ai-engineer-rag-implementation-vector-database

# ai-engineer-rag-implementation-vector-database

## Contents

__Roadmap info from [ roadmap website ] (https://roadmap.sh/ai-engineer/vector-database@zZA1FBhf1y4kCoUZ-hM4H) __

  ## Vector Database

  When
  implementing
  Retrieval-Augmented
  Generation
  (RAG), a vector database is used to store and efficiently retrieve embeddings, which are vector representations of data like documents, images, or other knowledge sources. During the RAG process, when a query is made, the system converts it into an embedding and searches the vector database for the most relevant, similar embeddings (e.g., related documents or snippets). These retrieved pieces of information are then fed to a generative model, which uses them to produce a more accurate, context-aware response.