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A fun and absurd introduction to Vector Databases by Alexander Chatzizacharias
Discover the fun and absurd world of Vector Databases, learn about vectorization, building embeddings, distance calculation, and more, with expert advice on using vector databases in search, recommendation systems, and more.
- Vectors are a mathematical concept used in databases, particularly in vector databases.
- Vectorization of objects is the process of converting an object into a vector, which can be used for search, classification, and other tasks.
- Building embeddings is a process where a database can automatically generate vectors for objects, eliminating the need for external services.
- Several vector databases are available, including Weaviate, Elasticsearch, and FAISS.
- Vector spaces are crucial in vector databases, and databases use the same vector space for both indexing and search.
- Distance calculation is a key concept in vector databases, with the most common metric being cosine distance.
- Embedding models are used to generate vectors for objects, and different libraries and frameworks offer different embedding models.
- Vector databases can be used in various applications, such as search, recommendation systems, and natural language processing.
- Some popular vectorization models include Word2Vec, BERT, and ResNet-50.
- Data preparation is necessary before using a vector database, and algorithms like hierarchical navigable small worlds can be used for this purpose.
- JDriven, a consultancy, offers expert advice on using vector databases.
- Games can be developed using vector databases, and developers can use vectors to say where objects need to be in a 2D or 3D space.