Chatbot- Custom knowledge using ChatGPT & LlamaIndex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['OPENAI_API_KEY'] = #Removing the key for security concerns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hamza\\anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\hamza\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "C:\\Users\\hamza\\anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas64__v0.3.21-gcc_10_3_0.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "from llama_index import LLMPredictor,PromptHelper,GPTVectorStoreIndex\n",
    "from langchain import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index import SimpleDirectoryReader\n",
    "documents = SimpleDirectoryReader('data1', recursive=True).load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = GPTVectorStoreIndex.from_documents(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\"What do u think of Facebook's LLaMa model?\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing LLM for different outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define LLM\n",
    "llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.1, model_name=\"text-davinci-002\"))\n",
    "\n",
    "# define prompt helper\n",
    "# set maximum input size\n",
    "max_input_size = 4096\n",
    "# set number of output tokens\n",
    "num_output = 256\n",
    "# set maximum chunk overlap\n",
    "max_chunk_overlap = 20\n",
    "prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)\n",
    "\n",
    "custom_LLM_index = GPTVectorStoreIndex.from_documents(\n",
    "    documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\"Facebook's LLaMa model?\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wikipedia Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index import download_loader\n",
    "\n",
    "WikipediaReader = download_loader(\"WikipediaReader\")\n",
    "\n",
    "loader = WikipediaReader()\n",
    "wikidocs = loader.load_data(pages=[\"Cyclone Freddy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_index = GPTVectorStoreIndex.from_documents(wikidocs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cyclone Freddy is a very intense tropical cyclone that traversed the southern Indian Ocean for more than five weeks in February and March 2023. It is the third-deadliest tropical cyclone recorded in the Southern Hemisphere, only behind 2019's Cyclone Idai and the 1973 Flores cyclone. It is both the longest-lasting and highest-ACE-producing tropical cyclone ever recorded worldwide.\n"
     ]
    }
   ],
   "source": [
    "query_engine = wiki_index.as_query_engine()\n",
    "response = query_engine.query(\"what is Cyclone Freddy\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Youtube Transcript Reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index import download_loader\n",
    "\n",
    "YoutubeTranscriptReader = download_loader(\"YoutubeTranscriptReader\")\n",
    "\n",
    "loader = YoutubeTranscriptReader()\n",
    "ytdocs = loader.load_data(ytlinks=['https://www.youtube.com/watch?v=VyWAvY2CF9c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yt_index = GPTVectorStoreIndex.from_documents(ytdocs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Deep learning is a machine learning technique that learns features and tasks directly from data by running inputs through a biologically inspired neural network architecture. These neural networks contain a number of hidden layers through which data is processed, allowing for the machine to go deep in its learning, making connections and weighing input for the best results.\n"
     ]
    }
   ],
   "source": [
    "query_engine = yt_index.as_query_engine()\n",
    "response = query_engine.query(\"what is Deep Learning?\")\n",
    "print(response)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}