langchain/docs/modules/llms/async_llm.ipynb

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    "# Async API for LLM\n",
    "\n",
    "LangChain provides async support for LLMs by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
    "\n",
    "Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, only `OpenAI` is supported, but async support for other LLMs is on the roadmap.\n",
    "\n",
    "You can use the `agenerate` method to call an OpenAI LLM asynchronously."
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      "\n",
      "\n",
      "I'm doing well. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "I am doing quite well. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing great, thank you! How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thanks for asking. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\u001b[1mConcurrent executed in 1.93 seconds.\u001b[0m\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing well, thank you. How about you?\n",
      "\n",
      "\n",
      "I'm doing great, thank you. How about you?\n",
      "\u001b[1mSerial executed in 10.54 seconds.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import asyncio\n",
    "\n",
    "from langchain.llms import OpenAI\n",
    "\n",
    "def generate_serially():\n",
    "    llm = OpenAI(temperature=0.9)\n",
    "    for _ in range(10):\n",
    "        resp = llm.generate([\"Hello, how are you?\"])\n",
    "        print(resp.generations[0][0].text)\n",
    "\n",
    "\n",
    "async def async_generate(llm):\n",
    "    resp = await llm.agenerate([\"Hello, how are you?\"])\n",
    "    print(resp.generations[0][0].text)\n",
    "\n",
    "\n",
    "async def generate_concurrently():\n",
    "    llm = OpenAI(temperature=0.9)\n",
    "    tasks = [async_generate(llm) for _ in range(10)]\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')"
   ]
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