assistant/ConfigureAssistant.py

@@ -94,24 +94,22 @@ class ConfigureAssistant:
         Preprocess files based on their type.
 
         :param file_path: The file path to preprocess.
-        :return: The new filename and a flag indicating if the file should be uploaded.
+        :return: The filename to be used during upload and a flag indicating if the file should be uploaded.
         """
         extension = file_path.split(".")[-1].lower()
 
-        # TO DO: Preprocess Outjob and PcbDoc files into something OpenAI (or future vector DB) can understand
         excluded_extensions = ["schdoc", "exe", "so", "dll", "outjob", "pcbdoc", "png"]
 
-        # If the extension is not in supported formats and not in excluded extensions, rename the file
+        # If the extension is not in supported formats and not in excluded extensions, spoof the file name
         if extension not in self.SUPPORTED_FORMATS and extension not in excluded_extensions:
-            new_file_path = f"{file_path}.txt"
-            os.rename(file_path, new_file_path)
-            print(f"Renamed unsupported file: {file_path} to {new_file_path}")
-            return new_file_path, True
+            new_file_name = f"{os.path.basename(file_path)}.txt"
+            print(f"Spoofing unsupported file: {file_path} as {new_file_name}")
+            return new_file_name, True
         elif extension in self.SUPPORTED_FORMATS:
-            return file_path, True
+            return os.path.basename(file_path), True
         else:
             print(f"Skipping excluded file: {file_path}")
-            return file_path, False
+            return os.path.basename(file_path), False
 
     def create_vector_store(self):
         """
@@ -133,10 +131,10 @@ class ConfigureAssistant:
             if not should_upload:
                 continue
             try:
-                with open(new_filename, "rb") as file_stream:
+                with open(path, "rb") as file_stream:
                     file_content = file_stream.read()
                     spoofed_file = io.BytesIO(file_content)
-                    spoofed_file.name = os.path.basename(new_filename)  # Spoof the filename
+                    spoofed_file.name = new_filename  # Spoof the filename
                     # Upload the file to the vector store
                     file_batch = (
                         self.client.beta.vector_stores.file_batches.upload_and_poll(