FastGPT's knowledge base retrieval process is divided into two steps: first, an embedding model chunks the uploaded documents and converts each chunk into a vector stored in a vector database; when a user asks a question, the same embedding model converts the question into a vector, and by computing cosine similarity or inner product the most relevant document chunks are found, which are finally handed to the chat model to generate an answer.