What is multi-step retrieval in RAG? Multi-step retrieval, or multi-hop retrieval, in Retrieval-Augmented Generation (RAG) refers to the process of iteratively querying a knowledge source to gather multiple pieces of information needed to answer a complex question. Unlike basic RAG, which retrieves a single set of documents to generate an answer, multi-step retrieval breaks down the problem into intermediate queries. Each query depends on prior results, allowing the system to "connect the dots" across disparate data points. This approach is necessary when a question requires synthesizing information from different sources or making logical inferences that aren’t explicitly stated in one document.
Why is it needed? Single-step retrieval struggles with questions that demand layered reasoning. For example, consider the question: "Which actor starred in both Inception and Interstellar, and who directed these films?" Answering this requires two distinct retrieval steps:
- First, identify the director of both movies (Christopher Nolan).
- Next, retrieve the cast lists for Inception and Interstellar, then find the overlapping actor (Michael Caine).
A basic RAG system might fail here if it retrieves only director information or only one movie’s cast. Multi-step retrieval explicitly chains these queries: the system first fetches director details, then uses that context to query for each film’s cast, and finally compares the results.
Another example and technical implications A question like "What company acquired the startup that developed the first transformer model?" would require:
- Retrieving the startup associated with the first transformer model (e.g., OpenAI, though historically context-dependent).
- Searching for acquisition details related to that startup.
In practice, multi-step retrieval systems use intermediate queries (e.g., rewriting the original question into sub-questions) or leverage metadata (like timestamps) to prioritize relevant documents. Developers implementing this might design a pipeline where the output of one retrieval step informs the next query’s parameters, ensuring the system traverses connected data points. This approach improves accuracy for complex questions but introduces challenges like error propagation between steps and increased computational overhead.