Spaces:
Running
Running
File size: 3,826 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
{
"cells": [
{
"cell_type": "markdown",
"id": "593f7553-7038-498e-96d4-8255e5ce34f0",
"metadata": {},
"source": [
"# 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](../indexes/chain_examples/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",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|