sofvBV_mcp重构v2

Embedding copy

mcp/SearchPaperByEmbedding/demo.py

@@ -0,0 +1,22 @@
+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')
+