Vector Databases

TLDR: Vector databases are designed to contain embeddings and feature vector information. They are commonly used in applications that require similarity search, recommendation systems, natural language processing, and machine learning tasks. They are optimized to handle similarity searches operations

What are Vector Databases

Vector databases are databases specifically designed to handle vector-based data. Each entry within the database represents a vector or an embedding.

Vector databases are optimized for efficient similarity search and retrieval operations. They provide specialized indexing structures and algorithms to organize and search through vector data effectively.

When to use them

Vector databases are particularly useful when you need to perform similarity searches or find nearest neighbors in a high-dimensional vector space. They excel when you want to compare vectors based on their distance or similarity.

Potential use cases are: finding similar images, documents, or products. Vector databases are also beneficial when dealing with large-scale datasets that require fast and efficient retrieval of similar items.

How are they different from relational databases?

Vector databases differ from traditional relational databases in several ways. Unlike relational databases that primarily focus on structured data and use SQL for querying, vector databases focus on handling unstructured or semi-structured vector data.

Vector databases provide specialized indexing techniques, such as inverted files, random projection trees, or product quantization, which enable efficient similarity search operations. Vector databases also offer APIs and query languages specifically designed for vector-based operations, allowing for fast and accurate similarity search and retrieval.

Examples

Faiss

Website: https://faiss.ai/

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

Developed by Facebook AI Research, Faiss is a widely used vector database library that provides efficient similarity search and clustering algorithms. It offers various indexing methods, including inverted files and product quantization, to accelerate search operations on large-scale vector datasets.

Annoy

Github: https://github.com/spotify/annoy

Annoy is a lightweight, open-source library for approximate nearest neighbor search. It is designed to be fast and memory-efficient and supports both in-memory and disk-based storage options. Annoy is commonly used in recommendation systems and content-based filtering applications.

Milvus

Website: https://milvus.io/

Github: https://github.com/milvus-io/milvus

Milvus is an open-source vector database that supports efficient storage and search of large-scale vector data. It offers flexible indexing options, including IVF (inverted file), HNSW (hierarchical navigable small world), and PQ (product quantization). Milvus is designed to handle real-time applications that require fast vector similarity search.