mirror of https://github.com/milvus-io/milvus.git
parent
a37b422acd
commit
0ff4ddc76c
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@ -14,7 +14,6 @@ set(INDEX_FILES
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Utils.cpp
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VectorMemIndex.cpp
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IndexFactory.cpp
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VectorMemNMIndex.cpp
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)
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if ( BUILD_DISK_ANN STREQUAL "ON" )
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@ -16,7 +16,6 @@
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#include "index/IndexFactory.h"
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#include "index/VectorMemIndex.h"
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#include "index/VectorMemNMIndex.h"
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#include "index/Utils.h"
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#include "index/Meta.h"
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@ -93,10 +92,6 @@ IndexFactory::CreateVectorIndex(const CreateIndexInfo& create_index_info,
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}
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#endif
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if (is_in_nm_list(index_type)) {
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return std::make_unique<VectorMemNMIndex>(
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index_type, metric_type, file_manager);
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}
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// create mem index
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return std::make_unique<VectorMemIndex>(
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index_type, metric_type, file_manager);
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@ -1,133 +0,0 @@
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// 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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#include "common/Slice.h"
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#include "common/Utils.h"
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#include "common/BitsetView.h"
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#include "index/VectorMemNMIndex.h"
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#include "log/Log.h"
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#include "knowhere/factory.h"
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#include "knowhere/comp/time_recorder.h"
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#define RAW_DATA "RAW_DATA"
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#include "common/Tracer.h"
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namespace milvus::index {
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BinarySet
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VectorMemNMIndex::Serialize(const Config& config) {
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knowhere::BinarySet ret;
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auto stat = index_.Serialize(ret);
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if (stat != knowhere::Status::success)
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PanicCodeInfo(
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ErrorCodeEnum::UnexpectedError,
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"failed to serialize index, " + KnowhereStatusString(stat));
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auto deleter = [&](uint8_t*) {}; // avoid repeated deconstruction
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auto raw_data = std::shared_ptr<uint8_t[]>(
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static_cast<uint8_t*>(raw_data_.data()), deleter);
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ret.Append(RAW_DATA, raw_data, raw_data_.size());
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Disassemble(ret);
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return ret;
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}
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void
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VectorMemNMIndex::BuildWithDataset(const DatasetPtr& dataset,
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const Config& config) {
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VectorMemIndex::BuildWithDataset(dataset, config);
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knowhere::TimeRecorder rc("store_raw_data", 1);
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store_raw_data(dataset);
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rc.ElapseFromBegin("Done");
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}
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void
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VectorMemNMIndex::LoadWithoutAssemble(const BinarySet& binary_set,
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const Config& config) {
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VectorMemIndex::LoadWithoutAssemble(binary_set, config);
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if (binary_set.Contains(RAW_DATA)) {
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std::call_once(raw_data_loaded_, [&]() {
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LOG_SEGCORE_INFO_ << "NM index load raw data done!";
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});
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}
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}
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void
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VectorMemNMIndex::AddWithDataset(const DatasetPtr& /*dataset*/,
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const Config& /*config*/) {
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}
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void
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VectorMemNMIndex::Load(const BinarySet& binary_set, const Config& config) {
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VectorMemIndex::Load(binary_set, config);
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if (binary_set.Contains(RAW_DATA)) {
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std::call_once(raw_data_loaded_, [&]() {
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LOG_SEGCORE_INFO_ << "NM index load raw data done!";
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});
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}
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}
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std::unique_ptr<SearchResult>
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VectorMemNMIndex::Query(const DatasetPtr dataset,
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const SearchInfo& search_info,
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const BitsetView& bitset) {
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auto load_raw_data_closure = [&]() { LoadRawData(); }; // hide this pointer
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// load -> query, raw data has been loaded
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// build -> query, this case just for test, should load raw data before query
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std::call_once(raw_data_loaded_, load_raw_data_closure);
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return VectorMemIndex::Query(dataset, search_info, bitset);
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}
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void
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VectorMemNMIndex::store_raw_data(const DatasetPtr& dataset) {
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auto index_type = GetIndexType();
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auto tensor = dataset->GetTensor();
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auto row_num = dataset->GetRows();
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auto dim = dataset->GetDim();
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int64_t data_size;
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if (is_in_bin_list(index_type)) {
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data_size = dim / 8 * row_num;
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} else {
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data_size = dim * row_num * sizeof(float);
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}
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raw_data_.resize(data_size);
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memcpy(raw_data_.data(), tensor, data_size);
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}
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void
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VectorMemNMIndex::LoadRawData() {
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knowhere::BinarySet bs;
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auto stat = index_.Serialize(bs);
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if (stat != knowhere::Status::success)
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PanicCodeInfo(
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ErrorCodeEnum::UnexpectedError,
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"failed to Serialize index, " + KnowhereStatusString(stat));
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auto bptr = std::make_shared<knowhere::Binary>();
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auto deleter = [&](uint8_t*) {}; // avoid repeated deconstruction
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bptr->data = std::shared_ptr<uint8_t[]>(
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static_cast<uint8_t*>(raw_data_.data()), deleter);
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bptr->size = raw_data_.size();
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bs.Append(RAW_DATA, bptr);
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stat = index_.Deserialize(bs);
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if (stat != knowhere::Status::success)
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PanicCodeInfo(
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ErrorCodeEnum::UnexpectedError,
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"failed to Deserialize index, " + KnowhereStatusString(stat));
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milvus::tracer::AddEvent("VectorMemNMIndex_Loaded_RawData");
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}
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} // namespace milvus::index
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@ -1,74 +0,0 @@
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// 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 <map>
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#include <memory>
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#include <string>
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#include <vector>
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#include "index/Utils.h"
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#include "index/VectorMemIndex.h"
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namespace milvus::index {
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class VectorMemNMIndex : public VectorMemIndex {
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public:
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explicit VectorMemNMIndex(
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const IndexType& index_type,
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const MetricType& metric_type,
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storage::FileManagerImplPtr file_manager = nullptr)
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: VectorMemIndex(index_type, metric_type, file_manager) {
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AssertInfo(is_in_nm_list(index_type), "not valid nm index type");
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}
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BinarySet
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Serialize(const Config& config) override;
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void
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BuildWithDataset(const DatasetPtr& dataset,
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const Config& config = {}) override;
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void
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AddWithDataset(const DatasetPtr& dataset, const Config& config) override;
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void
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Load(const BinarySet& binary_set, const Config& config = {}) override;
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std::unique_ptr<SearchResult>
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Query(const DatasetPtr dataset,
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const SearchInfo& search_info,
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const BitsetView& bitset) override;
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void
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LoadWithoutAssemble(const BinarySet& binary_set,
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const Config& config) override;
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private:
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void
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store_raw_data(const DatasetPtr& dataset);
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void
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LoadRawData();
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private:
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std::vector<uint8_t> raw_data_;
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std::once_flag raw_data_loaded_;
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};
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using VectorMemNMIndexPtr = std::unique_ptr<VectorMemNMIndex>;
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} // namespace milvus::index
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@ -16,7 +16,7 @@
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#include "common/SystemProperty.h"
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#include "segcore/FieldIndexing.h"
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#include "index/VectorMemNMIndex.h"
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#include "index/VectorMemIndex.h"
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#include "IndexConfigGenerator.h"
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namespace milvus::segcore {
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@ -50,7 +50,7 @@ VectorFieldIndexing::BuildIndexRange(int64_t ack_beg,
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data_.grow_to_at_least(ack_end);
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for (int chunk_id = ack_beg; chunk_id < ack_end; chunk_id++) {
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const auto& chunk = source->get_chunk(chunk_id);
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auto indexing = std::make_unique<index::VectorMemNMIndex>(
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auto indexing = std::make_unique<index::VectorMemIndex>(
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knowhere::IndexEnum::INDEX_FAISS_IVFFLAT, knowhere::metric::L2);
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auto dataset = knowhere::GenDataSet(
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source->get_size_per_chunk(), dim, chunk.data());
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@ -22,7 +22,7 @@
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#include "common/Schema.h"
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#include "index/ScalarIndexSort.h"
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#include "index/StringIndexSort.h"
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#include "index/VectorMemNMIndex.h"
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#include "index/VectorMemIndex.h"
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#include "query/SearchOnIndex.h"
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#include "segcore/SegmentGrowingImpl.h"
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#include "segcore/SegmentSealedImpl.h"
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@ -793,7 +793,7 @@ GenVecIndexing(int64_t N, int64_t dim, const float* vec) {
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{knowhere::indexparam::NLIST, "1024"},
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{knowhere::meta::DEVICE_ID, 0}};
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auto database = knowhere::GenDataSet(N, dim, vec);
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auto indexing = std::make_unique<index::VectorMemNMIndex>(
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auto indexing = std::make_unique<index::VectorMemIndex>(
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knowhere::IndexEnum::INDEX_FAISS_IVFFLAT, knowhere::metric::L2);
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indexing->BuildWithDataset(database, conf);
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return indexing;
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