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133 lines
3.7 KiB
Plaintext
133 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "593f7553-7038-498e-96d4-8255e5ce34f0",
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"metadata": {},
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"source": [
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"# Async API for Chain\n",
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"\n",
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"LangChain provides async support for Chains by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
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"\n",
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"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."
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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": 1,
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"id": "c19c736e-ca74-4726-bb77-0a849bcc2960",
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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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"BrightSmile Toothpaste Company\n",
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"\n",
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"\n",
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"BrightSmile Toothpaste Co.\n",
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"\n",
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"\n",
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"BrightSmile Toothpaste\n",
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"\n",
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"\n",
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"Gleaming Smile Inc.\n",
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"\n",
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"\n",
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"SparkleSmile Toothpaste\n",
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"\u001B[1mConcurrent executed in 1.54 seconds.\u001B[0m\n",
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"\n",
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"\n",
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"BrightSmile Toothpaste Co.\n",
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"\n",
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"\n",
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"MintyFresh Toothpaste Co.\n",
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"\n",
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"\n",
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"SparkleSmile Toothpaste.\n",
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"\n",
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"\n",
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"Pearly Whites Toothpaste Co.\n",
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"\n",
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"\n",
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"BrightSmile Toothpaste.\n",
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"\u001B[1mSerial executed in 6.38 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 asyncio\n",
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"import time\n",
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"\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import LLMChain\n",
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"\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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" prompt = PromptTemplate(\n",
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" input_variables=[\"product\"],\n",
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" template=\"What is a good name for a company that makes {product}?\",\n",
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" )\n",
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" chain = LLMChain(llm=llm, prompt=prompt)\n",
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" for _ in range(5):\n",
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" resp = chain.run(product=\"toothpaste\")\n",
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" print(resp)\n",
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"\n",
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"\n",
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"async def async_generate(chain):\n",
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" resp = await chain.arun(product=\"toothpaste\")\n",
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" print(resp)\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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" prompt = PromptTemplate(\n",
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" input_variables=[\"product\"],\n",
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" template=\"What is a good name for a company that makes {product}?\",\n",
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" )\n",
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" chain = LLMChain(llm=llm, prompt=prompt)\n",
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" tasks = [async_generate(chain) for _ in range(5)]\n",
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" await asyncio.gather(*tasks)\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": "3.9.1"
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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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