Faiss

Faiss

https://github.com/facebookresearch/faiss

https://faiss.ai/index.html

https://arxiv.org/pdf/2401.08281

 

Faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed primarily at Meta's Fundamental AI Research group.

 

Introduction

Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity, since this is a dot product on normalized vectors.

Some of the methods, like those based on binary vectors and compact quantization codes, solely use a compressed representation of the vectors and do not require to keep the original vectors. This generally comes at the cost of a less precise search but these methods can scale to billions of vectors in main memory on a single server. Other methods, like HNSW and NSG add an indexing structure on top of the raw vectors to make searching more efficient.

The GPU implementation can accept input from either CPU or GPU memory. On a server with GPUs, the GPU indexes can be used a drop-in replacement for the CPU indexes (e.g., replace IndexFlatL2 with GpuIndexFlatL2) and copies to/from GPU memory are handled automatically. Results will be faster however if both input and output remain resident on the GPU. Both single and multi-GPU usage is supported.

 

https://github.com/facebookresearch/faiss/wiki

Faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python (versions 2 and 3). Some of the most useful algorithms are implemented on the GPU. It is developed primarily at Meta AI Research with help from external contributors.

What is similarity search?

Given a set of vectors {x1,...,xn} in dimension d, Faiss builds a data structure in RAM. After the structure is constructed, when given a new vector x in dimension d it performs efficiently the operation:

i=argmini||x−xi||

where ||.|| is the Euclidean distance (L2).

In Faiss terms, the data structure is an index, an object that has an add method to add x_i vector. Note that the dimension of x_i is assumed to be fixed.

Computing the argmin is the search operation on the index.

This is all what Faiss is about. It can also:

• return not just the nearest neighbor, but also the 2nd nearest, 3rd, ..., k-th nearest neighbor

• search several vectors at a time rather than one (batch processing). For many index types, this is faster than searching one vector after another

• trade precision for speed, ie. give an incorrect result 10% of the time with a method that's 10x faster or uses 10x less memory

• perform maximum inner product search argmaxi⟨x,xi⟩ instead of minimum Euclidean search. There is also limited support for other distances (L1, Linf, etc.).

• return all elements that are within a given radius of the query point (range search)

• store the index on disk rather than in RAM

• index binary vectors rather than floating-point vectors

• ignore a subset of index vectors according to a predicate on the vector ids.

 

What Is Faiss (Facebook AI Similarity Search)?

https://www.datacamp.com/blog/faiss-facebook-ai-similarity-search

Traditional search engines do a good job of finding exact matches, but they often fall short when it comes to identifying similar items based on content like images, videos, or text. This limitation can hinder the development of applications such as recommendation systems, image searches, and anomaly detection systems.

To overcome this, Meta AI (formerly Facebook AI) developed Facebook AI Similarity Search (Faiss).

Faiss is a library specifically designed to handle similarity searches efficiently, which it’s especially useful when dealing with large multimedia datasets.

In this article, I’ll explain what Faiss is and guide you on how to start using it for your search applications.

What Is Faiss?

Faiss, short for Facebook AI Similarity Search, is an open-source library built for similarity search and clustering of dense vectors. Faiss can be used to build an index and perform searches with remarkable speed and memory efficiency. Additionally, it enhances search performance through its GPU implementations for various indexing methods.

Core functionality

Faiss makes nearest-neighbor searches fast by indexing vectors using sophisticated algorithms like k-means clustering and product quantization. These methods help Faiss organize and retrieve vectors efficiently, ensuring similarity searches are quick and accurate. Here's a closer look at the indexing algorithms:

1. K-means clustering: This algorithm breaks the data into clusters, which helps narrow down the search space by focusing on the most relevant clusters during queries.
2. Product quantization (PQ): PQ compresses vectors into shorter codes, reducing memory usage significantly and speeding up the search without a big drop in accuracy.
3. Optimized product quantization (OPQ): An enhanced version of PQ, OPQ rotates the data to better fit the quantization grid, improving the accuracy of the compressed vectors.

Flexibility

Faiss is pretty versatile when it comes to measuring similarity between vectors, offering a variety of distance metrics to choose from. The main ones are:

• Euclidean distance: This measures the straight-line distance between two points, which is great when you care about the geometric similarity of vectors.
• Cosine similarity: This looks at the cosine of the angle between two vectors, focusing more on their orientation than their size. It's especially handy for text analysis where the direction matters more than the length.

These options allow you to pick the metric that best fits your data and application needs.

Faiss can run on both CPUs and GPUs, using modern hardware to speed up the search process. Faiss is designed for various computing platforms, from personal computers to high-performance computing clusters. It smoothly transitions between CPU and GPU indices, and its Python interface works well with C++ indices, making it easy to switch from testing to deployment. This multi-platform support ensures that Faiss can be efficiently used in various computing environments, optimizing performance and resource use.

Key Features of Faiss

Faiss is a standout tool for similarity search, packed with features designed to handle large and diverse datasets effectively. Here’s a closer look at some of the core capabilities that make it a powerful asset for data-intensive tasks.

Scalability

Faiss is designed to manage datasets from millions to billions of vectors, which is perfect for applications like large recommendation systems or massive image and video databases. It uses advanced techniques like inverted file systems and hierarchical navigable small world (HNSW) graphs to keep things efficient even with extensive datasets.

Speed

Faiss is fast due to its optimized algorithms and data structures. It uses k-means clustering, product quantization, and optimized brute-force searches to speed things up. If you’re using a GPU, Faiss can be up to 20 times faster on newer Pascal-class hardware compared to its CPU versions. This speed is crucial for real-time applications where you need quick responses.

Accuracy

Faiss gives you flexibility in accuracy, balancing speed and precision based on what you need. You can fine-tune it for highly accurate searches or go for quicker, less precise results. There are different indexing methods and parameters to choose from, and you can measure performance with metrics like 1-recall@1 and 10-intersection to see how well it’s doing compared to a brute-force approach.

Versatility

Faiss can handle different types of data by converting them into vector representations. This means you can use it for images, text, audio, and more, making it useful across various fields and industries. It supports several distance metrics, including Euclidean distance, cosine similarity, and inner-product distance, allowing you to tailor the search process to your needs. Faiss is adaptable for diverse applications like image similarity search, text document retrieval, and audio fingerprinting.

Use Cases of Faiss

Faiss is versatile and efficient, making it a great fit for a variety of applications across different industries. Let’s dive into some of the main use cases where Faiss excels.

Recommendation systems

Faiss is a game-changer for recommendation systems. It can quickly find similar items within huge datasets, like products, movies, or articles.

Imagine an e-commerce platform using Faiss to analyze user behavior and product interactions. It generates high-dimensional vectors for these interactions, and by performing nearest-neighbor searches, Faiss identifies products similar to those a user has viewed or purchased. This personalized approach boosts user engagement and satisfaction, which drives sales and customer retention.

Image and video search

Faiss also powers search engines that retrieve visually similar images or videos by indexing high-dimensional vectors from multimedia content. Think of a photo organization app using Faiss to help users find all images of a specific landmark in their photo library. By converting images into vectors and indexing these vectors with Faiss, the app can quickly perform similarity searches. This capability is just as useful for video platforms, where Faiss can index and search for similar video clips based on visual and audio features, enhancing content discovery and recommendation features.

Anomaly detection

In anomaly detection, Faiss is great at identifying outliers or anomalies in datasets by finding points that deviate significantly from their nearest neighbors.

Take fraud detection, for example: financial transactions can be converted into vectors that include attributes like transaction amount, location, and time. Faiss performs similarity searches to flag transactions that are outliers, indicating potential fraud.

In network security, Faiss can detect unusual network traffic patterns that might signify cyber-attacks.

In quality control, it can identify defective products by comparing their features to those of normal products.

Information retrieval

Faiss is also a fantastic tool for information retrieval, helping find relevant documents or passages based on semantic similarity. This is invaluable for search engines, digital libraries, or any system needing quick and accurate text retrieval.

For example, a search engine can convert documents and queries into high-dimensional vectors using techniques like word2vec or BERT embeddings. By indexing these vectors with Faiss, the search engine can quickly perform similarity searches to retrieve documents that match the user’s query in meaning rather than just keyword matching. This makes search results more relevant, enhancing user experience and efficiency in information retrieval.

Getting Started With Faiss

In this section, I’ll show you how to set up Faiss and use it alongside LangChain and OpenAI embeddings.

Installation

To install Faiss, you can use pip to get the CPU or GPU version:

# For CPU pip install faiss-cpu # For GPU pip install faiss-gpu

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Additionally, to use Faiss with LangChain and OpenAI embeddings, you’ll need to install the following packages:

pip install -U langchain-community langchain-openai tiktoken

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Basic application

Here’s a simple code example demonstrating how to index vectors and perform a nearest neighbor search using Faiss, LangChain, and OpenAI embeddings:

from langchain.document_loaders import WikipediaLoader from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import CharacterTextSplitter # Load content from Wikipedia using WikipediaLoader loader = WikipediaLoader("Machine_learning") document = loader.load() # Chunking text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) # Loading embeddings model embeddings = OpenAIEmbeddings() # Convert documents to vectors and index vectors db = FAISS.from_documents(docs, embeddings) print(db.index.ntotal) # Search query query = "What is machine learning?" docs = db.similarity_search(query) print(docs[0].page_content)

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If you want to see more examples and familiarize yourself with Faiss, check out the official documentation and these tutorials.

Conclusion

Faiss stands out as a powerful tool for efficient similarity search, offering scalability, speed, accuracy, and versatility. It can handle large datasets and perform quick, accurate similarity searches, making it invaluable for various applications like recommendation systems, anomaly detection, and information retrieval.

By using Faiss, we can create smarter and more intuitive systems that enhance user experiences and improve operational efficiency.

 

langchain integration

https://python.langchain.com/docs/integrations/vectorstores/faiss/

Faiss

Facebook AI Similarity Search (FAISS) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also includes supporting code for evaluation and parameter tuning.

See The FAISS Library paper.

You can find the FAISS documentation at this page.

This notebook shows how to use functionality related to the FAISS vector database. It will show functionality specific to this integration. After going through, it may be useful to explore relevant use-case pages to learn how to use this vectorstore as part of a larger chain.