KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
Discover advanced AI techniques in my repository combining Multi-Hop Chain of Thought (CoT) and Retrieval-Augmented Generation (RAG) using DSPy and Indexify. Enhance complex problem-solving with multi-step reasoning and external knowledge integration. Perfect for AI enthusiasts and researchers.
A doctoral dissertation titled "Towards the Advancement of Open-Domain Textual Question Answering Methods". The dissertation presents several innovative solutions aimed at addressing the challenges faced by ODQA systems and improving the performance.
Multi-hop question answering (MHQA) with complex retrieval augmented generation (RAG)
LLM agents for multi-hop question answering (MHQA)
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