Are you having trouble getting RAG or GraphRAG to work as expected when building a knowledge base? This is due to a fundamental difference in approach.
While “concepts” are essential for rapid decision-making, RAG lacks them. GraphRAG, on the other hand, attempts to process information using ontologies rather than concepts. As a result, the more the system strives for perfect processing, the more complex it inevitably becomes.
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With ThinkNavi’s LLM Wiki+ conceptual structure model approach, you can build query-response systems that work reliably and more easily than traditional RAG or Graph RAG systems. Simply specify a website or document of up to approximately 1000 pages, and it will automatically compile it into a knowledge base, instantly building a response system.
The response system we have built can be used as follows:
- Operated as an internal inquiry system by logging into ThinkNavi.
- Operated as a public inquiry system/chatbot by embedding it on a website.
- Distributed free of charge or for a fee through the Store.
If you are interested in building a cross-functional knowledge base within your organization, we can integrate individual document-based knowledge bases using a conceptual structure model. We also offer on-premises deployment via an internal server; please contact us for further details.