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https://github.com/matsui528/rii
Fast and memory-efficient ANN with a subset-search functionality
https://github.com/matsui528/rii
approximate-nearest-neighbor-search data-compression nearest-neighbor-search product-quantization pybind11
Last synced: about 2 months ago
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Fast and memory-efficient ANN with a subset-search functionality
- Host: GitHub
- URL: https://github.com/matsui528/rii
- Owner: matsui528
- License: mit
- Created: 2018-07-24T09:12:54.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2023-11-22T08:05:08.000Z (10 months ago)
- Last Synced: 2024-05-16T11:08:26.574Z (4 months ago)
- Topics: approximate-nearest-neighbor-search, data-compression, nearest-neighbor-search, product-quantization, pybind11
- Language: C++
- Homepage:
- Size: 251 KB
- Stars: 153
- Watchers: 9
- Forks: 20
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://rii.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/rii)
[](https://pepy.tech/project/rii)
Reconfigurable Inverted Index (Rii): IVFPQ-based fast and memory efficient approximate nearest neighbor search method
with a subset-search functionality.
Reference:
- [Y. Matsui](http://yusukematsui.me/), [R. Hinami](http://www.satoh-lab.nii.ac.jp/member/hinami/), and [S. Satoh](http://research.nii.ac.jp/~satoh/index.html), "**Reconfigurable Inverted Index**", ACM Multimedia 2018 (oral). [**[paper](https://dl.acm.org/ft_gateway.cfm?id=3240630)**] [**[project](http://yusukematsui.me/project/rii/rii.html)**]
## Summary of features
 | 
:---:|:---:
The search can be operated for a subset of a database. | Rii remains fast even after many new items are added.
- Fast and memory efficient ANN. Rii enables you to run billion-scale search in less than 10 ms.
- You can run the search over a **subset** of the whole database
- Rii Remains fast even after many vectors are newly added (i.e., the data structure can be **reconfigured**)
## Installing
You can install the package via pip. This library works with Python 3.6+ on linux/mac/wsl/Windows10
```
pip install rii
```
For windows (maintained by @ashleyabraham)
### Installing in Windows 10 via `pip install`
Requires MS Visual Studio Build tools C++ 14.0 or 14.1 toolset or above to compile and install via pip install
### Pre-compiled binary for Windows 10
Pre-compiled binaries doesn't require MS Visual Studio Build tools
```
#Python 3.8
pip install https://github.com/ashleyabraham/rii/releases/download/v0.2.8/rii-0.2.8-cp38-cp38-win_amd64.whl
```
```
#Python 3.7
pip install https://github.com/ashleyabraham/rii/releases/download/v0.2.8/rii-0.2.8-cp37-cp37m-win_amd64.whl
```
#### OpenMP
OpenMP requires libomp140_x86_64.dll to compile in windows, which is part of MS Visual Studio Build tools and it is not redistributable.
In order to use OpenMP 3.0 /openmp:llvm flag is used which causes warnings of multiple libs loading, use at your descretion when used with other parallel processing library loadings. To supress use
`SET KMP_DUPLICATE_LIB_OK=TRUE`
#### SIMD
The /arch:AVX2 flag is used in MSVC to set appropriate SIMD preprocessors and compiler intrinsics
## [Documentation](https://rii.readthedocs.io/en/latest/index.html)
- [Tutorial](https://rii.readthedocs.io/en/latest/source/tutorial.html)
- [Tips](https://rii.readthedocs.io/en/latest/source/tips.html)
- [API](https://rii.readthedocs.io/en/latest/source/api.html)
## Usage
### Basic ANN
```python
import rii
import nanopq
import numpy as np
N, Nt, D = 10000, 1000, 128
X = np.random.random((N, D)).astype(np.float32) # 10,000 128-dim vectors to be searched
Xt = np.random.random((Nt, D)).astype(np.float32) # 1,000 128-dim vectors for training
q = np.random.random((D,)).astype(np.float32) # a 128-dim vector
# Prepare a PQ/OPQ codec with M=32 sub spaces
codec = nanopq.PQ(M=32).fit(vecs=Xt) # Trained using Xt
# Instantiate a Rii class with the codec
e = rii.Rii(fine_quantizer=codec)
# Add vectors
e.add_configure(vecs=X)
# Search
ids, dists = e.query(q=q, topk=3)
print(ids, dists) # e.g., [7484 8173 1556] [15.06257439 15.38533878 16.16935158]
```
Note that you can construct a PQ codec and instantiate the Rii class at the same time if you want.
```python
e = rii.Rii(fine_quantizer=nanopq.PQ(M=32).fit(vecs=Xt))
e.add_configure(vecs=X)
```
Furthermore, you can even write them in one line by chaining a function.
```python
e = rii.Rii(fine_quantizer=nanopq.PQ(M=32).fit(vecs=Xt)).add_configure(vecs=X)
```
### Subset search
```python
# The search can be conducted over a subset of the database
target_ids = np.array([85, 132, 236, 551, 694, 728, 992, 1234]) # Specified by IDs
# For windows, you must specify dtype=np.int64 as follows.
# target_ids = np.array([85, 132, 236, 551, 694, 728, 992, 1234], dtype=np.int64)
ids, dists = e.query(q=q, topk=3, target_ids=target_ids)
print(ids, dists) # e.g., [728 85 132] [14.80522156 15.92787838 16.28690338]
```
### Data addition and reconfiguration
```python
# Add new vectors
X2 = np.random.random((1000, D)).astype(np.float32)
e.add(vecs=X2) # Now N is 11000
e.query(q=q) # Ok. (0.12 msec / query)
# However, if you add quite a lot of vectors, the search might become slower
# because the data structure has been optimized for the initial item size (N=10000)
X3 = np.random.random((1000000, D)).astype(np.float32)
e.add(vecs=X3) # A lot. Now N is 1011000
e.query(q=q) # Slower (0.96 msec/query)
# In such case, run the reconfigure function. That updates the data structure
e.reconfigure()
e.query(q=q) # Ok. (0.21 msec / query)
```
### I/O by pickling
```python
import pickle
with open('rii.pkl', 'wb') as f:
pickle.dump(e, f)
with open('rii.pkl', 'rb') as f:
e_dumped = pickle.load(f) # e_dumped is identical to e
```
### Util functions
```python
# Print the current parameters
e.print_params()
# Delete all PQ-codes and posting lists. fine_quantizer is kept.
e.clear()
# You can switch the verbose flag
e.verbose = False
# You can merge two Rii instances if they have the same fine_quantizer
e1 = rii.Rii(fine_quantizer=codec)
e2 = rii.Rii(fine_quantizer=codec)
e1.add_configure(vecs=X1)
e2.add_configure(vecs=X2)
e1.merge(e2) # Now e1 contains both X1 and X2
```
## [Examples](./examples)
- [Simple tag search](./examples/tag_search/simple_tag_search.ipynb)
- [Benchmark](./examples/benchmark/)
## Author
- [Yusuke Matsui](http://yusukematsui.me)
## Credits
- The logo is designed by [@richardbmx](https://github.com/richardbmx) ([#4](https://github.com/matsui528/rii/issues/4))
- The windows implementation is by [@ashleyabraham](https://github.com/ashleyabraham) ([#42](https://github.com/matsui528/rii/pull/42))
- Fixing the build ([#50](https://github.com/matsui528/rii/pull/50)) and supporting for ARM CPUs ([#58](https://github.com/matsui528/rii/pull/58)) by [@timzag](https://github.com/timzag)