from search import PaperSearcher

# Use local model (free)
searcher = PaperSearcher('iclr2026_papers.json', model_type='local')

# Or use OpenAI (better quality)
# searcher = PaperSearcher('iclr2026_papers.json', model_type='openai')

searcher.compute_embeddings()

examples = [
    {
        "title": "Improving Developer Emotion Classification via LLM-Based Augmentation",
        "abstract": "Detecting developer emotion in the informative data stream of technical commit messages..."
    },
]

results = searcher.search(examples=examples, top_k=100)

searcher.display(results, n=10)
searcher.save(results, 'results.json')