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Abstract
Motivation
Answering and solving complex problems using a large language model (LLM) given a certain domain such as biomedicine is a challenging task that requires both factual consistency and logic, and LLMs often suffer from some major limitations, such as hallucinating false or irrelevant information, or being influenced by noisy data. These issues can compromise the trustworthiness, accuracy, and compliance of LLM-generated text and insights.
Results
Knowledge Retrieval Augmented Generation ENgine (KRAGEN) is a new tool that combines knowledge graphs, Retrieval Augmented Generation (RAG), and advanced prompting techniques to solve complex problems with natural language. KRAGEN converts knowledge graphs into a vector database and uses RAG to retrieve relevant facts from it. KRAGEN uses advanced prompting techniques: namely graph-of-thoughts (GoT), to dynamically break down a complex problem into smaller subproblems, and proceeds to solve each subproblem by using the relevant knowledge through the RAG framework, which limits the hallucinations, and finally, consolidates the subproblems and provides a solution. KRAGEN’s graph visualization allows the user to interact with and evaluate the quality of the solution’s GoT structure and logic.
Availability and implementation
KRAGEN is deployed by running its custom Docker containers. KRAGEN is available as open-source from GitHub at: https://github.com/EpistasisLab/KRAGEN.
Academic Publication KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models
Research Abstract & Technology Focus
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Frequently Asked Questions (FAQ)
Curated market intelligence mapped to this research.
What is the core focus of the research titled 'KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models'?
This literature focuses on: Abstract Motivation Answering and solving complex problems using a large language model (LLM) given a certain domain such as biomedicine is a challenging task that requires both factual consisten...
Are there open-source GitHub repositories related to KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models?
Yes, open-source projects like BigBodyCobain/Shadowbroker (Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic ev...) are actively building upon these concepts.
Which startups are commercializing the technology behind KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models?
Products like WUPHF by Nex.ai are bringing this to market. Their focus is: AI employees who build their own knowledge base.
What other academic literature is closely related to 'KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models'?
Yes, highly correlated activity was mapped. An entry titled 'KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models' discusses this: Abstract Motivation Answering and solving complex problems using a large language model (LLM) g...
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Commercial Realization
Startups and Open Source tools heavily associated with the concepts explored in this paper.
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GitHubBigBodyCobain/Shadowbroker
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GitHubLum1104/Understand-Anything
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Product HuntWUPHF by Nex.ai
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Product HuntLiminary