langchain/docs/modules/chains/async_chain.ipynb

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    "# Async API for Chain\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](https://langchain.readthedocs.io/en/latest/modules/chains/combine_docs_examples/question_answering.html). Async support for other chains is on the roadmap."
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      "\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",
    "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')"
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