rag template

App/Chat/ChatRoutes.py

@@ -39,7 +39,6 @@ async def generate_message(
 ):
     entry: Transcriptions = await Transcriptions.objects.filter(task_id=task_id).first()
     result = BaseTranscription(**entry.__dict__)
-    print(result)
     text = ""
     for item in result.content:
         text += item["text"]

App/Chat/utils/RAG.py

@@ -27,11 +27,13 @@ class GenerativeAIAssistant:
         Template =f''' 
       #Instructions
       You are given the following context in yaml of a transcript of a youtube video, the start and end times are indicated and the text that was said is also given. You are also given a question, use the context to answer the question in a consise manner, make it short and to the point, don't provide additional details.
-       Make it short and to the point, not more than 1 PARAGRAPH. 
+     
 
       #Context
       {context_yaml}
 
+      #Your response be as short as possible and to the point.
+      
       #Question
       {question}
         '''

App/Worker.py

@@ -1,6 +1,7 @@
 from celery import Celery, chain
 import os
 import time
+import cgi
 from App import celery_config
 import yt_dlp
 import tempfile
@@ -16,6 +17,7 @@ celery.conf.update(
 
 @celery.task(name="embbeding", bind=True)
 def generate_store(self, data, task_id):
+    print('generating chunks')
     chunks = generateChunks(data, task_id)
     encode(chunks)
     print("hellooo")
@@ -30,10 +32,8 @@ def transcription_task(self, file_path, model_size="tiny"):
 
 @celery.task(name="download", bind=True)
 def downloadfile(self, url, ydl_opts, model_size="base"):
-    # updated
     self.update_state(state="Downloading File..", meta={})
 
-    ####
     with yt_dlp.YoutubeDL(ydl_opts) as ydl:
         ydl.download([url])
 
@@ -41,7 +41,7 @@ def downloadfile(self, url, ydl_opts, model_size="base"):
     self.update_state(state="Downloading complete", meta={})
     audio_file = ydl_opts["outtmpl"]
     print(model_size, "worker after")
-    # print(audio_file["default"])
+
     data = transcribe_file(
         state=self, file_path=audio_file["default"], model_size=model_size
     )