mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-12 02:13:36 +00:00
Tia Lim
0f0f728166
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5349 # this is a no-op change Ran `black -l 80` over all tracked Python files under `fbcode/faiss/`, using line length 80 to match faiss's documented coding style (`CONTRIBUTING.md`: "80 character line length (both for C++ and Python)"). 172 files were reformatted; 28 were already compliant. This is a pure-formatting change. `black` runs its AST-equivalence safety check on every file, so no identifiers, string/numeric values, operators, or control flow were altered — only whitespace, indentation, line wrapping, blank lines, trailing commas, and string-quote normalization (single to double quotes). `fbcode/faiss/` is intentionally excluded from Meta's standard Python autoformatter (`arc f` / `arc lint -a` report no formatter for these paths) because it mirrors to public GitHub, so the Python had drifted out of any consistent style. The C++ is already clang-format-clean (0 of 807 files change), so this effort is Python-only. This diff brings the Python to canonical `black` formatting. Excluded `demos/index_pq_flat_separate_codes_from_codebook.py`: it contains a Jupyter `!rm` shell-magic line and is not valid Python, so `black` cannot parse it. This is the base of a 3-diff stack: (1) this black reformat; (2) `.flake8` alignment (max-line-length 80 + black-compatible ignores E203/W503); (3) genuine lint fixes (F401/F403/F841/B011). Reviewed By: mnorris11 Differential Revision: D109736099 fbshipit-source-id: 9a054635766ed11aa3760a788395babd3d198abf
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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# Portions Copyright 2025 Intel Corporation
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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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#
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import numpy as np
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import faiss # make faiss available
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d = 64 # dimension
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nb = 100000 # database size
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nq = 10000 # nb of queries
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np.random.seed(1234) # make reproducible
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xb = np.random.random((nb, d)).astype("float32")
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xb[:, 0] += np.arange(nb) / 1000.0
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xq = np.random.random((nq, d)).astype("float32")
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xq[:, 0] += np.arange(nq) / 1000.0
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index = faiss.IndexSVSVamana(d, 64) # build the index (DynamicVamana, float32)
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print(index.is_trained)
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index.add(xb) # add vectors to the index
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print(index.ntotal)
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k = 4 # we want to see 4 nearest neighbors
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print(f"{k} nearest neighbors of the first 5 vectors")
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D, I = index.search(xb[:5], k) # sanity check
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print(I)
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print(D)
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D, I = index.search(xq, k) # actual search
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print(f"{k} nearest neighbors of the 5 first query vectors")
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print(I[:5]) # neighbors of the 5 first queries
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print(f"{k} nearest neighbors of the 5 last query vectors")
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print(I[-5:]) # neighbors of the 5 last queries
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faiss.write_index(index, "index.faiss")
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reloaded = faiss.read_index("index.faiss")
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D, I = reloaded.search(xq, k) # search with the reloaded
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print(f"{k} nearest neighbors of the 5 first query vectors (after reloading)")
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print(I[:5]) # neighbors of the 5 first queries
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print(f"{k} nearest neighbors of the 5 last query vectors (after reloading)")
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print(I[-5:]) # neighbors of the 5 last queries
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flat_idx_fac = faiss.index_factory(
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d, "SVSFlat", faiss.METRIC_L2
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) # example of using factory for SVS Flat
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flat_idx_fac.add(xb)
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flat_idx_fac.search(xq, k)
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uncompressed_idx_fac = faiss.index_factory(
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d, "SVSVamana64", faiss.METRIC_L2
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) # example of using factory for SVS Vamana uncompressed
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uncompressed_idx_fac.add(xb)
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uncompressed_idx_fac.search(xq, k)
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lvq_idx = faiss.IndexSVSVamanaLVQ(
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d, 32, faiss.METRIC_L2, faiss.SVS_LVQ4x8
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) # example of using SVS Vamana LVQ
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lvq_idx_fac_2 = faiss.index_factory(
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d, "SVSVamana32,LVQ4x4", faiss.METRIC_L2
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) # example of using factory for SVS Vamana LVQ
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lvq_idx_fac_2.add(xb)
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lvq_idx_fac_2.search(xq, k)
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leanvec_idx = faiss.IndexSVSVamanaLeanVec(
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d, 32, faiss.METRIC_L2, 0, faiss.SVS_LeanVec4x4
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) # example of using SVS Vamana LeanVec
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leanvec_idx_fac = faiss.index_factory(
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d, "SVSVamana32,LeanVec4x4", faiss.METRIC_L2
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) # example of using factory for SVS Vamana LeanVec
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leanvec_idx_fac.train(xb)
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leanvec_idx_fac.add(xb)
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leanvec_idx_fac.search(xq, k)
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leanvec_idx_fac2 = faiss.index_factory(
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d, "SVSVamana64,LeanVec4x4_16", faiss.METRIC_L2
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) # example of using factory for SVS Vamana LeanVec. leanvec_dims is 16
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leanvec_idx_fac2.train(xb)
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leanvec_idx_fac2.add(xb)
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leanvec_idx_fac2.search(xq, k)
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