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A Beginner’s Guide to FAISS, use-cases, Mathematical foundations & implementation
FAISS (Facebook AI Similarity Search) is an open-source library developed by Facebook AI Research (FAIR) for high-dimensional data similarity search and clustering. It’s very beneficial for large-scale machine learning tasks including nearest neighbour search, clustering, and approximate nearest neighbour search. In this blog post, we will discuss FAISS, including its benefits, use cases, and how to implement it using Python.
What is similarity search?
Real-world objects and concepts are frequently represented in machine learning as a set of continuous numbers, sometimes known as vector embeddings. This approach converts the similarity of items as interpreted by us into a vector space
This means that when we represent images or text as vector embeddings, the semantic similarity of the images or text is expressed by how near their vectors are in the vector space. As a result, we want to look at the distance between the objects’ vectors to find similar objects
A similarity search for vectors is a technique used to locate vectors in a high-dimensional space that are similar to a specified query vector. Vectors can be any type of data that can be represented as a vector, including text, images, and audio.
These vector representations (embeddings) are frequently generated by training models based on input data and tasks. Word2Vec, GLoVE, and other popular models are used to generate embeddings from text data, whereas CNN models such as VGG are frequently employed to generate picture embeddings.
Why FAISS?
Similarity search is a popular problem in machine learning, and it becomes more difficult as data dimensionality and size increase. For large-scale datasets, traditional similarity search methods such as linear search and k-d trees become unfeasible. FAISS solves this issue by providing efficient algorithms for similarity search and clustering that are capable of dealing with large-scale, high-dimensional data.
FAISS has numerous indexing structures that can be utilised to speed up the search, including LSH, IVF, and PQ. It also includes GPU support, which enables further search acceleration. FAISS also offers an estimated nearest neighbour search, which delivers approximate nearest neighbours with a quality guarantee.
Use Cases for FAISS
FAISS has a wide range of applications, including:
- Image retrieval: FAISS can be used to search for comparable photos in a huge dataset efficiently.
- Natural Language Processing: FAISS can be used to search for comparable text in a huge dataset efficiently.
- Recommender systems: FAISS can be used to efficiently search for comparable things in a large dataset for a user, as well as to speed up the prediction process for collaborative filtering and content-based recommendation.
- Clustering: FAISS can be used to efficiently group related objects in high-dimensional data sets.Image retrieval: FAISS can be used to efficiently search for similar images in a large dataset.
Mathematical foundations of FAISS
FAISS is built on the concept of indexing, which is a method of preprocessing a dataset to make it more searchable. By grouping comparable components together, indexing reduces the number of elements that must be compared throughout the search. Product quantization (PQ) and inverted file indexing structures are the two main types of indexing structures employed in FAISS (IVF).
Product Quantization (PQ) is a technique for reducing the number of discrete values in high-dimensional vectors. The vectors are subdivided, and each subvector is quantized independently. This yields a vector representation that is small enough to be utilised for similarity search.
The Inverted File (IVF) method generates an inverted index of the dataset. The inverted index is a data structure that allows you to quickly discover objects in a dataset that match a specified query. The inverted index is constructed by dividing the dataset into a number of small clusters (known as inverted lists) and identifying each element with the cluster to which it belongs.
Implementation with Python
FAISS can be implemented in Python by installing and importing the library using pip. Here’s an example of how to use FAISS to find the nearest neighbour:
import faiss
import numpy as np# Generate a dataset of 1000 points in 100 dimensions
X = np.random.rand(1000, 100).astype('float32')
# Create an index for the dataset
index = faiss.IndexFlatL2(100)
# Add the dataset to the index
index.add(X)
# Perform a nearest neighbor search for a query vector
query = np.random.rand(1, 100).astype('float32')
D, I = index.search(query, k=10)
# Print the distances and indices of the nearest neighbors
print(D)
print(I)
In this example, we first establish a dataset of 1000 points in 100 dimensions and then use the faiss.IndexFlatL2 class to create an index. The dataset is then added to the index and the index.search() method is used to execute a nearest neighbour search for a query vector. The function returns the nearest neighbours’ distances and indices.
Another example is the approximate nearest neighbour search:
import faiss
import numpy as np# Generate a dataset of 1000 points in 100 dimensions
X = np.random.rand(1000, 100).astype('float32')
# Create an index for the dataset
nlist = 100
quantizer = faiss.IndexFlatL2(100) # this remains the same
index = faiss.IndexIVFPQ(quantizer, X.shape[1], list, 8, 8)
# Train the index
index.train(X)
# Add the dataset to the index
index.add(X)
# Perform an approximate nearest neighbor search for a query vector
query = np.random.rand(1, 100).astype('float32')
D, I = index.search(query, k=10)
# Print the distances and indices of the nearest neighbors
print(D)
print(I)
In this example, we use the IVFPQ indexing structure, which supports approximate nearest neighbours search.
Benefits of FAISS
FAISS has various advantages, including:
- Efficient similarity search: FAISS provides efficient methods for similarity search and grouping, which can handle large-scale, high-dimensional data.
- Approximate nearest neighbour search: FAISS offers an approximate closest neighbour search that delivers approximate nearest neighbours with a quality guarantee.
- GPU support: FAISS includes GPU support, which enables for further search acceleration and can greatly increase search performance on large-scale datasets.
- Scalability: FAISS is designed to be extremely scalable and capable of handling large-scale datasets including billions of components.
- Flexibility: FAISS provides a number of indexing structures, including as LSH, IVF, and PQ, that can be utilised to speed up searches and handle various types of data and use cases.
Conclusion
FAISS is a sophisticated package for high-dimensional data similarity search and clustering. It includes numerous indexing structures as well as GPU support, which can be utilised to speed up searches and manage huge datasets. It has a wide range of applications, including image retrieval, audio recognition, natural language processing, and recommender systems. It is simple to build in Python, making it an excellent tool for machine learning practitioners. It’s important to note that FAISS is regularly updated, and new indexing structures and features are added, so keep a watch on the updates.