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?
you could do a rag that uses vector embeddings, but you could also just ask the llm for a search query and use that to search a database and that would still be a rag
This is interesting, you are saying like , you have embeddings on vector db , and you ask llm to give you some kind of sql query to search in vec db ?
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.