Enabling vector similarity search is the primary purpose of pgvector. Pgvector provides options for both exact and approximation searches
Vector embeddings, which are numerical representations of data points, can also be stored using Pgvector
Google Cloud is pleased to announce the release of a quickstart solution and reference architecture for Retrieval Augmented Generation (RAG) applications
Retraining or optimising an LLM to deliver new, domain-specific data can be a costly and difficult procedure
RAG provides the LLM with access to this data without the need for fine-tuning or training
For RAG applications, Google Cloud has created a quickstart solution and reference architecture based on GKE
As traffic increases, GKE automatically allocates nodes, removing the need for human configuration to expand