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151 lines
4.1 KiB
Plaintext
151 lines
4.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f6574496-b360-4ffa-9523-7fd34a590164",
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"metadata": {},
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"source": [
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"# Async API for LLM\n",
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"\n",
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"LangChain provides async support for LLMs by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
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"\n",
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"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",
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"\n",
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"You can use the `agenerate` method to call an OpenAI LLM asynchronously."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5e49e96c-0f88-466d-b3d3-ea0966bdf19e",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"I'm doing well. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"I am doing quite well. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing great, thank you! How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thanks for asking. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\u001b[1mConcurrent executed in 1.93 seconds.\u001b[0m\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing well, thank you. How about you?\n",
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"\n",
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"\n",
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"I'm doing great, thank you. How about you?\n",
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"\u001b[1mSerial executed in 10.54 seconds.\u001b[0m\n"
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]
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}
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],
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"source": [
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"import time\n",
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"import asyncio\n",
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"\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"def generate_serially():\n",
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" llm = OpenAI(temperature=0.9)\n",
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" for _ in range(10):\n",
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" resp = llm.generate([\"Hello, how are you?\"])\n",
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" print(resp.generations[0][0].text)\n",
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"\n",
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"\n",
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"async def async_generate(llm):\n",
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" resp = await llm.agenerate([\"Hello, how are you?\"])\n",
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" print(resp.generations[0][0].text)\n",
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"\n",
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"\n",
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"async def generate_concurrently():\n",
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" llm = OpenAI(temperature=0.9)\n",
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" tasks = [async_generate(llm) for _ in range(10)]\n",
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" await asyncio.gather(*tasks)\n",
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"\n",
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"\n",
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"s = time.perf_counter()\n",
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"# If running this outside of Jupyter, use asyncio.run(generate_concurrently())\n",
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"await generate_concurrently() \n",
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"elapsed = time.perf_counter() - s\n",
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"print('\\033[1m' + f\"Concurrent executed in {elapsed:0.2f} seconds.\" + '\\033[0m')\n",
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"\n",
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"s = time.perf_counter()\n",
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"generate_serially()\n",
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"elapsed = time.perf_counter() - s\n",
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"print('\\033[1m' + f\"Serial executed in {elapsed:0.2f} seconds.\" + '\\033[0m')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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