Vector Databases: Similarity Search & Semantic Search for AI

AI Impact Summary

The introduction of vector databases is driven by the rise of LLMs and the need for efficient retrieval of context for RAG pipelines. These databases store data as vector embeddings, enabling similarity search and semantic search capabilities, which are crucial for applications like recommendation systems and conversational AI. The ability to represent data semantically, rather than relying on keyword matching, unlocks more flexible and intuitive search experiences.