The Retrieval Stage
In the RAG model, the user's query is first converted into a vector representation, and then vector retrieval is performed in the knowledge base. Usually, the retriever uses pre-trained models such as BERT to generate the vector representations of the query and document fragments, and matches the most relevant document fragments through similarity calculations (such as cosine similarity). The retriever of RAG does not rely solely on simple keyword matching but adopts semantic-level vector representations. Thus, when facing complex questions or fuzzy queries, it can find relevant knowledge more accurately. This step is crucial for the final generated answer because the efficiency and quality of the retrieval directly determine the contextual information available for the generator.
The Generation Stage
The generation stage is the core part of the RAG model, and the generator is responsible for generating coherent and natural text answers based on the retrieved content. Generators in RAG, such as models like BART or GPT, combine the user's input query and the retrieved document fragments to generate more accurate and rich answers. Compared with traditional generative models, the generator of RAG can not only generate fluent language answers but also provide more fact-based content according to the actual information in the external knowledge base, thereby improving the accuracy of generation.
The Multi-round Interaction and Feedback Mechanism
The RAG model can effectively support multi-round interaction in the dialogue system. Each round of query and generated result will be used as the input for the next round. The system gradually optimizes the context of subsequent queries by analyzing and learning from the user's feedback. Through this cyclic feedback mechanism, RAG can better adjust its retrieval and generation strategies, making the answers generated in multi-round conversations increasingly meet the user's expectations. In addition, multi-round interaction also enhances the adaptability of RAG in complex dialogue scenarios, enabling it to handle knowledge integration and complex reasoning across multiple rounds.