wehub-resource-sync
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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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# coding=UTF-8
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import hnswlib
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import numpy as np
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from paddlenlp.utils.log import logger
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def build_index(args, data_loader, model):
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index = hnswlib.Index(space="ip", dim=args.output_emb_size)
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# Initializing index
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# max_elements - the maximum number of elements (capacity). Will throw an exception if exceeded
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# during insertion of an element.
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# The capacity can be increased by saving/loading the index, see below.
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#
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# ef_construction - controls index search speed/build speed tradeoff
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#
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# M - is tightly connected with internal dimensionality of the data. Strongly affects memory consumption (~M)
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# Higher M leads to higher accuracy/run_time at fixed ef/efConstruction
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index.init_index(max_elements=args.hnsw_max_elements, ef_construction=args.hnsw_ef, M=args.hnsw_m)
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# Controlling the recall by setting ef:
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# higher ef leads to better accuracy, but slower search
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index.set_ef(args.hnsw_ef)
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# Set number of threads used during batch search/construction
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# By default using all available cores
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index.set_num_threads(16)
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logger.info("start build index..........")
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all_embeddings = []
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for text_embeddings in model.get_semantic_embedding(data_loader):
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all_embeddings.append(text_embeddings.numpy())
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all_embeddings = np.concatenate(all_embeddings, axis=0)
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index.add_items(all_embeddings)
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logger.info("Total index number:{}".format(index.get_current_count()))
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return index
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