GenAI RAG Vector.

Retrieval-Augmented Generation (RAG) is a Generative AI (GenAI) architecture that uses vector databases to improve the accuracy and reliability of AI models: 
RAG
A technique that enhances the output of a large language model (LLM) by referencing external knowledge bases. RAG improves the relevance, accuracy, and usefulness of LLM output without retraining the model. 
Vector databases
Specialized storage systems that handle multi-dimensional data, such as images and text. Data points in a vector database are typically represented as vectors. 
RAG and vector databases
Vector databases are widely used to enable RAG. They are needed when there are so many possible inputs that linear retrieval is too slow. 
Some examples of vector databases include: 
Chroma, Pinecone, Weaviate, Faiss, Qdrant, Milvus, and pgvector.
RAG involves two main components: the retriever and the generator: 
Retriever: Finds relevant documents based on the input query
Generator: Uses the retrieved documents and the original query to generate a response

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