Using vector storage and search is becoming increasingly popular due to the momentum that Generative AI has gained. It is a critical part of many use cases that require relevant context for prompts, while staying within the token limit of LLMs.
There are many options available for storing vector data and performing searches. Some are provided as third-party hosted APIs, while others can be deployed as containers to Kubernetes clusters. There is also an interesting alternative: using a good old PostgreSQL database with the pgvector extension!