Create Scientific_Embedding.py

src/models/Scientific_Embedding.py

@@ -0,0 +1,22 @@
+# © 2025 Elena Marziali — Code released under Apache 2.0 license.
+# See LICENSE in the repository for details.
+# Removal of this copyright is prohibited.
+
+# This section manages the system's memory, allowing efficient storage and
+# retrieval of scientific content. Embeddings are generated using models
+# specialized for academic texts.
+
+def safe_encode(text):
+    if not isinstance(text, str) or not text.strip():
+        raise ValueError("Il testo da codificare è vuoto o non valido.")
+    try:
+        return embedding_model.encode([text])
+    except Exception as e:
+        print(f"Errore durante l'embedding: {e}")
+        return np.zeros((1, 768), dtype=np.float32)  # fallback neutro
+
+
+# === Load Specter model ===
+word_embedding_model = models.Transformer("allenai/specter")
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
+embedding_model = SentenceTransformer(modules=[word_embedding_model, pooling_model])