Update app.py

app.py

@@ -169,8 +169,7 @@ def load_business_info():
         print(traceback.format_exc())
         business_info_available = False
 
-# --- Business Info Retrieval (RAG) ---
-def retrieve_business_info(query: str, top_n: int = 3) -> list:
+def retrieve_business_info(query: str, top_n: int = 2) -> list: # Reduced top_n
     """
     Retrieves relevant business information from loaded data based on a query.
     """
@@ -182,7 +181,11 @@ def retrieve_business_info(query: str, top_n: int = 3) -> list:
     try:
         query_embedding = embedder.encode(query, convert_to_tensor=True)
         cosine_scores = util.cos_sim(query_embedding, embeddings)[0]
-        top_results_indices = torch.topk(cosine_scores, k=min(top_n, len(data)))[1].tolist()
+        # Get the top N indices based on cosine similarity
+        # Make sure k does not exceed the number of available descriptions
+        top_results_indices = torch.topk(cosine_scores, k=min(top_n, len(descriptions_for_embedding)))[1].tolist()
+
+        # Retrieve the actual data entries corresponding to the top indices
         top_results = [data[i] for i in top_results_indices]
 
         if reranker is not None and top_results: