I’ve recently been involved in document processing and knowledge management, and I have a mental image that helps with understanding the difference between vector and graph-based RAG systems.
Imagine your local library as a knowledge hub. When a new book arrives, the team records its details and assigns subject headings to create a comprehensive bibliographic record.
Digitally, the book’s content is transformed into embeddings, capturing its essence and storing it in a vector database that understands context.
Your librarian, an LLM, has knowledge of millions of books but relies on its last training session. Occasionally, it might confidently mention information that doesn’t exist. This is where VectorRAG steps in. When a visitor poses a question, the system translates it into a vector, scouts the database for similar documents, and presents them to the LLM alongside the original query. This method anchors the LLM’s responses in real sources, minimising errors and ensuring the information is up-to-date.
Vector-based retrieval excels at handling straightforward factual questions where semantic similarity matches relevance. However, when questions become complex, like \”Did any former Google employees start their own company?\” the system’s elegance falters. These multi-hop inquiries require linking information across various catalogue sections. As questions span multiple domains, involving six or nine sections, vector search accuracy diminishes. The system retrieves document chunks mentioning related terms independently, but vital connections remain obscured, and repeated information floods the results.
To address these challenges, the library has other tools available that goes beyond mere vectors to search for concepts, not just strings. Inverted indexes allow for precise keyword matching, retrieving exact documents without semantic confusion.
Knowledge graphs, the pinnacle of this evolution, encode real relationships rather than numerical proximity. Unlike vectors, which approximate similarity, graphs explicitly depict relationships. They maintain context, navigate explicit connections, and enable traceable multi-hop reasoning that vector similarity cannot achieve.
Your library must be equipped to handle complex retrieval, transitioning from approximate to precise. Inverted indexes provide exact term matching, while knowledge graphs encode meaningful relationships. Together, they offer accuracy, explainability, and the ability to address complex questions across various domains.
#graphrag #knowledgegraphs
