Update app.py

app.py

@@ -206,72 +206,55 @@ class AdvancedRAG:
         self.assistant_id: Optional[str] = os.getenv("ASSISTANT_ID")
 
     def create_thread(self) -> str:
-        thread = openai.beta.threads.create(
-            tool_resources={
-                "file_search": {
-                    "vector_store_ids": [self.vector_store_id]
-                }
-            }
-        )
+        thread = openai.beta.threads.create()
         self.thread_id = thread.id
         return self.thread_id
 
-    def create_vector_store(self, name: str = "My Vector Store") -> str:
+    def upload_document(self, file) -> str:
         try:
-            vector_store = openai.beta.vector_stores.create(name=name)
-            self.vector_store_id = vector_store.id
-            return self.vector_store_id
-        except Exception as e:
-            raise Exception(f"Error creating vector store: {str(e)}")
+            # Create a new file
+            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.name)[1]) as tmp:
+                tmp.write(file.read())
+                tmp.flush()
+                with open(tmp.name, "rb") as file_obj:
+                    file_obj = openai.files.create(
+                        file=file_obj,
+                        purpose="assistants"
+                    )
+                    self.file_ids.append(file_obj.id)
 
-    def upload_document(self, file) -> str:
-        if self.vector_store_id:
-            try:
-                openai.beta.vector_stores.delete(self.vector_store_id)
-            except Exception as e:
-                print(f"Could not delete previous vector store {self.vector_store_id}: {e}")
-        
-        vector_store = openai.beta.vector_stores.create(name="knowledge_base")
-        self.vector_store_id = vector_store.id
-        self.file_ids = []
+            # Create a new thread with the file
+            thread = openai.beta.threads.create()
+            self.thread_id = thread.id
 
-        with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.name)[1]) as tmp:
-            tmp.write(file.read())
-            tmp.flush()
-            with open(tmp.name, "rb") as file_obj:
-                file_obj = openai.files.create(
-                    file=file_obj,
-                    purpose="assistants"
-                )
-                self.file_ids.append(file_obj.id)
-                openai.beta.vector_stores.files.create(
-                    vector_store_id=self.vector_store_id,
-                    file_id=file_obj.id
-                )
+            # Add the file to the thread
+            openai.beta.threads.messages.create(
+                thread_id=self.thread_id,
+                role="user",
+                content="I have uploaded a document. Please analyze it.",
+                file_ids=[file_obj.id]
+            )
 
-        thread = openai.beta.threads.create(
-            tool_resources={
-                "file_search": {
-                    "vector_store_ids": [self.vector_store_id]
-                }
-            }
-        )
-        self.thread_id = thread.id
-        return file_obj.id
+            return file_obj.id
+        except Exception as e:
+            raise Exception(f"Error uploading document: {str(e)}")
 
     def ask_question(self, question: str) -> str:
         try:
+            # Add the question to the thread
             openai.beta.threads.messages.create(
                 thread_id=self.thread_id,
                 role="user",
                 content=question
             )
             
+            # Create a run
             run = openai.beta.threads.runs.create(
                 thread_id=self.thread_id,
                 assistant_id=self.assistant_id
             )
             
+            # Wait for the run to complete
             waited = 0
             while True:
                 run_status = openai.beta.threads.runs.retrieve(
@@ -287,6 +270,7 @@ class AdvancedRAG:
                 if waited > 60:
                     raise Exception("Run timed out after 60 seconds.")
 
+            # Get the latest message
             messages = openai.beta.threads.messages.list(
                 thread_id=self.thread_id,
                 order='desc',