Update app.py

app.py

@@ -231,10 +231,23 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search):
         answer = memory_database[question]
     else:
         model = get_model(temperature, top_p, repetition_penalty)
+        embed = get_embeddings()
         
         if web_search:
             search_results = google_search(question)
             context_str = "\n".join([result["text"] for result in search_results if result["text"]])
+            
+            # Convert web search results to Document format
+            web_docs = [Document(page_content=result["text"], metadata={"source": result["link"]}) for result in search_results if result["text"]]
+            
+            # Load or create the vector database
+            if os.path.exists("faiss_database"):
+                database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
+                database.add_documents(web_docs)
+            else:
+                database = FAISS.from_documents(web_docs, embed)
+                database.save_local("faiss_database")
+            
             prompt_template = """
             Answer the question based on the following web search results:
             Web Search Results:
@@ -246,7 +259,6 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search):
             prompt_val = ChatPromptTemplate.from_template(prompt_template)
             formatted_prompt = prompt_val.format(context=context_str, question=question)
         else:
-            embed = get_embeddings()
             database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
             history_str = "\n".join([f"Q: {item['question']}\nA: {item['answer']}" for item in conversation_history])