{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "593f7553-7038-498e-96d4-8255e5ce34f0",
   "metadata": {},
   "source": [
    "# Async API\n",
    "\n",
    "LangChain provides async support for Chains by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
    "\n",
    "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`), `ChatVectorDBChain`, and [QA chains](/docs/modules/chains/additional/question_answering.html). Async support for other chains is on the roadmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c19c736e-ca74-4726-bb77-0a849bcc2960",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BrightSmile Toothpaste Company\n",
      "\n",
      "\n",
      "BrightSmile Toothpaste Co.\n",
      "\n",
      "\n",
      "BrightSmile Toothpaste\n",
      "\n",
      "\n",
      "Gleaming Smile Inc.\n",
      "\n",
      "\n",
      "SparkleSmile Toothpaste\n",
      "\u001b[1mConcurrent executed in 1.54 seconds.\u001b[0m\n",
      "\n",
      "\n",
      "BrightSmile Toothpaste Co.\n",
      "\n",
      "\n",
      "MintyFresh Toothpaste Co.\n",
      "\n",
      "\n",
      "SparkleSmile Toothpaste.\n",
      "\n",
      "\n",
      "Pearly Whites Toothpaste Co.\n",
      "\n",
      "\n",
      "BrightSmile Toothpaste.\n",
      "\u001b[1mSerial executed in 6.38 seconds.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import asyncio\n",
    "import time\n",
    "\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.chains import LLMChain\n",
    "\n",
    "\n",
    "def generate_serially():\n",
    "    llm = OpenAI(temperature=0.9)\n",
    "    prompt = PromptTemplate(\n",
    "        input_variables=[\"product\"],\n",
    "        template=\"What is a good name for a company that makes {product}?\",\n",
    "    )\n",
    "    chain = LLMChain(llm=llm, prompt=prompt)\n",
    "    for _ in range(5):\n",
    "        resp = chain.run(product=\"toothpaste\")\n",
    "        print(resp)\n",
    "\n",
    "\n",
    "async def async_generate(chain):\n",
    "    resp = await chain.arun(product=\"toothpaste\")\n",
    "    print(resp)\n",
    "\n",
    "\n",
    "async def generate_concurrently():\n",
    "    llm = OpenAI(temperature=0.9)\n",
    "    prompt = PromptTemplate(\n",
    "        input_variables=[\"product\"],\n",
    "        template=\"What is a good name for a company that makes {product}?\",\n",
    "    )\n",
    "    chain = LLMChain(llm=llm, prompt=prompt)\n",
    "    tasks = [async_generate(chain) for _ in range(5)]\n",
    "    await asyncio.gather(*tasks)\n",
    "\n",
    "\n",
    "s = time.perf_counter()\n",
    "# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
    "await generate_concurrently()\n",
    "elapsed = time.perf_counter() - s\n",
    "print(\"\\033[1m\" + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")\n",
    "\n",
    "s = time.perf_counter()\n",
    "generate_serially()\n",
    "elapsed = time.perf_counter() - s\n",
    "print(\"\\033[1m\" + f\"Serial executed in {elapsed:0.2f} seconds.\" + \"\\033[0m\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}