, 0.25240582, 0.82230526, 0.21011819, 0.16813536, 0.8129038, 0.74800706], 'pk': '7'}", "{'random': 0.9007387227368949, 'embeddings': [0.27464902, 0.07500089, 0.57728964, 0.6654878, 0.8698446, 0.3814792, 0.8825416, 0.58730817], 'pk': '8'}"], extra_info: {'cost': '21'};
+# query cost: 21
+print(f"query result: {result};\nquery cost: {result.extra['cost']}")
+print(search_latency_fmt.format(end_time - start_time))
+
+
+# -----------------------------------------------------------------------------
+# hybrid search
+print(fmt.format("Start hybrid searching with `random > 0.5`"))
+
+start_time = time.time()
+result = hello_milvus.search(vectors_to_search, "embeddings", search_params, limit=3, expr="random > 0.5", output_fields=["random"])
+end_time = time.time()
+
+# OUTPUT:
+# search result: data: ['["id: 8, distance: 0.0, entity: {\'random\': 0.9007387227368949}", "id: 0, distance: 0.49515748023986816, entity: {\'random\': 0.6378742006852851}", "id: 7, distance: 0.670731246471405, entity: {\'random\': 0.6025374094941409}"]', '["id: 8, distance: 0.558194100856781, entity: {\'random\': 0.9007387227368949}", "id: 0, distance: 1.0780366659164429, entity: {\'random\': 0.6378742006852851}", "id: 7, distance: 1.1083570718765259, entity: {\'random\': 0.6025374094941409}"]'], cost: 21;
+# search cost: 21
+print(f"search result: {result};\nsearch cost: {result.cost}")
+print(search_latency_fmt.format(end_time - start_time))
+
+###############################################################################
+# 6. delete entities by PK
+# You can delete entities by their PK values using boolean expressions.
+ids = insert_result.primary_keys
+
+expr = f'pk in ["{ids[0]}" , "{ids[1]}"]'
+print(fmt.format(f"Start deleting with expr `{expr}`"))
+
+result = hello_milvus.query(expr=expr, output_fields=["random", "embeddings"])
+print(f"query before delete by expr=`{expr}` -> result: \n-{result[0]}\n-{result[1]}\n")
+
+delete_result = hello_milvus.delete(expr)
+# OUTPUT:
+# delete result: (insert count: 0, delete count: 2, upsert count: 0, timestamp: 0, success count: 0, err count: 0, cost: 2);
+# delete cost: 2
+print(f"delete result: {delete_result};\ndelete cost: {delete_result.cost}")
+
+result = hello_milvus.query(expr=expr, output_fields=["random", "embeddings"])
+print(f"query after delete by expr=`{expr}` -> result: {result}\n")
+
+
+###############################################################################
+# 7. drop collection
+# Finally, drop the hello_milvus collection
+print(fmt.format(f"Drop collection `{collection_name}`"))
+utility.drop_collection(collection_name)

examples/milvus_client/simple_cost.py

@@ -0,0 +1,74 @@
+import time
+import numpy as np
+from pymilvus import (
+    MilvusClient,
+)
+
+fmt = "\n=== {:30} ===\n"
+dim = 8
+collection_name = "hello_client_cost"
+# milvus_client = MilvusClient("http://localhost:19530")
+milvus_client = MilvusClient(uri="https://in01-20fa6a32462c074.aws-us-west-2.vectordb-uat3.zillizcloud.com:19541",
+                             token="root:j6|y3/g$5Lq,a[TJ^ckphSMs{-F[&Jl)")
+
+has_collection = milvus_client.has_collection(collection_name, timeout=5)
+if has_collection:
+    milvus_client.drop_collection(collection_name)
+milvus_client.create_collection(collection_name, dim, consistency_level="Strong", metric_type="L2")
+
+print(fmt.format("    all collections    "))
+print(milvus_client.list_collections())
+
+print(fmt.format(f"schema of collection {collection_name}"))
+print(milvus_client.describe_collection(collection_name))
+
+rng = np.random.default_rng(seed=19530)
+rows = [
+        {"id": 1, "vector": rng.random((1, dim))[0], "a": 100},
+        {"id": 2, "vector": rng.random((1, dim))[0], "b": 200},
+        {"id": 3, "vector": rng.random((1, dim))[0], "c": 300},
+        {"id": 4, "vector": rng.random((1, dim))[0], "d": 400},
+        {"id": 5, "vector": rng.random((1, dim))[0], "e": 500},
+        {"id": 6, "vector": rng.random((1, dim))[0], "f": 600},
+]
+
+print(fmt.format("Start inserting entities"))
+insert_result = milvus_client.insert(collection_name, rows, progress_bar=True)
+print(fmt.format("Inserting entities done"))
+# OUTPUT:
+# insert result: {'insert_count': 6, 'ids': [1, 2, 3, 4, 5, 6], 'cost': '1'};
+# insert cost: 1
+print(f"insert result: {insert_result};\ninsert cost: {insert_result['cost']}")
+
+print(fmt.format("Start query by specifying primary keys"))
+query_results = milvus_client.query(collection_name, ids=[2])
+# OUTPUT:
+# query result: data: ["{'id': 2, 'vector': [0.9007387, 0.44944635, 0.18477614, 0.42930314, 0.40345728, 0.3957196, 0.6963897, 0.24356908], 'b': 200}"], extra_info: {'cost': '21'}
+# query cost: 21
+print(f"query result: {query_results}\nquery cost: {query_results.extra['cost']}")
+
+upsert_ret = milvus_client.upsert(collection_name, {"id": 2 , "vector": rng.random((1, dim))[0], "g": 100})
+# OUTPUT:
+# upsert result: {'upsert_count': 1, 'cost': '2'}
+# upsert cost: 2
+print(f"upsert result: {upsert_ret}\nupsert cost: {upsert_ret['cost']}")
+
+print(fmt.format("Start query by specifying primary keys"))
+query_results = milvus_client.query(collection_name, ids=[2])
+print(f"query result: {query_results}\nquery cost: {query_results.extra['cost']}")
+
+print(f"start to delete by specifying filter in collection {collection_name}")
+delete_result = milvus_client.delete(collection_name, ids=[6])
+# OUTPUT:
+# delete result: {'delete_count': 1, 'cost': '1'}
+# delete cost: 1
+print(f"delete result: {delete_result}\ndelete cost: {delete_result['cost']}")
+
+rng = np.random.default_rng(seed=19530)
+vectors_to_search = rng.random((1, dim))
+
+print(fmt.format(f"Start search with retrieve serveral fields."))
+result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["pk", "a", "b"])
+print(f"search result: {result}\nsearch cost: {result.extra['cost']}")
+
+milvus_client.drop_collection(collection_name)

pymilvus/client/abstract.py

@@ -4,7 +4,7 @@
 import ujson
 
 from pymilvus.exceptions import DataTypeNotMatchException, ExceptionsMessage, MilvusException
-from pymilvus.grpc_gen import schema_pb2
+from pymilvus.grpc_gen import common_pb2, schema_pb2
 from pymilvus.settings import Config
 
 from . import entity_helper, utils
@@ -195,6 +195,7 @@ def __init__(self, raw: Any):
         self._timestamp = 0
         self._succ_index = []
         self._err_index = []
+        self._cost = 0
 
         self._pack(raw)
 
@@ -234,10 +235,16 @@ def succ_index(self):
     def err_index(self):
         return self._err_index
 
+    # The unit of this cost is vcu, similar to token
+    @property
+    def cost(self):
+        return self._cost
+
     def __str__(self):
         return (
             f"(insert count: {self._insert_cnt}, delete count: {self._delete_cnt}, upsert count: {self._upsert_cnt}, "
-            f"timestamp: {self._timestamp}, success count: {self.succ_count}, err count: {self.err_count})"
+            f"timestamp: {self._timestamp}, success count: {self.succ_count}, err count: {self.err_count}, "
+            f"cost: {self._cost})"
         )
 
     __repr__ = __str__
@@ -262,6 +269,9 @@ def _pack(self, raw: Any):
         self._timestamp = raw.timestamp
         self._succ_index = raw.succ_index
         self._err_index = raw.err_index
+        self._cost = int(
+            raw.status.extra_info["report_value"] if raw.status and raw.status.extra_info else "0"
+        )
 
 
 class SequenceIterator:
@@ -374,10 +384,17 @@ def __str__(self):
 class SearchResult(list):
     """nq results: List[Hits]"""
 
-    def __init__(self, res: schema_pb2.SearchResultData, round_decimal: Optional[int] = None):
+    def __init__(
+        self,
+        res: schema_pb2.SearchResultData,
+        round_decimal: Optional[int] = None,
+        status: Optional[common_pb2.Status] = None,
+    ):
         self._nq = res.num_queries
         all_topks = res.topks
 
+        self.cost = int(status.extra_info["report_value"] if status and status.extra_info else "0")
+
         output_fields = res.output_fields
         fields_data = res.fields_data
 
@@ -497,7 +514,7 @@ def __iter__(self) -> SequenceIterator:
 
     def __str__(self) -> str:
         """Only print at most 10 query results"""
-        return str(list(map(str, self[:10])))
+        return f"data: {list(map(str, self[:10]))} {'...' if len(self) > 10 else ''}, cost: {self.cost}"
 
     __repr__ = __str__
 

pymilvus/client/asynch.py

@@ -164,7 +164,7 @@ def exception(self):
 class SearchFuture(Future):
     def on_response(self, response: milvus_pb2.SearchResults):
         check_status(response.status)
-        return SearchResult(response.results)
+        return SearchResult(response.results, status=response.status)
 
 
 class MutationFuture(Future):

pymilvus/client/grpc_handler.py

@@ -44,6 +44,7 @@
     CompactionState,
     DatabaseInfo,
     DataType,
+    ExtraList,
     GrantInfo,
     Group,
     IndexState,
@@ -57,6 +58,7 @@
     State,
     Status,
     UserInfo,
+    get_cost_extra,
 )
 from .utils import (
     check_invalid_binary_vector,
@@ -732,7 +734,7 @@ def _execute_search(
             response = self._stub.Search(request, timeout=timeout)
             check_status(response.status)
             round_decimal = kwargs.get("round_decimal", -1)
-            return SearchResult(response.results, round_decimal)
+            return SearchResult(response.results, round_decimal, status=response.status)
 
         except Exception as e:
             if kwargs.get("_async", False):
@@ -751,7 +753,7 @@ def _execute_hybrid_search(
             response = self._stub.HybridSearch(request, timeout=timeout)
             check_status(response.status)
             round_decimal = kwargs.get("round_decimal", -1)
-            return SearchResult(response.results, round_decimal)
+            return SearchResult(response.results, round_decimal, status=response.status)
 
         except Exception as e:
             if kwargs.get("_async", False):
@@ -1519,7 +1521,7 @@ def query(
                 response.fields_data, index, dynamic_fields
             )
             results.append(entity_row_data)
-        return results
+        return ExtraList(results, extra=get_cost_extra(response.status))
 
     @retry_on_rpc_failure()
     def load_balance(