transcript_local.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from youtube_transcript_api import YouTubeTranscriptApi\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.indexes import  VectorstoreIndexCreator\n",
    "from langchain.document_loaders import TextLoader, DirectoryLoader\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.memory import VectorStoreRetrieverMemory\n",
    "from langchain.llms import GPT4All\n",
    "from langchain import ConversationChain, PromptTemplate\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory already exists\n"
     ]
    }
   ],
   "source": [
    "video_links = [\"9lVj_DZm36c\", \"ZUN3AFNiEgc\", \"8KtDLu4a-EM\"]\n",
    "\n",
    "if os.path.exists('transcripts'):\n",
    "    print('Directory already exists')\n",
    "else:\n",
    "    os.mkdir('transcripts')\n",
    "for video_id in video_links:\n",
    "    dir = os.path.join('transcripts', video_id)\n",
    "    transcript = YouTubeTranscriptApi.get_transcript(video_id)\n",
    "\n",
    "    with open(dir+'.txt', 'w') as f:\n",
    "     for line in transcript:\n",
    "            f.write(f\"{line['text']}\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = DirectoryLoader(path='./', glob = \"**/*.txt\", loader_cls=TextLoader,\n",
    "                         show_progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 230.76it/s]\n"
     ]
    }
   ],
   "source": [
    "index = VectorstoreIndexCreator(embedding=embeddings).from_loaders([loader])\n",
    "retriever = index.vectorstore.as_retriever(search_kwargs=dict(k=2))\n",
    "memory = VectorStoreRetrieverMemory(retriever=retriever)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found model file.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "llm = GPT4All(model=\"./ggml-mpt-7b-instruct.bin\", top_p=0.15, top_k=0,  temp=0.5, repeat_penalty=1.1, n_threads=12, n_batch=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "INSTRUCTION_KEY = \"### Instruction:\"\n",
    "RESPONSE_KEY = \"### Response:\"\n",
    "END_KEY = \"### End\"\n",
    "INTRO_BLURB = \"Below is an instruction that describes a task. Write a response that appropriately completes the request.\"\n",
    "TEMPLATE = \"\"\"{intro}\n",
    "{instruction_key}\n",
    "{instruction}\n",
    "{response_key}\n",
    "\"\"\".format(\n",
    "    intro=INTRO_BLURB,\n",
    "    instruction_key=INSTRUCTION_KEY,\n",
    "    instruction=\"{instruction}\",\n",
    "    response_key=RESPONSE_KEY,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "_DEFAULT_TEMPLATE = \"\"\"\n",
    "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "###Instruction: The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
    "Do not make up answers and provide only information that you have.\n",
    "Relevant pieces of previous conversation:\n",
    "{history}\n",
    "\n",
    "(You do not need to use these pieces of information if not relevant)\n",
    "{input}\n",
    "\n",
    "### Response:\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "PROMPT = PromptTemplate(\n",
    "    input_variables=[ \"history\", \"input\"], template=_DEFAULT_TEMPLATE\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Lenovo Slim 7i Pro X is powered by an Intel Core i7-1165G7 processor, with 8GB RAM and 512 GB SSD. It has a 15 inch display at 1920 x 1080 resolution (Full HD). The laptop weighs 2.3 pounds / 1 kg.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "conversation_with_summary = ConversationChain(\n",
    "    llm=llm, \n",
    "    prompt=PROMPT,\n",
    "    # We set a very low max_token_limit for the purposes of testing.\n",
    "    memory = memory\n",
    "    )\n",
    "\n",
    "\n",
    "with torch.inference_mode():\n",
    "\n",
    "   conversation_with_summary.predict(input = \"what are the specifications of lenovo 7i\")"
   ]
  }
 ],
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