当前位置：首页 > news >正文

异步使用langchain

news 2025/6/26 12:11:23

文章目录

一.先利用langchain官方文档的AI功能问问
二.langchain async api
三.串行，异步速度比较

一.先利用langchain官方文档的AI功能问问

在这里插入图片描述

然后看他给的 Verified Sources
这个页面里面虽然有些函数是异步函数，但是并非专门讲解异步的

二.langchain async api

还不如直接谷歌搜😂 一下搜到，上面那个AI文档问答没给出这个链接

在这里插入图片描述

官方示例

import asyncio
import timefrom langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChaindef generate_serially():llm = OpenAI(temperature=0.9)prompt = PromptTemplate(input_variables=["product"],template="What is a good name for a company that makes {product}?",)chain = LLMChain(llm=llm, prompt=prompt)for _ in range(5):resp = chain.run(product="toothpaste")print(resp)async def async_generate(chain):resp = await chain.arun(product="toothpaste")print(resp)async def generate_concurrently():llm = OpenAI(temperature=0.9)prompt = PromptTemplate(input_variables=["product"],template="What is a good name for a company that makes {product}?",)chain = LLMChain(llm=llm, prompt=prompt)tasks = [async_generate(chain) for _ in range(5)]await asyncio.gather(*tasks)s = time.perf_counter()
# If running this outside of Jupyter, use asyncio.run(generate_concurrently())
await generate_concurrently()
elapsed = time.perf_counter() - s
print("\033[1m" + f"Concurrent executed in {elapsed:0.2f} seconds." + "\033[0m")s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print("\033[1m" + f"Serial executed in {elapsed:0.2f} seconds." + "\033[0m")

不过官方代码报错了

我让copilot修改一下，能跑了

import time
import asyncio
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChaindef generate_serially():llm = OpenAI(temperature=0.9)prompt = PromptTemplate(input_variables=["product"],template="What is a good name for a company that makes {product}?",)chain = LLMChain(llm=llm, prompt=prompt)for _ in range(5):resp = chain.run(product="toothpaste")print(resp)async def async_generate(chain):resp = await chain.arun(product="toothpaste")print(resp)async def generate_concurrently():llm = OpenAI(temperature=0.9)prompt = PromptTemplate(input_variables=["product"],template="What is a good name for a company that makes {product}?",)chain = LLMChain(llm=llm, prompt=prompt)tasks = [async_generate(chain) for _ in range(5)]await asyncio.gather(*tasks)async def main():s = time.perf_counter()await generate_concurrently()elapsed = time.perf_counter() - sprint("\033[1m" + f"Concurrent executed in {elapsed:0.2f} seconds." + "\033[0m")s = time.perf_counter()generate_serially()elapsed = time.perf_counter() - sprint("\033[1m" + f"Serial executed in {elapsed:0.2f} seconds." + "\033[0m")asyncio.run(main())

在这里插入图片描述

这还有一篇官方blog

三.串行，异步速度比较

先学习一下掘金上看到的一篇：https://juejin.cn/post/7231907374688436284
为了更方便的看到异步效果，我在原博主的基础上，print里面加了一个提示

# 引入time和asyncio模块
import time
import asyncio
# 引入OpenAI类
from langchain.llms import OpenAI# 定义异步函数async_generate，该函数接收一个llm参数和一个name参数
async def async_generate(llm, name):# 调用OpenAI类的agenerate方法，传入字符串列表["Hello, how are you?"]并等待响应resp = await llm.agenerate(["Hello, how are you?"])# 打印响应结果的生成文本和函数名print(f"{name}: {resp.generations[0][0].text}")# 定义异步函数generate_concurrently
async def generate_concurrently():# 创建OpenAI实例，并设置temperature参数为0.9llm = OpenAI(temperature=0.9)# 创建包含10个async_generate任务的列表tasks = [async_generate(llm, f"Function {i}") for i in range(10)]# 并发执行任务await asyncio.gather(*tasks)# 主函数
# 如果在Jupyter Notebook环境运行该代码，则无需手动调用await generate_concurrently()，直接在下方执行单元格即可执行该函数
# 如果在命令行或其他环境下运行该代码，则需要手动调用asyncio.run(generate_concurrently())来执行该函数
asyncio.run(generate_concurrently())

免费用户一分钟只能3次，实在是有点难蚌

在这里插入图片描述

整合一下博主的代码，对两个速度进行比较，但是这个调用限制真的很搞人啊啊啊

import time
import asyncio
from langchain.llms import OpenAIasync def async_generate(llm, name):resp = await llm.agenerate(["Hello, how are you?"])# print(f"{name}: {resp.generations[0][0].text}")async def generate_concurrently():llm = OpenAI(temperature=0.9)tasks = [async_generate(llm, f"Function {i}") for i in range(3)]await asyncio.gather(*tasks)def generate_serially():llm = OpenAI(temperature=0.9)for _ in range(3):resp = llm.generate(["Hello, how are you?"])# print(resp.generations[0][0].text)async def main():s = time.perf_counter()await generate_concurrently()elapsed = time.perf_counter() - sprint("\033[1m" + f"Concurrent executed in {elapsed:0.2f} seconds." + "\033[0m")s = time.perf_counter()generate_serially()elapsed = time.perf_counter() - sprint("\033[1m" + f"Serial executed in {elapsed:0.2f} seconds." + "\033[0m")asyncio.run(main())

在这里插入图片描述

再看一篇blog
- 作者将代码开源在这里了：https://github.com/gabrielcassimiro17/async-langchain
- 测试一下它的async_chain.py文件

读取csv的时候路径一直报错，还好不久前总结了一篇blog：Python中如何获取各种目录路径

直接获取当前脚本路径了

import os
import pandas as pd# Get the directory where the script is located
script_directory = os.path.dirname(os.path.abspath(__file__))# Construct the path to the CSV file
csv_path = os.path.join(script_directory, 'wine_subset.csv')# Read the CSV file
df = pd.read_csv(csv_path)