Support numpy type value for bulkwriter

examples/example_bulkwriter.py

@@ -232,7 +232,8 @@ def test_all_types_writer(bin_vec: bool, schema: CollectionSchema)->list:
         ),
     ) as remote_writer:
         print("Append rows")
-        for i in range(10000):
+        batch_count = 10000
+        for i in range(batch_count):
             row = {
                 "id": i,
                 "bool": True if i%5 == 0 else False,
@@ -249,6 +250,23 @@ def test_all_types_writer(bin_vec: bool, schema: CollectionSchema)->list:
             }
             remote_writer.append_row(row)
 
+        # append rows by numpy type
+        for i in range(batch_count):
+            remote_writer.append_row({
+                "id": np.int64(i+batch_count),
+                "bool": True if i % 3 == 0 else False,
+                "int8": np.int8(i%128),
+                "int16": np.int16(i%1000),
+                "int32": np.int32(i%100000),
+                "int64": np.int64(i),
+                "float": np.float32(i/3),
+                "double": np.float64(i/7),
+                "varchar": f"varchar_{i}",
+                "json": json.dumps({"dummy": i, "ok": f"name_{i}"}),
+                "vector": gen_binary_vector() if bin_vec else gen_float_vector(),
+                f"dynamic_{i}": i,
+            })
+
         print("Generate data files...")
         remote_writer.commit()
         print(f"Data files have been uploaded: {remote_writer.batch_files}")

pymilvus/bulk_writer/bulk_writer.py

@@ -10,9 +10,12 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
+import json
 import logging
 from threading import Lock
 
+import numpy as np
+
 from pymilvus.client.types import DataType
 from pymilvus.exceptions import MilvusException
 from pymilvus.orm.schema import CollectionSchema
@@ -85,6 +88,16 @@ def commit(self, **kwargs):
     def data_path(self):
         return ""
 
+    def _try_convert_json(self, field_name: str, obj: object):
+        if isinstance(obj, str):
+            try:
+                return json.loads(obj)
+            except Exception as e:
+                self._throw(
+                    f"Illegal JSON value for field '{field_name}', type mismatch or illegal format, error: {e}"
+                )
+        return obj
+
     def _throw(self, msg: str):
         logger.error(msg)
         raise MilvusException(message=msg)
@@ -109,10 +122,12 @@ def _verify_row(self, row: dict):
             dtype = DataType(field.dtype)
             validator = TYPE_VALIDATOR[dtype.name]
             if dtype in {DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR}:
+                if isinstance(row[field.name], np.ndarray):
+                    row[field.name] = row[field.name].tolist()
                 dim = field.params["dim"]
                 if not validator(row[field.name], dim):
                     self._throw(
-                        f"Illegal vector data for vector field: '{dtype.name}',"
+                        f"Illegal vector data for vector field: '{field.name}',"
                         f" dim is not {dim} or type mismatch"
                     )
 
@@ -126,20 +141,23 @@ def _verify_row(self, row: dict):
                 max_len = field.params["max_length"]
                 if not validator(row[field.name], max_len):
                     self._throw(
-                        f"Illegal varchar value for field '{dtype.name}',"
+                        f"Illegal varchar value for field '{field.name}',"
                         f" length exceeds {max_len} or type mismatch"
                     )
 
                 row_size = row_size + len(row[field.name])
             elif dtype == DataType.JSON:
+                row[field.name] = self._try_convert_json(field.name, row[field.name])
                 if not validator(row[field.name]):
-                    self._throw(f"Illegal varchar value for field '{dtype.name}', type mismatch")
+                    self._throw(f"Illegal JSON value for field '{field.name}', type mismatch")
 
                 row_size = row_size + len(row[field.name])
             else:
+                if isinstance(row[field.name], np.generic):
+                    row[field.name] = row[field.name].item()
                 if not validator(row[field.name]):
                     self._throw(
-                        f"Illegal scalar value for field '{dtype.name}', value overflow or type mismatch"
+                        f"Illegal scalar value for field '{field.name}', value overflow or type mismatch"
                     )
 
                 row_size = row_size + TYPE_SIZE[dtype.name]