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Understanding Faiss (Facebook AI Similarity Search)

Apr 10, 202415 min read

Faiss (Facebook AI similarity search) is an open-source library for efficient similarity search of unstructured data and clustering of dense vectors.

By Chris Churilo

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Latest Update: October 21, 2024

Understanding Faiss (Facebook AI Similarity Search)

Faiss (Facebook AI similarity search) is an open-source library for efficient similarity search of unstructured data and clustering of vectors.

Picture the ability to swiftly and accurately find visually similar images or semantically similar text within a massive dataset of images or documents. This capability is a reality with Facebook AI Similarity Search (Faiss), a vector similarity search library developed by Facebook AI Research. Faiss outperforms traditional query search engines optimized for hash-based searches and exact keyword matching, offering lightning speed and remarkable accuracy in similarity searches.

Simply put, Facebook AI similarity search, or Faiss, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data such as text, images, and videos within a massive dataset of unstructured data. This similarity search is achieved by representing the unstructured data in the form of numerical representations, known as vector embeddings. The closer these vector embeddings are to each other in the high-dimensional space, the more similar and relevant the data is.

Thanks to its vector similarity search capability, Faiss is very useful for many applications and use cases, such as recommendation systems, chatbots, natural language processing (NLP), video deduplication systems, and retrieval augmented generation (RAG).

This post will discuss the Facebook AI similarity search (Faiss), its capability, practical use cases, limitations, and how to use it effectively for your projects. We will also briefly introduce the differences between the Faiss vector search library and many other purpose-built vector databases like Milvus and Zilliz Cloud (the fully managed Milvus).

Understanding Faiss for Efficient Similarity Search

Illustration of a powerful library for vector similarity search | Source: Pixabay

Fig: Illustration of a powerful library for vector similarity search | Source: Pixabay

Faiss is a vector search libary used in information retrieval for semantically similar items within massive amounts of unstructured data using efficient indexing structures and different vector distance measures. It functions by leveraging a Faiss index type that stores vectors and provides a way to search them with similarity metrics like Euclidean distance (L2), dot product vector comparison, and Faiss cosine similarity. You can search exactly or adjust the search parameters (time, quality, memory) to fit your specific needs.

Facebook AI Research team created Faiss in 2015 to improve Facebook AI similarity search and introduce better core techniques. It employs a lossy compression method for high dimensional vectors, which allows for precise distance computations and reconstructions, even with compressed data. Faiss also provides state-of-the-art GPU implementation for speeds up to 5 to 10 times faster than CPU implementations.

Note: Hervé Jégou, Matthijs Douze, Jeff Johnson, Lucas Hosseini, Chengqi Deng, and Alexandr Guzhva are the main creators of Faiss. Alexandr also works on improving Milvus' performance at Zilliz, a vector database provider.

Supported Vector Distance Metrics of Faiss

Faiss extends support to a range of distance metrics, and the more popular ones include:

METRIC_L2: calculated by returning the squared distances between vectors
METRIC_INNER_PRODUCT: used to gauge the similarity between vectors and compute the inner product of two vectors
Cosine similarity: measures the cosine of the angle between two vectors. Cosine similarity is implemented as a distance metric through the computation of the inner product between vectors.

Other metrics Faiss also supports are METRIC_L1, METRIC_Linf, METRIC_Lp, METRIC_Canberra, METRIC_BrayCurtis,METRIC_JensenShannon, and Mahalanobis distance.

How to Install Faiss for Your Project

Illustration of setting up Faiss for a project | Source: Pixabay

Installing Faiss is a direct and simple process, with Conda standing as the recommended package installation method. For enhanced performance, Faiss also provides optional GPU support through CUDA, with its supporting code.

Install Faiss with Conda

Faiss is available through the `conda-forge` channel, which is a community-maintained repository of Conda packages. Faiss installation via Conda requires Anaconda (or possibly Miniconda) on the system, configuration of a virtual environment (optional but recommended), and access to the Conda prompt or terminal. You can activate GPU support during the installation of Faiss with Conda as well.

Prerequisites

Anaconda or Miniconda: Ensure you have Anaconda or Miniconda installed on your system. You can check if Conda is installed by running:

conda --version

Create a New Conda Environment (Optional)

It’s a good practice to create a new Conda environment for your project. This keeps your dependencies isolated:

conda create -n faiss_env python=3.8
conda activate faiss_env

Install from Conda-Forge

To install Faiss, you’ll need to specify the `conda-forge` channel. Depending on whether you need the CPU or GPU version, use the following commands:

CPU Version:

conda install -c conda-forge faiss-cpu

This version is suitable for development and smaller datasets.

GPU Version:

conda install -c conda-forge faiss-gpu

The GPU version leverages CUDA for faster processing and is recommended for large datasets and production environments.

Verify the Installation

After installation, you can verify that Faiss is installed correctly by running:

import faiss
print(faiss.__version__)

If Faiss is installed correctly, this script will print the installed version of Faiss.

Once Faiss is installed, you can start using it in your Conda environment as needed.

GPU Implementation

The GPU version speeds up vector similarity search using a modern GPU implementation for indexing methods. This helps with fast, exact, and approximate nearest neighbor search, k-means, and small k-selection algorithms.

Compared to the CPU implementation on a single GPU, GPU in Faiss is typically 5-10 times quicker.

For a detailed walkthrough of the installation setup with a sample code, read our blog: Setting Up With Facebook AI Similarity Search (FAISS).

Creating and Managing Faiss Indexes

Illustration of creating and managing indexes in Faiss | Source: Pixabay

For efficient similarity search and clustering, Faiss offers a variety of index types, including IndexFlatL2, partitioning, quantization methods, and many other CPU and GPU indices.

IndexFlatL2, a type of index, employs a brute-force search algorithm and computes vector distances using similarity metrics like Euclidean distances (L2) or dot product.

IndexFlatL2 and Other FAISS Indexes

IndexFlatL2 and IndexFlatIP are the basic index types in Faiss that compute the L2 distance similarity metric between the query vectors and indexed vectors. In addition to IndexFlatL2, it also provides:

Brute force search without an index on CPU or GPU
Inverted File (IVF) index (IndexIVFFlat)
HNSW (IndexHNDSWFlat)
Product Quantization (PQ) index
Locality Sensitive Hashing (IndexLSH) index
Scalar quantizer (SQ) in flat mode (IndexScalarQuantizer)
Product quantizer (PQ) in flat mode (IndexPQ)
Composite indexes (combination of different index structures)
IVF and scalar quantizer (IndexIVFScalarQuantizer)
IVFADC (coarse quantizer+PQ on residuals) (IndexIVFPQ)
IVFADC+R (same as IVFADC with re-ranking based on codes) (IndexIVFPQR)

These index types are designed to facilitate efficient similarity searches and clustering of dense vectors.

Since so many vector index types are available, how can you choose the right one for your project? Here are our guidelines for choosing the right vector indexes.

Partitioning and Quantization

Partitioning and quantization techniques in Faiss aid in optimizing search efficiency by narrowing the search scope and compressing vectors. Partitioning involves dividing the feature space into smaller subsets or cells, while quantization involves encoding the vectors in a compressed form.

Faiss employs Product Quantization, which facilitates the indexing of high dimensional vectors for similarity search.

Creating Vector Embeddings for Text Data

Semantic searching of text is the most popular use cases for Faiss. So it is helpful to understand how to create vector embeddings for text data.

Generating text embeddings includes data preparation and vector generation using deep learning models such as Sentence Transformers and BERT. A vector embedding or a numerical vector representation of text data, provides a language interpretable by the machine learning model. The types of vector embeddings can vary based on the model and machine learning techniques you use.

Preparing the Data

For data preparation in Faiss, its documentation recommends the L2 normalization technique. This involves applying the L2 norm to the vectors representing the text data, which can be done using the normalize_L2 function in Faiss.

Generating Vectors with Sentence Transformers

Sentence Transformers, a Python framework, empowers the generation of advanced sentence, text, and image embeddings. As described above, you can use these embeddings for vector similarity search cases.

For more details on how sentence transforms create vector embeddings, see our blog: Sentence Transformers for Long-Form Text

How to Use Faiss and Implement Similarity Search

Illustration of implementing a vector similarity search with Faiss | Source Pixababy

To use Faiss for vector similarity search, you need to create an index, perform the similarity search, and analyze and sort the results. The search is done using Index instances that create a structure for adding and finding similar vectors.

Creating a vector embedding

A vector embedding is created by normalizing the input text and converting it into a numerical representation. Techniques such as word embeddings or vectorization methods like TF-IDF or BERT can convert input text into a vector embedding (a numerical representation) that can be searched using Faiss.

Executing the Vector Search

The search, executed through Faiss, returns the nearest neighbors and their distances from the search vector. The k-parameter in the search function of this library indicates the number of most similar vectors that will be returned for each query vector.

During a Faiss search, the library computes the distance between vectors using the squared Euclidean distance (L2) metric.

Analyzing and Sorting Results

The results can be sorted and analyzed to quickly search and retrieve pertinent information. Faiss provides several nearest neighbor search implementations, including similarity search methods and approximate search, that offer a trade-off between speed and accuracy, making it an ideal choice for large-scale image similarity search tasks.

It is possible to associate each vector with a label or category during indexing in Faiss, which allows retrieving the corresponding labels or categories for nearest neighbors when using it, in other words, nearest neighbors search.

Here's your Faiss tutorial that helps you set up FAISS, get it up and running, and demonstrate its power through a sample search program. You can also explore Faiss Documentation for more information.

Balancing Speed and Accuracy in Faiss

In Faiss, a balance between speed and accuracy can be attained through parameter tuning and selection between GPU and CPU implementations. This capability in Faiss can have a significant impact on both the speed and accuracy of similarity search.

Parameter Tuning

Faiss permits users to fine-tune search-time parameters to optimize the balance between accuracy and search time, offering an automatic tuning mechanism for some of Faiss index types. By adjusting the parameters, you can find a balance between speed and accuracy that is suitable for your use case.

GPU vs. CPU Performance

The GPU implementation in Faiss offers significant speed enhancements compared to CPU implementations, enabling quicker similarity searches in large datasets. Compared to the CPU implementation on a single GPU, GPU in Faiss is typically 5-10 times quicker.

Real-World Applications and Use Cases of Faiss

Faiss finds use in various real-world applications, embracing large-scale image similarity search, and text classification and clustering. It has been employed for large-scale image retrieval to effectively search for analogous photos in a vast dataset.

Large-Scale Image Similarity Search

Successfully used for billion-scale vector similarity search tasks, Faiss leverages its similarity search capabilities and GPU memory implementation.

Examples of projects that have taken advantage of GPU include creating vector-based search engines and expediting vector search using IVF methods.

Text Classification and Clustering

Text classification and clustering are achievable using Faiss by constructing vectors from text data and executing similarity searches to identify related documents or categories.

It can be used for semantic similarity search in natural language processing (NLP). It is also used for similarity search in text at Loopio. Additionally, Faiss can be used to create a vector-based search engine with Sentence Transformers.

Learn the top use case for vector search in 2024.

Limitations of Faiss Similarity Search Library

While Faiss (Facebook AI Similarity Search) is a powerful tool for similarity search and clustering of dense vectors, it does have some limitations that users should be aware of:

Memory Usage

High Memory Consumption: Faiss indexes, especially flat indexes, can consume significant memory when working with large datasets. This is because all vectors are stored in memory, which can be a limitation for very large datasets.
Limited Scalability: For extremely large datasets (billions of vectors), Faiss may require substantial memory resources, limiting its scalability on standard hardware. A purpose-built vector database like Milvus and Zilliz Cloud can be a good option to handle billion-scale vectors.

GPU Dependency

CUDA Dependency for GPU Acceleration: Faiss GPU functionalities require NVIDIA GPUs and CUDA. This dependency can be a limitation for users with non-NVIDIA hardware or those working in environments where CUDA is not supported.
Complex Setup: Setting up Faiss with GPU support can be complex, particularly if CUDA or the necessary drivers are not correctly installed or configured.

Index Type Trade-offs

Accuracy vs. Speed: Faiss offers various index types, each with trade-offs between accuracy and speed. For example, approximate nearest neighbor (ANN) searches are faster but less accurate than exact searches. Choosing the right index requires balancing these trade-offs based on the specific use case.
Indexing Overhead: Some advanced indexing methods, like IVF (Inverted File Index) or HNSW (Hierarchical Navigable Small World), require additional time and computational resources for training and building the index, especially for large datasets.

Lack of Support for Sparse and Multimodal Vectors

Faiss is optimized for dense vectors and single-modality data (e.g., text, image embeddings). It does not natively support sparse vectors and multimodal search (e.g., combining text and image vectors), which can be a limitation for some applications that require these capabilities.
- Milvus (open-source) and Zilliz Cloud (fully managed Milvus) are two best Faiss alternatives with a powerful hybrid search capability. This hybrid search allows for combining multimodal search, hybrid sparse and dense search, and hybrid dense and full-text search, offering versatile and flexible search functionality.

Lack of Built-in Distributed Support

Faiss does not natively support distributed or multi-node deployments out of the box. This can be a limitation when working with extremely large datasets that require distribution across multiple machines.
- Milvus is an open-source, distributed, and cloud native vector database with a computing and storage seperation architecture and can store, index, and retrieve billion-scale vector embeddings.
Users need to implement their own sharding strategies or use external tools to distribute Faiss across multiple nodes, which can increase complexity.

Understanding these limitations can help you determine whether Faiss is the right tool for your specific use case and guide you in how to effectively utilize it while being aware of its constraints.

Comparing Faiss with Vector Databases or Vector Search Services

While Faiss is a powerful tool for efficient vector similarity searches, it's not the only option available. There are also more scalable, purpose-built vector databases like Milvus, as well as vector search plugins for traditional relational databases such as Elasticsearch. What sets these solutions apart? Explore the key differences on the comparison pages below for more insights.

Compare Vector Databases, Vector Search Libraries and Plugins

Summary

In conclusion, Faiss is a powerful library for efficient similarity search and clustering of vector embeddings, with various real-world applications such as large-scale image retrieval and text classification and clustering. With its ease of installation, GPU implementation, and parameter tuning capabilities, Faiss can help you optimize the speed and accuracy of your projects. Don’t hesitate to explore the potential of the Faiss library and harness its power for your next similarity search endeavor.

For more information, see Faiss Documentation.

Frequently Asked Questions

What is Faiss?

Faiss (Facebook AI Similarity Search) is a Facebook AI-developed library for efficient similarity search and clustering of dense vectors. It provides efficient solutions for similarity search and clustering in high-dimensional spaces, allowing developers to quickly search for embeddings of multimedia documents that are similar to each other. Popular use cases include RAG, recommendation systems, semantic search, natural language processing, and chatbots.

Is Faiss a vector database?

No, Faiss is not a vector database but an open-source approximate nearest neighbor search (ANN) library, which is used instead of a fully managed solution with limited functionality. If your dataset is small and limited, these FAISS can be sufficient for unstructured data processing, even for systems running in production.

Read our comparison blog to learn more about the differences between purpose-built vector databases, vector search plugins within conventional databases and vector search libraries like Faiss.

What is the difference between Faiss and Milvus?

FAISS is an underlying library, while Milvus is a vector database. By contrast, the Milvus vector database is an optimal solution for unstructured data storage and retrieval. It can store and search billion-scale vectors, giving millisecond-level responses. It also provides various advanced and enterprise-grade functionalities like hybrid search, which enables hybrid sparse and dense vector search, multimodal vector search, and hybrid dense and full-text searches.

In addition, Milvus has user-friendly features for structured/semi-structured data, such as cloud nativity, multi-tenancy, and scalability.

Wondering how to migrate your data from Faiss to Milvus? Read our blog about Milvus migration techniques and tools.

Which is better, Chroma or Faiss?

Chroma makes local development easy, while FAISS offers efficient search and clustering if you want to add more features. Depending on the use case, both are great options for small datasets. Here is a comparison of Chroma vs Faiss.

What is the primary purpose of Faiss?

A library developed primarily by Facebook AI Research that enables similarity search and clustering of dense vectors. It is a popular approach for many developers.

Is Faiss cosine similarity supported?

Yes, you can do FAISS cosine similarity for vector search operations. Here's a brief overview:

FAISS provides efficient similarity search and clustering of dense vectors. It includes multiple index types and search algorithms, including those that use cosine similarity as their distance metric.

To use cosine similarity in FAISS:

Normalize your vectors: Cosine similarity is equivalent to L2 distance on normalized vectors. FAISS typically works with L2 distance, so you'll need to normalize your vectors to unit length before indexing.
Choose an appropriate index: Most FAISS indexes support L2 distance, which translates to cosine similarity on normalized vectors. Common choices include IndexFlatL2 for exact search or IndexIVFFlat for approximate search.
Perform searches: After indexing your normalized vectors, search queries will return results based on cosine similarity.

What is the future of vector databases?

Traditionally, vector databases supported similarity-based search. Now, they are extending their capabilities to include exact search or matching. This versatility allows you to analyze your data through two lenses: a similarity search for a broader understanding and an exact search for nuances. By combining these two approaches, users can fine-tune the balance between obtaining a high-level overview and delving into specific details. For more information, read Charles Xie's insights about the evolution of vector databases.

Updated on Oct 21, 2024

Chris Churilo
Chris Churilo is the VP of Marketing & Community at Zilliz where she leads all community, developer relations, and marketing efforts. Prior to Zilliz, Chris was a founding member of the InfluxData’s go to market efforts and helped propel the time series database platform to dominance in the market. In earlier roles she defined and designed a SaaS monitoring solution at Centroid, and prior to that she was the VP of product management at iPass and was the LOB for several cloud services that required her to track the business and operational metrics and analytics to help identify and resolve issues.

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