Retrieval Process

The retrieval process in Retrieval-Augmented Generation (RAG) involves finding relevant information from a large dataset or knowledge base to support the generation of accurate, context-aware responses. When a query is received, the system first converts it into a vector (embedding) and uses this vector to search a database of pre-indexed embeddings, identifying the most similar or relevant data points. Techniques like approximate nearest neighbor (ANN) search are often used to speed up this process.