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Increment to version 1.13.2 for serialization fix (#4734)
Summary: Pull Request resolved: #4734 we intentionally made a backwards incompatible change to simplify rabitq serialization. This hotfix will establish the right baseline. Then we can fix the backwards compatibility test. Reviewed By: alibeklfc, subhadeepkaran Differential Revision: D89564512 fbshipit-source-id: ce9535bbe51aad715e917d601ab9391c83bea255
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CHANGELOG.md

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## [Unreleased]
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## [1.13.2] - 2025-12-19
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Added
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- 033e6acc6995d1adb9ea5317fadff152df3116bc Add RaBitQStats for tracking two-stage search filtering effectiveness (#4723)
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- 64a236744b9ec4ca18d6b5d4e21c898f861a242a Add multi-bit support for IndexIVFRaBitQFastScan (#4722)
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- cd2af8bc37628ac63dad736067200ac1291a77e7 Implement `IndexRefinePanorama` (#4683)
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- 18d20febb579788be74724cd5a0bbc71632f978b Add multi-bit support for IndexRaBitQFastScan (#4721)
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- 7372bc7982e6d15cd2048744094d510ddeb7495b Reapply `IndexHNSWFlatPanorama` with backward compatible serialization (#4692)
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- 1721ebff6de6ed5a8481302123479be9d85059a2 Also add backwards compatible check for binary (#4714)
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- 98bf8b3808ba325660006623afe177951579f3d9 Implement remaining `IndexFlatPanorama` functions (#4694)
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- a695814f4c108a2ba7a82da9ac2b526b1fff118c Enable Intel ScalableVectorSearch support (#4548)
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- d81a08e2409bb0ec2d2d6a2442d4beb7b2a8cbc9 Index serialization backward compatibility test (#4706)
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Changed
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- 9a6c02b061c4142a8e566d9d3360326140c95ad8 Rename RaBitQ factor structs for clarity and reorganize tests (#4730)
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- 281a999abab90aed5b145b193aaff043b52045c2 Enable cuVS in Faiss (#4729)
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- 6452d192cfaf67faf21808c285dfad1ec13b3d39 Update SVS binary to v0.1.0 (#4726)
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- 1ea99d8073bfd5d20bfee4d63c4bb049b7d63154 clean up serialization tests (#4700)
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- 89dd5a7b4ec9eb1f8c540828fc93c540f74c6699 Enable `-Wunused-exception-parameter` in faiss/PACKAGE +1
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Fixed
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- 5b19fca3f057b837ac898af52a8eb801c4744892 Allow over-writing centroid files to mitigate [S603653] (#4725)
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- 337dfe8043a9bd9b8f4e2f3ec3c23fffb7b02654 Fix typos in demos, benchs, and other directories" (#4719)
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- aea2b6bc8543f8a9b1b38e537cd55bcd9f6eb059 fix(docs): broken link to SVS in INSTALL.md (#4724)
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- 4627695179e304adba1addd342355f366500149c Fix SVS Python tutorial (#4720)
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- ac2e3abe3890fc7eaff06888878915bbda9c25b0 Update c_api install docs for CMake build system (#4702)
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- abc294419ae2d235aea4a15813e168e742e34995 fix broken test due to renaming to avoid lint (#4712)
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- 3d4d59fc3bd1986b31334c1a8bc6192a773b5666 Fix typos in demos, benchs, and other directories (#4709)
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## [1.13.1] - 2025-12-02
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CMakeLists.txt

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endif()
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project(faiss
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VERSION 1.13.1
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VERSION 1.13.2
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DESCRIPTION "A library for efficient similarity search and clustering of dense vectors."
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HOMEPAGE_URL "https://github.com/facebookresearch/faiss"
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LANGUAGES ${FAISS_LANGUAGES})

INSTALL.md

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``` shell
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# CPU-only version
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$ conda install -c pytorch faiss-cpu=1.13.1
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$ conda install -c pytorch faiss-cpu=1.13.2
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# GPU(+CPU) version
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$ conda install -c pytorch -c nvidia faiss-gpu=1.13.1
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$ conda install -c pytorch -c nvidia faiss-gpu=1.13.2
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# GPU(+CPU) version with NVIDIA cuVS
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$ conda install -c pytorch -c nvidia -c rapidsai -c conda-forge libnvjitlink faiss-gpu-cuvs=1.13.1
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$ conda install -c pytorch -c nvidia -c rapidsai -c conda-forge libnvjitlink faiss-gpu-cuvs=1.13.2
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# GPU(+CPU) version using AMD ROCm not yet available
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```
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$ conda install -c pytorch/label/nightly faiss-cpu
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# GPU(+CPU) version
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$ conda install -c pytorch/label/nightly -c nvidia faiss-gpu=1.13.1
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$ conda install -c pytorch/label/nightly -c nvidia faiss-gpu=1.13.2
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# GPU(+CPU) version with NVIDIA cuVS (package built with CUDA 12.6)
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conda install -c pytorch -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version=12.6'

faiss/Index.h

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#define FAISS_VERSION_MAJOR 1
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#define FAISS_VERSION_MINOR 13
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#define FAISS_VERSION_PATCH 1
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#define FAISS_VERSION_PATCH 2
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// Macro to combine the version components into a single string
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#ifndef FAISS_STRINGIFY

faiss/python/setup.py

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"""
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setup(
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name="faiss",
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version="1.13.1",
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version="1.13.2",
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description="A library for efficient similarity search and clustering of dense vectors",
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long_description=long_description,
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long_description_content_type="text/plain",

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