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A fun and absurd introduction to Vector Databases by Alexander Chatzizacharias
Explore the world of Vector Databases with Alexander Chatzizacharias, discover how to efficiently search and index high-dimensional vectors, and learn about open-source and closed-source solutions like Elastic and VV8.
- Vector databases are a type of database that allows for fast and efficient semantic search of large amounts of data.
- They are typically used to represent high-dimensional vectors and can be used for tasks such as image and text recognition.
- Vector databases can be used to store and index high-dimensional vectors, and they can be used to search for similar objects based on their vector representation.
- The author of this talk, Alexander Chatzizacharias, has used vector databases in his own projects, including a Shazam-like application.
- There are various algorithms that can be used to index and retrieve vectors, including nearest neighbor search algorithms.
- The talk also mentions the use of managed services, such as Docker, to run vector databases.
- The author also mentions the use of Kotlin, a programming language, to create applications on top of vector databases.
- The talk also touches on the use of AI and machine learning in vector databases, such as using transformer models.
- It also mentions that some vector databases are closed-source, such as Elastic, and others are open-source, such as VV8.