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title: Medical RAG with Meditron-7B-LLM | |
emoji: π | |
colorFrom: blue | |
colorTo: indigo | |
sdk: docker | |
sdk_version: 20.10.17 | |
app_file: app.py | |
pinned: false | |
license: apache-2.0 | |
short_description: A specialized AI assistant for medical information retrieval | |
# Medical RAG QA System | |
A Retrieval-Augmented Generation (RAG) system for medical question answering using: | |
- Meditron-7B LLM | |
- Qdrant Vector Database | |
- PubMedBERT Embeddings | |
[](https://opensource.org/licenses/Apache-2.0) | |
[](https://huggingface.co/joshnader/meditron-7b-Q4_K_M-GGUF) | |
## Features | |
- PDF document ingestion | |
- Semantic search with medical embeddings | |
- LLM-powered question answering | |
- Source document citation | |
## π Usage | |
1. **Query Interface**: | |
```python | |
import requests | |
response = requests.post("https://d221/Qdrant_Backend.hf.space/get_response", | |
data={"query": "What are the symptoms of diabetes?"}) | |
print(response.json()) |