diff --git a/ai_ta_backend/beam/ingest.py b/ai_ta_backend/beam/ingest.py
index a23043a2..a9cb0a4c 100644
--- a/ai_ta_backend/beam/ingest.py
+++ b/ai_ta_backend/beam/ingest.py
@@ -31,7 +31,6 @@
     Docx2txtLoader,
     GitLoader,
     PythonLoader,
-    SRTLoader,
     TextLoader,
     UnstructuredExcelLoader,
     UnstructuredPowerPointLoader,
@@ -41,7 +40,6 @@
 from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Qdrant
-
 from nomic_logging import delete_from_document_map, log_to_document_map, rebuild_map
 from OpenaiEmbeddings import OpenAIAPIProcessor
 from PIL import Image
@@ -157,7 +155,7 @@ def loader():
     loader=loader,
     autoscaler=autoscaler)
 def ingest(**inputs: Dict[str, Any]):
-    
+
   qdrant_client, vectorstore, s3_client, supabase_client, posthog = inputs["context"]
 
   course_name: List[str] | str = inputs.get('course_name', '')
@@ -173,8 +171,6 @@ def ingest(**inputs: Dict[str, Any]):
 
   ingester = Ingest(qdrant_client, vectorstore, s3_client, supabase_client, posthog)
 
-
-
   def run_ingest(course_name, s3_paths, base_url, url, readable_filename, content):
     if content:
       return ingester.ingest_single_web_text(course_name, base_url, url, content, readable_filename)
@@ -204,7 +200,7 @@ def run_ingest(course_name, s3_paths, base_url, url, readable_filename, content)
       time.sleep(13 * retry_num)  # max is 65
     else:
       break
-  
+
   # Final failure / success check
   if success_fail_dict['failure_ingest']:
     print(f"INGEST FAILURE -- About to send to supabase. success_fail_dict: {success_fail_dict}")
@@ -225,7 +221,7 @@ def run_ingest(course_name, s3_paths, base_url, url, readable_filename, content)
     }
     response = supabase_client.table('documents_failed').insert(document).execute()  # type: ignore
     print(f"Supabase ingest failure response: {response}")
-  else: 
+  else:
     # Success case: rebuild nomic document map after all ingests are done
     rebuild_status = rebuild_map(str(course_name), map_type='document')
 
@@ -510,9 +506,14 @@ def _ingest_single_video(self, s3_path: str, course_name: str, **kwargs) -> str:
     """
     print("Starting ingest video or audio")
     try:
+      # Ensure the media directory exists
+      media_dir = "media"
+      if not os.path.exists(media_dir):
+        os.makedirs(media_dir)
+
       # check for file extension
       file_ext = Path(s3_path).suffix
-      openai.api_key = os.getenv('OPENAI_API_KEY')
+      openai.api_key = os.getenv('VLADS_OPENAI_KEY')
       transcript_list = []
       with NamedTemporaryFile(suffix=file_ext) as video_tmpfile:
         # download from S3 into an video tmpfile
@@ -521,7 +522,7 @@ def _ingest_single_video(self, s3_path: str, course_name: str, **kwargs) -> str:
         mp4_version = AudioSegment.from_file(video_tmpfile.name, file_ext[1:])
 
       # save the extracted audio as a temporary webm file
-      with NamedTemporaryFile(suffix=".webm", dir="media", delete=False) as webm_tmpfile:
+      with NamedTemporaryFile(suffix=".webm", dir=media_dir, delete=False) as webm_tmpfile:
         mp4_version.export(webm_tmpfile, format="webm")
 
       # check file size
@@ -536,7 +537,7 @@ def _ingest_single_video(self, s3_path: str, course_name: str, **kwargs) -> str:
         count = 0
 
         while count < file_count:
-          with NamedTemporaryFile(suffix=".webm", dir="media", delete=False) as split_tmp:
+          with NamedTemporaryFile(suffix=".webm", dir=media_dir, delete=False) as split_tmp:
             if count == file_count - 1:
               # last segment
               audio_chunk = full_audio[start:]
@@ -613,27 +614,33 @@ def _ingest_single_docx(self, s3_path: str, course_name: str, **kwargs) -> str:
 
   def _ingest_single_srt(self, s3_path: str, course_name: str, **kwargs) -> str:
     try:
-      with NamedTemporaryFile() as tmpfile:
-        # download from S3 into pdf_tmpfile
-        self.s3_client.download_fileobj(Bucket=os.getenv('S3_BUCKET_NAME'), Key=s3_path, Fileobj=tmpfile)
+      import pysrt
 
-        loader = SRTLoader(tmpfile.name)
-        documents = loader.load()
+      # NOTE: slightly different method for .txt files, no need for download. It's part of the 'body'
+      response = self.s3_client.get_object(Bucket=os.environ['S3_BUCKET_NAME'], Key=s3_path)
+      raw_text = response['Body'].read().decode('utf-8')
 
-        texts = [doc.page_content for doc in documents]
-        metadatas: List[Dict[str, Any]] = [{
-            'course_name': course_name,
-            's3_path': s3_path,
-            'readable_filename': kwargs.get('readable_filename',
-                                            Path(s3_path).name[37:]),
-            'pagenumber': '',
-            'timestamp': '',
-            'url': '',
-            'base_url': '',
-        } for doc in documents]
+      print("UTF-8 text to ingest as SRT:", raw_text)
+      parsed_info = pysrt.from_string(raw_text)
+      text = " ".join([t.text for t in parsed_info])  # type: ignore
+      print(f"Final SRT ingest: {text}")
 
-        self.split_and_upload(texts=texts, metadatas=metadatas)
-        return "Success"
+      texts = [text]
+      metadatas: List[Dict[str, Any]] = [{
+          'course_name': course_name,
+          's3_path': s3_path,
+          'readable_filename': kwargs.get('readable_filename',
+                                          Path(s3_path).name[37:]),
+          'pagenumber': '',
+          'timestamp': '',
+          'url': '',
+          'base_url': '',
+      }]
+      if len(text) == 0:
+        return "Error: SRT file appears empty. Skipping."
+
+      self.split_and_upload(texts=texts, metadatas=metadatas)
+      return "Success"
     except Exception as e:
       err = f"❌❌ Error in (SRT ingest): `{inspect.currentframe().f_code.co_name}`: {e}\nTraceback:\n", traceback.format_exc(
       )