Practical AI Vector databases (beyond the hype)
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Aug 1, 2023 Prashanth Rao, a Senior AI and data engineer at the Royal Bank of Canada, shares his hands-on experience with vector databases. He breaks down the evolution of data management from SQL to NoSQL while highlighting the efficiency of vector representations. The conversation delves into integrating transformer models for advanced search capabilities and discusses the maturity and trade-offs of current database options. Prashanth also explores future trends in disk-based vector databases, emphasizing their potential to revolutionize information retrieval.
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Vector Database Definition
- Vector databases efficiently manage, store, and update vectors at scale.
- They retrieve semantically similar vectors, considering meaning beyond keywords.
Semantic Search
- Semantic search considers the meaning of words, not just their presence.
- It translates queries into concepts the database understands, yielding meaningful results.
Vector Databases as NoSQL Evolution
- Vector databases are an evolution of NoSQL databases, extending their capabilities.
- They address the limitations of rigid schemas and keyword-based search.