Have you had experience with RAG and vector search?

RAG (Retrieval-Augmented Generation) is an approach where a generative model is supplemented with information retrieved from external sources, such as knowledge bases or documents. Vector search is used for quickly finding relevant documents based on their vector representations obtained through embeddings.

Experience with RAG and vector search typically includes:

• Creating text embeddings (e.g., using BERT models, Sentence Transformers).
• Indexing these embeddings in vector databases (e.g., FAISS, Pinecone).
• Implementing a nearest neighbor search mechanism for quickly finding relevant documents.
• Integrating search results with a generative model to improve answer quality.

For example, in Java, libraries for working with vector indexes can be used and integrated with NLP models to implement a RAG system.