Mastering Efficient Similarity Search with Faiss Library
An ultimate guide to understanding the FAISS library, including how to install and use FAISS, and much more.
Read the entire series
- Raft or not? The Best Solution to Data Consistency in Cloud-native Databases
- Mastering Efficient Similarity Search with Faiss Library
- Information Retrieval Metrics
- Advanced Querying Techniques in Vector Databases
- Popular Machine-learning Algorithms Behind Vector Searches
- Hybrid Search: Combining Text and Image for Enhanced Search Capabilities
- Ensuring High Availability of Vector Databases
- Ranking Models: What Are They and When to Use Them?
- Navigating the Nuances of Lexical and Semantic Search with Zilliz
- Enhancing Efficiency in Vector Searches with Binary Quantization and Milvus
- Model Providers: Open Source vs. Closed-Source
- Embedding and Querying Multilingual Languages with Milvus
- An Ultimate Guide to Vectorizing and Querying Structured Data
Latest Update: July 21, 2024
Imagine the power to find visually similar images or semantically similar text through a massive dataset of images or text with lightning speed and remarkable accuracy. With the Faiss (Facebook AI similarity search) library, developed by Facebook AI Research, this capability is now at your fingertips. In simple terms, Faiss is a tool that allows developers to quickly search for semantically similar multimedia documents in the form of vector embeddings that are closer to each other in a high-dimensional vector space. It solves the limitations of traditional query search engines that are optimized for hash-based searches and provides more scalable similarity search functions.
Imagine being able to search a large collection of unorganized data, including images, videos, audio files, and text. The Faiss library allows you to search this data quickly and accurately.
It finds similar things and groups dense vectors. It is useful for things like RAG, recommendation systems, chatbots, and natural language processing (NLP). This article will discuss this library, its practical uses, and how to use it effectively for your projects.
Key Takeaways of Faiss Similarity Search (Facebook vector database)
- Faiss is a strong tool used in information retrievaland data analysis for finding similar items and grouping vectors using efficient indexing structures and different vector distance measures.
- Faiss provides state-of-the-art GPU implementation for speeds up to 5 to 10 times faster than CPU implementations.
- Faiss is used in real life for things like Retrieval Augmented Generation (RAG), recommendation systems, and finding similar meanings in unstructured data. You can apply this to various forms of data, such as text, audio, and video.
Understanding Faiss: A Powerful Similarity Search Library
Illustration of a powerful library for vector similarity search | Source: Pixabay
The Faiss library is a high-tech information retrieval tool that improves the speed and accuracy of AI searching and grouping of vectors, making it very valuable. 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 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. Hervé Jégou, Matthijs Douze, Jeff Johnson, Lucas Hosseini, Chengqi Deng, and Alexandr Guzhva are the main creators. Alexandr also works on improving Milvus' performance.
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 also supported 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 installation method.
For enhanced performance, Faiss also provides optional GPU support through CUDA, with its supporting code.
Install Faiss with Conda
Faiss installation via Conda requires Anaconda (or 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.
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 Index Types
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 search 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.
Building Vectors for Text Data
Generating text embeddings includes data preparation and vector generation using frameworks such as Sentence Transformers. A vector or embedding, a numerical vector representation of text data, provides a language interpretable by the machine learning model. The vector embedding type can vary based on the model and machine learning techniques you use.
Preparing the Data
For data preparation in Faiss, the 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.
Learn the top use case for vector search in 2024.
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 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 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 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.
Comparing Faiss with Other Vector Datases or Vector Search Services
- Faiss vs. Milvus: https://zilliz.com/comparison/milvus-vs-faiss
- Faiss vs. Redis: https://zilliz.com/comparison/faiss-vs-redis
- Faiss vs Elastic: https://zilliz.com/comparison/elastic-vs-faiss
- Faiss vs. Weaviate: https://zilliz.com/comparison/weaviate-vs-faiss
- Faiss vs. Pinecone: https://zilliz.com/comparison/faiss-vs-pinecone
- Faiss vs. Qdrant: https://zilliz.com/comparison/qdrant-vs-faiss
- Faiss vs. Pgvector: https://zilliz.com/comparison/faiss-vs-pgvector
- Faiss vs. MongoDB: https://zilliz.com/comparison/milvus-vs-mongodb%20atlas
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 Faiss 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. An open-source approximate nearest neighbor search (ANN) library 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're willing to add more features. Depending on the use case, both are great options for small datasets.
What is the primary purpose of Faiss?
A library developed by Facebook AI Research that enables similarity search and clustering of dense vectors. It is a popular approach for many developers.
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.
- Key Takeaways of Faiss Similarity Search (Facebook vector database)
- Understanding Faiss: A Powerful Similarity Search Library
- How to Install Faiss for Your Project
- Creating and Managing Faiss Indexes
- Building Vectors for Text Data
- How to Use Faiss and Implement Similarity Search
- Balancing Speed and Accuracy in Faiss
- Real-World Applications of Faiss
- Comparing Faiss with Other Vector Datases or Vector Search Services
- Summary
- Frequently Asked Questions