RAG — Retrieval as a Multi-Step Process: Optimizing Knowledge Graph and Vector Retrieval
Retrieval systems aim to identify and return the most relevant information for a given query from a large corpus of data. This could involve searching a database of documents, finding pertinent passages from a collection of texts, or retrieving the most useful knowledge from a vast knowledge graph. The challenge is doing this quickly, accurately, and at scale.
Traditionally, retrieval has been treated as a single-step process. A query is used to directly search over the full corpus using some scoring function, and the top matching results are returned. For small datasets, this monolithic approach can work reasonably well. However, as the volume of data to search over grows exponentially, critical limitations emerge.
Querying the entirety of a massive corpus directly is slow. Computing relevance on huge vector spaces or densely interconnected knowledge graphs becomes intractable. It becomes difficult to balance precision and recall, as directly optimizing for one hurts the other.
Furthermore, restricting to a single retrieval step makes it hard to incorporate diverse signals and strategies that each have complementary strengths and weaknesses.
0 Comments