Create tagger.py

tagger.py

@@ -0,0 +1,29 @@
+from sklearn.metrics.pairwise import cosine_similarity
+
+def clause_match_by_cosine(chunk_embeddings, clause_reference):
+    reference_embeddings = [clause['embedding'] for clause in clause_reference]
+    reference_labels = [clause['label'] for clause in clause_reference]
+    tagged_chunks = []
+
+    for i, chunk_data in enumerate(chunk_embeddings):
+        chunk_text = chunk_data['chunk']
+        chunk_embed = chunk_data['embedding']
+
+        similarities = cosine_similarity([chunk_embed], reference_embeddings)[0]
+        top_indices = similarities.argsort()[-3:][::-1]
+
+        top_matches = [
+            {
+                "label": reference_labels[idx],
+                "similarity": float(similarities[idx])
+            }
+            for idx in top_indices if similarities[idx] >= 0.75
+        ]
+
+        tagged_chunks.append({
+            "chunk_index": i,
+            "chunk_preview": chunk_text[:200],
+            "top_matches": top_matches
+        })
+
+    return tagged_chunks