Update app.py

app.py

@@ -24,16 +24,25 @@ def create_embeddings(texts):
 
 # Use Faiss for similarity search
 def search(query, documents, k=1):
+    if not documents:  # Ensure documents are not empty
+        return ["No documents available for searching."]
+    
     embeddings = create_embeddings([query] + documents)
     query_embedding = embeddings[0]
     doc_embeddings = np.stack(embeddings[1:])
+
+    if doc_embeddings.shape[0] == 0:  # Ensure embeddings are non-empty
+        return ["No embeddings available for searching."]
     
     index = faiss.IndexFlatL2(doc_embeddings.shape[1])  # L2 distance for similarity
     index.add(doc_embeddings)
-    
-    # Search for the top-k most similar documents
-    D, I = index.search(np.array([query_embedding]), k)
-    return [documents[i] for i in I[0]]
+
+    # Perform search
+    try:
+        D, I = index.search(np.array([query_embedding]), k)
+        return [documents[i] for i in I[0] if i < len(documents)]  # Ensure indices are valid
+    except Exception as e:
+        return [f"Error during search: {str(e)}"]
 
 # Function for Text-to-Speech
 def text_to_speech(text):