Summary:
This PR adds a Python HNSW tutorial corresponding to the existing C++ HNSW tutorial.
The example creates synthetic vectors, builds an IndexHNSWFlat index, runs a search, and prints the nearest-neighbor IDs and distances for the final queries.
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5260
Reviewed By: mnorris11
Differential Revision: D107272616
Pulled By: alibeklfc
fbshipit-source-id: 721d7a1a95aa79150d6cb9653bd803065c82dcd7
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/issues/4450
This pull request introduces support for [Intel ScalableVectorSearch](https://github.com/intel/ScalableVectorSearch), integrating Intel's proprietary LVQ and LeanVec technologies in binary form. The following index types are now supported: `IndexSVSVamana`, `IndexSVSVamanaLVQ`, `IndexSVSVamanaLeanVec`, and `IndexSVSFlat`. `IndexSVSVamana` and `IndexSVSFlat` utilizes SVS open-source float32/float16/int8 implementation.
Key features and enhancements include:
- Implemented `search`, `add`, and `remove_ids`
- Implemented filter search and `range_search`
- Enabled SVS open-source fp16 and scalar quantization implementation
- Enabled factory methods
- Enabled save/load
- Enabled Python bindings
- Enable fallback mechanism that fallbacks to 8-bit scalar quantization if LVQ/LeanVec is used on non-intel hardware CPUs
- Added examples in both Python and C++ under the `tutorial/` directory
- Added `FAISS_ENABLE_SVS` flag to allows users to optionally enable SVS
TODOs:
- Support more indices such as `IndexSVSIVF` and `IndexSVSFlat` with LVQ and LeanVec
- Documentation
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/4548
Reviewed By: subhadeepkaran
Differential Revision: D84713772
Pulled By: mnorris11
fbshipit-source-id: 290365eb3337f685dd46696935b621c57dad3174
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3480
This tutorial summarize the methods to construct different indexs for PQFastScan refinement. It shows how the choice can impact on accuracy.
Reviewed By: junjieqi
Differential Revision: D57799598
fbshipit-source-id: a75c52c60a5217366f3361676da8f03f0c4a9feb
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/3465
This commit include python version of tutorial for FastScan. It includes all the parameters enabled within PQFastScan.
Reviewed By: junjieqi
Differential Revision: D57594044
fbshipit-source-id: cb12679b6fc241a654b9545c5bc7bd0517aa1813
Changelog:
- changed license: BSD+Patents -> MIT
- propagates exceptions raised in sub-indexes of IndexShards and IndexReplicas
- support for searching several inverted lists in parallel (parallel_mode != 0)
- better support for PQ codes where nbit != 8 or 16
- IVFSpectralHash implementation: spectral hash codes inside an IVF
- 6-bit per component scalar quantizer (4 and 8 bit were already supported)
- combinations of inverted lists: HStackInvertedLists and VStackInvertedLists
- configurable number of threads for OnDiskInvertedLists prefetching (including 0=no prefetch)
- more test and demo code compatible with Python 3 (print with parentheses)
- refactored benchmark code: data loading is now in a single file
* Remove transitional print import.
* Add example for multiple GPUs in python.
* Add example on GPU in C++.
* Add example on multiple GPUs in C++.
* Add IVFFlat example on GPU in python.
* Add Makefile for C++ tutorial examples.
* Add IVF index on GPU example in C++.
* moved most FAISS_ASSERT calls to C++ exceptions, and adjusted
memory allocation to avoid mem leaks
* added an IndexIVFScalarQuantizer type that offers an
intermediate compression between IVFFlat and IVFPQ
* support removal of indices in IndexIDMap / IndexFlat combination
* various fixes in GPU code
Saim-naushad