fix

Signed-off-by: lixinguo <xinguo.li@zilliz.com>

internal/core/src/storage/Event.cpp

@@ -175,10 +175,17 @@ DescriptorEventData::Serialize() {
     auto fix_part_data = fix_part.Serialize();
     nlohmann::json extras_json;
     for (auto v : extras) {
-        if (v.first == NULLABLE) {
-            extras_json.emplace(v.first, std::any_cast<bool>(v.second));
-        } else {
-            extras_json.emplace(v.first, std::any_cast<std::string>(v.second));
+        try {
+            if (v.first == NULLABLE) {
+                extras_json.emplace(v.first, std::any_cast<bool>(v.second));
+            } else {
+                extras_json.emplace(v.first,
+                                    std::any_cast<std::string>(v.second));
+            }
+        } catch (const std::bad_any_cast& e) {
+            std::cout << " lxg v.first " << v.first << std::endl;
+            std::cout << "lxg Type conversion failed: " << e.what()
+                      << std::endl;
         }
     }
     std::string extras_string = extras_json.dump();

issue.py

@@ -0,0 +1,41 @@
+from pymilvus import CollectionSchema, FieldSchema, Collection, connections, DataType, Partition, utility
+import numpy as np
+import random
+import time
+
+# 建collection —————————————————————————————————————————————————————————————————————————————————————————————
+dim = 128
+int64_field = FieldSchema(name="int64", dtype=DataType.INT64, is_primary=True)
+float_field = FieldSchema(name="float", dtype=DataType.FLOAT)
+float_vector = FieldSchema(
+    name="float_vector", dtype=DataType.FLOAT_VECTOR, dim=dim)
+schema = CollectionSchema(fields=[int64_field, float_field, float_vector])
+connections.connect()
+utility.drop_collection("test_upsert")
+collection = Collection("test_upsert", schema=schema)
+
+# 建partition 和插入数据 ————————————————————————————————————————————————————————————————————————————————————
+# partition = Partition(collection, "_default")
+# vectors = [[random.random() for _ in range(dim)] for _ in range(1500)]
+# partition.insert([[i for i in range(1500)], [np.float32(i)
+#                  for i in range(1500)], vectors])
+
+index = {"index_type": "DISKANN", "metric_type": "L2", "params": {}}
+# collection.create_index("float_vector", index)
+# collection.load()  # partition_new.load()
+
+nb = 17
+vectors = [[random.random() for _ in range(dim)] for _ in range(nb)]
+data = [[(i+10000) for i in range(nb)], [np.float32(i+10000)
+                                         for i in range(nb)], vectors]
+collection.insert(data=data)
+collection.create_index("float_vector", index)
+collection.load()
+
+search_params = {"metric_type": "L2", "params": {"search_list": 10}}
+output_fields = ["int64", "float", "float_vector"]
+res = collection.search(vectors, "float_vector",
+                        search_params, limit=10,
+                        output_fields=output_fields)
+
+print(len(res))