5 Vector Similarity Search Algorithms for LLMs

5 Vector Similarity Search Algorithms for LLMs

In this blog, we will review five popular similarity search algorithms that are widely used in AI applications for retrieving similar data from vector databases. But before we dive into the list, let’s first understand what similarity search algorithms are and how they are used in Retrieval-Augmented Generation applications.

What is a Similarity Search Algorithm?

Similarity Search Algorithms are essential for large language models (LLMs) and modern AI. They are used for building RAG (Retrieval-Augmented Generation) applications, which combine the capabilities of a pre-trained LLM with an external data source. In short, LLMs become context-aware and accurate in responding to users’ queries. 

How Similarity Search Algorithms Work

The Similarity Search Algorithms helps find similar data from Vector Database that are similar to the user query. This process involves comparing the semantic features of texts stored as embedding vectors in the database. Embedding vectors are numerical representations of words or phrases in a high-dimensional space, capturing their meaning and context. This allows the algorithms to identify semantically similar data points even if the exact keywords do not match.

The algorithm measures the proximity of these vectors using similarity metrics like cosine similarity. This capability is particularly useful for tasks such as finding related articles, generating recommendations, or clustering similar documents.

Application in RAG Applications

In the RAG application, the retrieved similar texts are combined with the user’s query to provide additional context. This augmented input is then fed into the LLM, enabling it to generate highly accurate and relevant responses tailored to specific use cases. This approach addresses key limitations of LLMs, such as their tendency to provide generic answers or generate false responses (hallucinations).

1. Hierarchical Navigable Small World (HNSW)

HNSW is a graph-based approximate nearest neighbor search method that uses proximity graph indexing and retrieval to handle high-dimensional spaces efficiently. 

HNSW works by constructing a multi-layered graph where each layer captures different levels of proximity, with the top layers containing long-distance links for quick navigation and the bottom layers focusing on short-distance links for precise local searches.

HNSW is particularly effective in scenarios where high-dimensional data needs to be searched quickly and accurately. However, it typically has a larger memory footprint and slower index build times compared to ScaNN.

2. K-D Tree (K-Dimensional Tree)

K-D Tree is a data structure for organizing points in a k-dimensional space. During the construction of the K-D tree, the algorithm selects a dimension and a splitting value to partition the data into two subsets. The process is recursively applied to each subset until the tree is fully constructed. During the query phase, the algorithm traverses the tree to find the nearest neighbors. K-D Trees are effective for exact nearest neighbor searches but can become inefficient as the number of dimensions increases.

3. ScaNN (Scalable Nearest Neighbors)

ScaNN, developed by Google, is designed to handle large-scale nearest neighbor search efficiently. It leverages search space pruning and quantization to optimize Maximum Inner Product Search (MIPS) and supports other distance functions like Euclidean distance. ScaNN is known for its high performance, achieving up to 4x faster vector queries and 8x faster index build times compared to the HNSW index, while maintaining a smaller memory footprint.

4. Faiss IVF (Inverted File)

Faiss (Facebook AI Similarity Search), an open-source library by Facebook AI Research, is designed for efficient similarity search and clustering of dense vectors. It uses quantization and binary indexes to reduce search latency, making it ideal for large-scale AI and machine learning tasks.

One of the key indexing methods provided by Faiss is the Inverted File (IVF) index, which significantly enhances the efficiency of similarity searches in large datasets. It is designed to improve the efficiency of nearest-neighbor searches by reducing the search space.

5. Annoy (Approximate Nearest Neighbors Oh Yeah!)

Annoy, developed by Spotify, is an open-source library that constructs a forest of binary search trees for approximate nearest neighbor search. It uses random projections and hyperplanes to partition the space, making it faster than traditional K-D Trees. Annoy is particularly effective for finding k-nearest neighbors quickly and is known for its speed and performance.

Conclusion

Similarity search algorithms that are used in vector databases for retrieval processing. Each algorithm has its strengths and is suited for different use cases, from handling high-dimensional data to providing efficient nearest-neighbor searches. If you want to learn more about each algorithm’s performance and discover new and better-performing algorithms, then check out ANN-Benchmarks.

In this blog, we have learned about how similarity search algorithms work and their applications. We have also reviewed the most popular algorithms used in recommendation engines, retrieval systems, clustering, and image search. If you want to learn more about LLMs and AI, please let me know in the comments or reach out to me on LinkedIn.

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