I am confused about these 2 . Sometimes people use it interchangeably. Is it because rag is a method and where u store it should be vector db ? I remember before llms there was word2vec in the beginning ,before all of this llm. But isn’t the hard part to create such a meaningful word2vec , by the way word2vec is now called “embeddings” right?
Most often you search the vector db with natural language, there is no special schema to use but you do need to consider how the embedding model is capturing the vectors so it is matched with the embedded query. RAG actually also describes when the LLM is driving the searches, and is the only way I have coded it, the user may ask for something but the LLM creates the search query based on that and the conversation history.