Vector Databases
Explore the role of vector databases in AI, focusing on how they store and retrieve high-dimensional embeddings to power applications like semantic search and retrieval-augmented generation. Understand their differences from traditional databases, key technical challenges in integrating them with large language models, and privacy considerations such as embedding inversion risks.
Generative AI applications, especially those involving large language models (LLMs), increasingly rely on vector databases to enhance their capabilities. In use cases such as retrieval-augmented generation (RAG), semantic search, and recommendation systems, vector databases offer a means to inject relevant knowledge or identify similar items based on meaning rather than exact keywords.
Vector databases are becoming foundational in these GenAI scenarios because they enable low-latency similarity queries at scale, something not feasible with classic relational databases. It won’t be wrong to say that shortly, most enterprises will have adopted vector databases to build their foundation models with relevant business data. As a result, questions about vector databases are now ...
