Update app.py

app.py

@@ -495,30 +495,40 @@ def create_web_search_vectors(search_results):
 
 def get_response_with_search(query, model, num_calls=3, temperature=0.2):
     search_results = duckduckgo_search(query)
-    ranked_results = rank_results(query, search_results)
-    web_search_database = create_web_search_vectors(ranked_results)
     
-    if not web_search_database:
+    if not search_results:
         yield "No web search results available. Please try again.", ""
         return
     
-    retriever = web_search_database.as_retriever(search_kwargs={"k": 3})
-    relevant_docs = retriever.get_relevant_documents(query)
-    
     accumulated_response = ""
 
-    for i, doc in enumerate(relevant_docs, 1):
-        context = doc.page_content
-        source = doc.metadata.get('source', 'Unknown source')
-        date = doc.metadata.get('date', 'Unknown date')
+    # Split the search results into separate documents
+    documents = search_results.split("------------------------------------------------------------")
+    
+    for i, doc in enumerate(documents, 1):
+        if not doc.strip():  # Skip empty documents
+            continue
+        
+        # Extract title, URL, and content from the document
+        title_start = doc.find("Website Title:") + len("Website Title:")
+        title_end = doc.find("===========", title_start)
+        title = doc[title_start:title_end].strip()
+        
+        url_start = doc.find("Website URL:") + len("Website URL:")
+        url_end = doc.find("===========", url_start)
+        source = doc[url_start:url_end].strip()
+        
+        content_start = doc.find("Website Content") + len("Website Content ===========")
+        content_end = doc.find("Website Content End")
+        context = doc[content_start:content_end].strip()
         
         prompt = f"""Using the following context from a web search result:
 {context}
-This information is from {date}.
+This information is from the website titled: {title}
 You are an expert AI assistant. Write a detailed and complete research article that fulfills the following user request: '{query}'
 Base your summary strictly on the information from this source. Only include information that is directly supported by the given content.
 If any part of the information cannot be verified from this source, clearly state that it could not be confirmed."""
-          
+        
         if model == "@cf/meta/llama-3.1-8b-instruct":
             # Use Cloudflare API
             source_response = ""