Practical AI
Vector databases (beyond the hype)
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Quick takeaways
- Vector databases are purpose-built databases that efficiently manage, store, and update vectors at scale, offering better scalability, efficiency, and access to the latest technologies and algorithms.
- Embedded databases, like LancerD and ChromaDB, provide an alternative to client-server architecture, improving data privacy and scalability, while the future of vector databases lies in combining them with graph databases for enhanced retrieval augmented generation models.
Deep dives
Vector databases: Efficiently managing and retrieving vectors at scale
Vector databases are purpose-built databases that efficiently manage, store, and update vectors at scale. These databases retrieve the most similar vectors to a given query, considering the semantics of the query. They are a natural evolution of the NoSQL class of databases and have become more accessible, allowing companies to build powerful search and information retrieval systems. With the combination of vector databases and large language models (LLMs), applications like querying data by natural language and retrieval augmented generation are becoming more achievable. The trade-off between using existing databases with vector capabilities and purpose-built vendors is an important consideration. Purpose-built vendors generally offer better scalability, efficiency, and access to the latest technologies and algorithms.
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