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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question answering over a group chat messages\n",
    "In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to semantically search and ask questions over a group chat.\n",
    "\n",
    "View a working demo [here](https://twitter.com/thisissukh_/status/1647223328363679745)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python3 -m pip install --upgrade langchain deeplake openai tiktoken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Add API keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import getpass\n",
    "from langchain.document_loaders import PyPDFLoader, TextLoader\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import (\n",
    "    RecursiveCharacterTextSplitter,\n",
    "    CharacterTextSplitter,\n",
    ")\n",
    "from langchain.vectorstores import DeepLake\n",
    "from langchain.chains import ConversationalRetrievalChain, RetrievalQA\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.llms import OpenAI\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
    "os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")\n",
    "os.environ[\"ACTIVELOOP_ORG\"] = getpass.getpass(\"Activeloop Org:\")\n",
    "\n",
    "org = os.environ[\"ACTIVELOOP_ORG\"]\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "dataset_path = \"hub://\" + org + \"/data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 2. Create sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can generate a sample group chat conversation using ChatGPT with this prompt:\n",
    "\n",
    "```\n",
    "Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.\n",
    "```\n",
    "\n",
    "I've already generated such a chat in `messages.txt`. We can keep it simple and use this for our example.\n",
    "\n",
    "## 3. Ingest chat embeddings\n",
    "\n",
    "We load the messages in the text file, chunk and upload to ActiveLoop Vector store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"messages.txt\") as f:\n",
    "    state_of_the_union = f.read()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "pages = text_splitter.split_text(state_of_the_union)\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
    "texts = text_splitter.create_documents(pages)\n",
    "\n",
    "print(texts)\n",
    "\n",
    "dataset_path = \"hub://\" + org + \"/data\"\n",
    "embeddings = OpenAIEmbeddings()\n",
    "db = DeepLake.from_documents(\n",
    "    texts, embeddings, dataset_path=dataset_path, overwrite=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Ask questions\n",
    "\n",
    "Now we can ask a question and get an answer back with a semantic search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)\n",
    "\n",
    "retriever = db.as_retriever()\n",
    "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
    "retriever.search_kwargs[\"k\"] = 4\n",
    "\n",
    "qa = RetrievalQA.from_chain_type(\n",
    "    llm=OpenAI(), chain_type=\"stuff\", retriever=retriever, return_source_documents=False\n",
    ")\n",
    "\n",
    "# What was the restaurant the group was talking about called?\n",
    "query = input(\"Enter query:\")\n",
    "\n",
    "# The Hungry Lobster\n",
    "ans = qa({\"query\": query})\n",
    "\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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