A vector database is a type of database that stores data as high-dimensional vectors. Vectors are mathematical representations of objects or entities, and they can be used to represent a wide variety of data, such as images, text, and audio. Vector databases are designed to efficiently store and retrieve data that is similar to each other.
Illustration of a digital storage system labeled 'Vector Databases'
Uses of vector databases:
Semantic search:Vector databases can be used to power semantic search engines, which are able to understand the meaning of queries and return results that are relevant to the user's intent.
Recommendation systems:Vector databases can be used to power recommendation systems, which are able to recommend items to users based on their past behavior or preferences.
Fraud detection:Vector databases can be used to detect fraud by identifying patterns in data that are indicative of fraudulent activity.
Anomaly detection:Vector databases can be used to detect anomalies in data by identifying data points that are significantly different from the rest of the data.
Image and audio similarity search:Vector databases can be used to find images or audio recordings that are similar to a given query image or audio recording.
How Vector Databases Work
Vector databases store data as vectors, which are mathematical objects that can be represented as arrays of numbers. Each number in a vector represents a feature of the entity or concept being represented. For example, a vector representing an image might contain features such as the average pixel intensity, the presence of certain colors, or the distribution of edges.
To find similar vectors, vector databases use a variety of techniques, such as cosine similarity and Euclidean distance. Cosine similarity measures the angle between two vectors, while Euclidean distance measures the straight-line distance between two vectors. Vectors that are more similar will have a smaller angle or a shorter distance between them.
Top vector databases to learn as a developer:
Milvus:Milvus is an open-source vector database that is designed for scalability and performance.
Pinecone:Pinecone is a cloud-based vector database that is designed for ease of use.
Faiss:Faiss is a library for efficient similarity search on CPU and GPUs.
Annoy:Annoy is a library for approximate nearest neighbor search.
NMSLIB:NMSLIB is a library for similarity search on large datasets.
Learning about vector databases can be a valuable skill for developers who are working on projects that involve semantic search, recommendation systems, fraud detection, anomaly detection, or image and audio similarity search.
Additional resources for learning about vector databases:
Vector Databases: The Definitive Guideby Yannis Katsis and Aristidis Protopapadakis
Vector Search: The Secret Sauce of Modern Applicationsby David Arthur and Andrey Maletz
Vector Databases for Practitionersby Alexey Boyarsky, Dmitry Konovalov, Ilya Ovodov, and Vladimir Yavorskiy
The Growing Importance of Vector Databases for AI Engineers
Vector databases are a valuable skill for AI engineers to learn. This is because vector databases are well-suited for storing and retrieving high-dimensional data, which is a common type of data in AI applications. For example, vector databases can be used to store image data, audio data, and natural language processing (NLP) data.
In addition, vector databases are often used to power similarity search applications. Similarity search is a type of query that finds data points that are similar to a given query point. This type of query is common in AI applications such as image retrieval, recommender systems, and fraud detection.
By learning about vector databases, AI engineers can gain the skills they need to build and maintain AI applications that involve high-dimensional data and similarity search. In addition, learning about vector databases can help AI engineers to better understand the underlying data structures and algorithms that are used in AI applications.
Here are some specific examples of how AI engineers can use vector databases:
Image retrieval:Vector databases can be used to store and retrieve images based on their visual similarity. This can be used to build applications such as image search engines and image recommendation systems.
Natural language processing (NLP):Vector databases can be used to store and retrieve word embeddings, which are numerical representations of words. This can be used to build applications such as machine translation, text summarization, and question answering.
Recommender systems:Vector databases can be used to store and retrieve user profiles and product information. This can be used to build recommender systems that suggest products to users based on their past behavior or preferences.
Fraud detection:Vector databases can be used to store and retrieve transaction data. This can be used to build fraud detection systems that identify patterns in data that are indicative of fraudulent activity.
Overall, vector databases are a valuable skill for AI engineers to learn. By learning about vector databases, AI engineers can gain the skills they need to build and maintain AI applications that involve high-dimensional data and similarity search.