Buckets:
| import sys | |
| import os | |
| sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| from rag_engine.retriever import OncoRAGRetriever | |
| def main(): | |
| queries = [ | |
| "Patient with Stage III colon cancer, KRAS mutated", | |
| "Paciente masculino de 65 años con cáncer de colon en Estadio III, mutación KRAS", | |
| "Tratamiento para glioblastoma recurrente en adultos mayores", | |
| "How to treat a common cold with vitamin C", | |
| "Melanoma metastásico con mutación BRAF V600E, progresión tras ipilimumab", | |
| "Receta para hacer una torta de chocolate", | |
| "Non-small cell lung cancer stage IV with EGFR exon 19 deletion", | |
| "Dolor de cabeza leve y fiebre baja en niño de 8 años" | |
| ] | |
| retriever = OncoRAGRetriever() | |
| for q in queries: | |
| print(f"\n{'='*60}\nQuery: {q}") | |
| candidates, distances = retriever._bi_encoder_retrieve(q, 5) | |
| for i, (cand, dist) in enumerate(zip(candidates, distances)): | |
| pass_gate = "PASS" if dist <= retriever.distance_threshold else "FAIL" | |
| print(f" [{i}] Dist: {dist:.4f} [{pass_gate}] | Source: {cand['source']} - {cand['header']}") | |
| if __name__ == '__main__': | |
| main() | |
Xet Storage Details
- Size:
- 1.22 kB
- Xet hash:
- fc02de5c92199f8acb8657384db1b010df06b0b673a5dc420f2ceade997a5363
·
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