honeyandme/RAGQnASystem
本项目设计并实现了一个基于知识图谱 RAG 与大语言模型的医疗智能问答系统。系统以 DiseaseKG 医疗数据集为基础，依托 Neo4j 构建包含 4.4 万实体与 31 万关系的领域知识图谱；结合 BERT 命名实体识别与 32B 大模型意图识别，通过精确的图谱检索与受控的答案生成，有效缓解大模型在医疗场景中的幻觉问题，显著提升回答的准确性与可靠性。 observed · 2026-08-28
Health v2 · maintenance only
62/100
- Activity 84
- Release rhythm 35
- Longevity 59
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 839
- days_rel: n/a
- days_push: 96
- n_releases_24m: 0
Adoption not part of the score
1415 stars · 149 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A medical intelligent question-answering system combining knowledge-graph RAG with large language models, built on the DiseaseKG dataset with a Neo4j graph of 44k entities and 310k relations. It uses BERT+RNN for named entity recognition, a local LLM via ollama for intent recognition and streaming answer generation, and a Streamlit UI, aiming to reduce hallucinations in medical Q&A.
Use cases
- build a medical question answering chatbot
- answer health questions from a disease knowledge graph
- reduce LLM hallucinations with structured knowledge retrieval
- run a local RAG system with ollama
- do named entity recognition on medical questions
- query a Neo4j medical knowledge graph with Cypher
- classify user question intents with an LLM
- learn how to combine KG retrieval with LLM generation
When to choose
- you need accurate, grounded answers for medical/disease questions in Chinese
- you want a self-hosted RAG pipeline using a knowledge graph instead of vector search
- you want a complete reference implementation with NER, intent routing, and a web UI
- you have Neo4j and a local LLM available and want to study KG-RAG architecture
When to avoid
- you need a production-grade, clinically validated medical system
- you need English-language medical Q&A rather than Chinese
- you want a simple vector-database RAG setup
- you need a hosted SaaS solution or a permissive license (none is provided)
Facets
application · maturity active
rag nlp chatbot machine-learning search-engine llm-inference healthcare large-language-models chatbots artificial-intelligence python self-hosted knowledge-graph neo4j bert-ner medical-qa ollama streamlit intent-recognition hallucination-mitigation natural-language-processing retrieval-augmented-generation docker
1 source
- readme: https://github.com/honeyandme/RAGQnASystem · fetched 2026-08-28 · 31190d764ed9
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| honeyandme/RAGQnASystem | main | 62 |
For agents
markdown · JSON · MCP: product_card(name="honeyandme/RAGQnASystem")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem