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https://github.com/milvus-io/milvus/issues/35528 This PR adds json index support for json and dynamic fields. Now you can only do unary query like 'a["b"] > 1' using this index. We will support more filter type later. basic usage: ``` collection.create_index("json_field", {"index_type": "INVERTED", "params": {"json_cast_type": DataType.STRING, "json_path": 'json_field["a"]["b"]'}}) ``` There are some limits to use this index: 1. If a record does not have the json path you specify, it will be ignored and there will not be an error. 2. If a value of the json path fails to be cast to the type you specify, it will be ignored and there will not be an error. 3. A specific json path can have only one json index. 4. If you try to create more than one json indexes for one json field, sdk(pymilvus<=2.4.7) may return immediately because of internal implementation. This will be fixed in a later version. --------- Signed-off-by: sunby <sunbingyi1992@gmail.com>
225 lines
6.0 KiB
C++
225 lines
6.0 KiB
C++
// Licensed to the LF AI & Data foundation under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <optional>
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#include <stdexcept>
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#include <string>
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#include <utility>
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#include "common/EasyAssert.h"
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#include "common/Types.h"
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namespace milvus {
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using TypeParams = std::map<std::string, std::string>;
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using TokenizerParams = std::string;
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TokenizerParams
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ParseTokenizerParams(const TypeParams& params);
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class FieldMeta {
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public:
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static const FieldMeta RowIdMeta;
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FieldMeta(const FieldMeta&) = default;
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FieldMeta(FieldMeta&&) = default;
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FieldMeta&
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operator=(const FieldMeta&) = delete;
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FieldMeta&
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operator=(FieldMeta&&) = default;
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FieldMeta(FieldName name, FieldId id, DataType type, bool nullable)
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: name_(std::move(name)), id_(id), type_(type), nullable_(nullable) {
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Assert(!IsVectorDataType(type_));
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}
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FieldMeta(FieldName name,
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FieldId id,
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DataType type,
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int64_t max_length,
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bool nullable)
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: name_(std::move(name)),
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id_(id),
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type_(type),
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nullable_(nullable),
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string_info_(StringInfo{max_length}) {
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Assert(IsStringDataType(type_));
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}
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FieldMeta(FieldName name,
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FieldId id,
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DataType type,
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int64_t max_length,
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bool nullable,
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bool enable_match,
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bool enable_analyzer,
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std::map<std::string, std::string>& params)
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: name_(std::move(name)),
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id_(id),
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type_(type),
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nullable_(nullable),
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string_info_(StringInfo{
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max_length, enable_match, enable_analyzer, std::move(params)}) {
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Assert(IsStringDataType(type_));
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}
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FieldMeta(FieldName name,
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FieldId id,
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DataType type,
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DataType element_type,
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bool nullable)
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: name_(std::move(name)),
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id_(id),
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type_(type),
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element_type_(element_type),
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nullable_(nullable) {
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Assert(IsArrayDataType(type_));
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}
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// pass in any value for dim for sparse vector is ok as it'll never be used:
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// get_dim() not allowed to be invoked on a sparse vector field.
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FieldMeta(FieldName name,
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FieldId id,
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DataType type,
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int64_t dim,
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std::optional<knowhere::MetricType> metric_type,
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bool nullable)
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: name_(std::move(name)),
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id_(id),
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type_(type),
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nullable_(nullable),
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vector_info_(VectorInfo{dim, std::move(metric_type)}) {
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Assert(IsVectorDataType(type_));
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Assert(!nullable);
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}
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int64_t
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get_dim() const {
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Assert(IsVectorDataType(type_));
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// should not attempt to get dim() of a sparse vector from schema.
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Assert(!IsSparseFloatVectorDataType(type_));
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Assert(vector_info_.has_value());
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return vector_info_->dim_;
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}
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int64_t
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get_max_len() const {
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Assert(IsStringDataType(type_));
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Assert(string_info_.has_value());
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return string_info_->max_length;
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}
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bool
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enable_match() const;
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bool
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enable_analyzer() const;
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TokenizerParams
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get_analyzer_params() const;
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std::optional<knowhere::MetricType>
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get_metric_type() const {
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Assert(IsVectorDataType(type_));
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Assert(vector_info_.has_value());
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return vector_info_->metric_type_;
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}
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const FieldName&
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get_name() const {
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return name_;
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}
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const FieldId&
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get_id() const {
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return id_;
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}
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DataType
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get_data_type() const {
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return type_;
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}
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DataType
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get_element_type() const {
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return element_type_;
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}
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bool
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is_vector() const {
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return IsVectorDataType(type_);
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}
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bool
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is_json() const {
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return type_ == DataType::JSON;
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}
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bool
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is_string() const {
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return IsStringDataType(type_);
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}
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bool
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is_nullable() const {
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return nullable_;
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}
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size_t
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get_sizeof() const {
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AssertInfo(!IsSparseFloatVectorDataType(type_),
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"should not attempt to get_sizeof() of a sparse vector from "
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"schema");
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static const size_t ARRAY_SIZE = 128;
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static const size_t JSON_SIZE = 512;
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if (is_vector()) {
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return GetDataTypeSize(type_, get_dim());
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} else if (is_string()) {
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Assert(string_info_.has_value());
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return string_info_->max_length;
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} else if (IsVariableDataType(type_)) {
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return type_ == DataType::ARRAY ? ARRAY_SIZE : JSON_SIZE;
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} else {
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return GetDataTypeSize(type_);
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}
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}
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public:
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static FieldMeta
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ParseFrom(const milvus::proto::schema::FieldSchema& schema_proto);
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private:
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struct VectorInfo {
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int64_t dim_;
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std::optional<knowhere::MetricType> metric_type_;
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};
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struct StringInfo {
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int64_t max_length;
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bool enable_match;
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bool enable_analyzer;
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std::map<std::string, std::string> params;
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};
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FieldName name_;
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FieldId id_;
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DataType type_ = DataType::NONE;
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DataType element_type_ = DataType::NONE;
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bool nullable_;
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std::optional<VectorInfo> vector_info_;
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std::optional<StringInfo> string_info_;
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};
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} // namespace milvus
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