台州市城市建设规划局网站,网站分析 案例,牙医工具网站建设课程设计报告,wordpress图片美化文章目录 一.先利用langchain官方文档的AI功能问问二.langchain async api三.串行#xff0c;异步速度比较 一.先利用langchain官方文档的AI功能问问 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#xff0c;但是并非专门讲解异步的
二.langchain asy… 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 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 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(temperature0.9)prompt PromptTemplate(input_variables[product],templateWhat is a good name for a company that makes {product}?,)chain LLMChain(llmllm, promptprompt)for _ in range(5):resp chain.run(producttoothpaste)print(resp)async def async_generate(chain):resp await chain.arun(producttoothpaste)print(resp)async def generate_concurrently():llm OpenAI(temperature0.9)prompt PromptTemplate(input_variables[product],templateWhat is a good name for a company that makes {product}?,)chain LLMChain(llmllm, promptprompt)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 fConcurrent executed in {elapsed:0.2f} seconds. \033[0m)s time.perf_counter()
generate_serially()
elapsed time.perf_counter() - s
print(\033[1m fSerial 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(temperature0.9)prompt PromptTemplate(input_variables[product],templateWhat is a good name for a company that makes {product}?,)chain LLMChain(llmllm, promptprompt)for _ in range(5):resp chain.run(producttoothpaste)print(resp)async def async_generate(chain):resp await chain.arun(producttoothpaste)print(resp)async def generate_concurrently():llm OpenAI(temperature0.9)prompt PromptTemplate(input_variables[product],templateWhat is a good name for a company that makes {product}?,)chain LLMChain(llmllm, promptprompt)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 fConcurrent executed in {elapsed:0.2f} seconds. \033[0m)s time.perf_counter()generate_serially()elapsed time.perf_counter() - sprint(\033[1m fSerial executed in {elapsed:0.2f} seconds. \033[0m)asyncio.run(main()) 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(temperature0.9)# 创建包含10个async_generate任务的列表tasks [async_generate(llm, fFunction {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 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(temperature0.9)tasks [async_generate(llm, fFunction {i}) for i in range(3)]await asyncio.gather(*tasks)def generate_serially():llm OpenAI(temperature0.9)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 fConcurrent executed in {elapsed:0.2f} seconds. \033[0m)s time.perf_counter()generate_serially()elapsed time.perf_counter() - sprint(\033[1m fSerial executed in {elapsed:0.2f} seconds. \033[0m)asyncio.run(main())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blogPython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)sequential_run.py 就不跑了… 一天200次调用都快没了 主要是看看两者区别
