AI10 min read
Vector Databases in the Era of LLMs
Understanding how vector embeddings work and why vector databases like Pinecone and Milvus have become essential infrastructure for modern AI apps.
ALI
2023-12-18
HIGH-DIMENSIONAL EMBEDDING SPACE
Large Language Models (
LLMs
) are powerful, but they lack long-term memory and knowledge of private data. Enter Vector Databases.
By converting text, images, or audio into high-dimensional vector embeddings, we can perform semantic search—finding information based on meaning rather than exact keyword matches. This is the foundation of Retrieval-Augmented Generation (RAG
).
As AI applications scale, specialized vector databases are proving critical for managing billions of embeddings with low-latency retrieval, becoming as standard as relational databases in the modern stack.