BaranziniLab/KG_RAG
A knowledge graph-based retrieval-augmented generation system that enhances LLMs for domain-specific question answering.
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The system retrieves structured knowledge from knowledge graphs to augment LLM prompts, improving accuracy on knowledge-intensive tasks. It integrates with multiple LLM providers (GPT-3.5/4, Llama) and can be configured for domain-specific applications. Includes a benchmark dataset (BiomixQA) for evaluation.
Frequently asked
- What is BaranziniLab/KG_RAG?
- A knowledge graph-based retrieval-augmented generation system that enhances LLMs for domain-specific question answering.
- Is KG_RAG open source?
- Yes — BaranziniLab/KG_RAG is open source, released under the Apache-2.0 license.
- What language is KG_RAG written in?
- BaranziniLab/KG_RAG is primarily written in Jupyter Notebook.
- How popular is KG_RAG?
- BaranziniLab/KG_RAG has 939 stars on GitHub.
- Where can I find KG_RAG?
- BaranziniLab/KG_RAG is on GitHub at https://github.com/BaranziniLab/KG_RAG.