mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
133 lines
3.7 KiB
Plaintext
133 lines
3.7 KiB
Plaintext
{
|
|
"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
|
|
}
|