Update app.py

app.py

@@ -255,6 +255,10 @@ def summarize_content(content, model):
     return summary
 
 def rank_search_results(titles, summaries, model):
+    if not titles or not summaries:
+        print("No titles or summaries to rank.")
+        return list(range(1, len(titles) + 1))
+
     ranking_prompt = (
         "Rank the following search results from a financial analyst perspective. "
         f"Assign a rank from 1 to {len(titles)} based on relevance, with 1 being the most relevant. "
@@ -273,12 +277,13 @@ def rank_search_results(titles, summaries, model):
         
         # Check if we have the correct number of ranks
         if len(ranks) != len(titles):
-            raise ValueError("Number of ranks does not match number of titles")
+            print(f"Warning: Number of ranks ({len(ranks)}) does not match number of titles ({len(titles)})")
+            print(f"Model output: {ranks_str}")
+            return list(range(1, len(titles) + 1))
         
         return ranks
     except Exception as e:
         print(f"Error in ranking: {str(e)}. Using fallback ranking method.")
-        # Fallback: assign ranks based on original order
         return list(range(1, len(titles) + 1))
 
 def ask_question(question, temperature, top_p, repetition_penalty, web_search):
@@ -417,20 +422,26 @@ def update_vector_db_with_search_results(search_results, summaries, ranks):
     
     current_date = datetime.now().strftime("%Y-%m-%d")
     
+    documents = []
     for result, summary, rank in zip(search_results, summaries, ranks):
-        doc = Document(
-            page_content=summary,
-            metadata={
-                "search_date": current_date,
-                "search_title": result["title"],
-                "search_content": result["text"],
-                "search_summary": summary,
-                "rank": rank
-            }
-        )
-        database.add_documents([doc])
+        if summary:  # Only create a document if there's a summary
+            doc = Document(
+                page_content=summary,
+                metadata={
+                    "search_date": current_date,
+                    "search_title": result["title"],
+                    "search_content": result["text"],
+                    "search_summary": summary,
+                    "rank": rank
+                }
+            )
+            documents.append(doc)
     
-    database.save_local("faiss_database")
+    if documents:  # Only update the database if there are documents to add
+        database.add_documents(documents)
+        database.save_local("faiss_database")
+    else:
+        print("No valid documents to add to the database.")
 
 def export_vector_db_to_excel():
     embed = get_embeddings()