快速入门指南：如何在华为云平台modelarts 快速使用 langchain 构建端到端语言模型应用程序-yb体育官方

1.登录：

点击右上角“控制台”，搜索栏输入“modelarts”

点击“开发环境”-“notebook”,“创建”：

进入创建notebook，名称“notebook-langchain”,选择gpu规格，“gpu: 1*t4(16gb)|cpu: 8核 32gb”，点击“立即创建”，磁盘规格选择“50g”,点击“创建”

点击返回“任务中心”，点击notebook进入

 首先，安装 langchain。只需运行以下命令：

!pip install langchain

looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple
collecting langchain
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/1f/fd/f2aa39f8e63a6fbacf2e7be820b846c27b1e5830af9c2e2e208801b6c07f/langchain-0.0.27-py3-none-any.whl (124 kb)
     |████████████████████████████████| 124 kb 49.0 mb/s eta 0:00:01
collecting sqlalchemy
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/ac/d8/51e617a1eb143a48ab2dceb194afe40b3c42b785723a031cc29a8c04103d/sqlalchemy-2.0.20-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 mb)
     |████████████████████████████████| 2.9 mb 30.8 mb/s eta 0:00:01
requirement already satisfied: numpy in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from langchain) (1.19.5)
requirement already satisfied: pyyaml in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from langchain) (5.1)
requirement already satisfied: requests in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from langchain) (2.27.1)
collecting pydantic
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/fd/35/86b1e7571e695587df0ddf2937100436dce0caa277d2f016d4e4f7d3791a/pydantic-2.2.1-py3-none-any.whl (373 kb)
     |████████████████████████████████| 373 kb 55.0 mb/s eta 0:00:01
collecting typing-extensions>=4.6.1
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/38/60/300ad6f93adca578bf05d5f6cd1d854b7d140bebe2f9829561aa9977d9f3/typing_extensions-4.6.2-py3-none-any.whl (31 kb)
collecting pydantic-core==2.6.1
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/c0/ca/4cf24afe80f5839a5cad5e35e2a0a11fe41b0f4f6a544109f73337567579/pydantic_core-2.6.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 mb)
     |████████████████████████████████| 1.9 mb 38.3 mb/s eta 0:00:01
collecting annotated-types>=0.4.0
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/d8/f0/a2ee543a96cc624c35a9086f39b1ed2aa403c6d355dfe47a11ee5c64a164/annotated_types-0.5.0-py3-none-any.whl (11 kb)
requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from requests->langchain) (1.26.12)
requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from requests->langchain) (2022.9.24)
requirement already satisfied: charset-normalizer~=2.0.0 in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from requests->langchain) (2.0.12)
requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/pytorch-1.8/lib/python3.7/site-packages (from requests->langchain) (3.4)
collecting greenlet!=0.4.17
  downloading http://repo.myhuaweicloud.com/repository/pypi/packages/1a/ed/72998fb3609f6c4b0817df32e2b98a88bb8510613d12d495bbab8534ebd0/greenlet-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (514 kb)
     |████████████████████████████████| 514 kb 12.0 mb/s eta 0:00:01

import os 
os .environ[ "openai_api_key" ] = "..."

• 构建语言模型应用程序：llm

  安装好 langchain 并设置好环境后，我们就可以开始构建我们的语言模型应用程序了。langchain 提供了一堆模块，您可以使用它们来创建语言模型应用程序。您可以将这些模块组合起来用于更复杂的应用程序，或者将它们单独用于更简单的应用程序。

• 构建语言模型应用程序：chat model

  除了 llm，您还可以使用聊天模型。这些是语言模型的变体，它们在底层使用语言模型但具有不同的界面。聊天模型使用聊天消息作为输入和输出，而不是“文本输入、文本输出”api。聊天模型 api 的使用还比较新，所以大家都还在寻找最佳抽象使用方式。

要完成聊天，您需要将一条或多条消息传递给聊天模型。langchain 目前支持 aimessage、humanmessage、systemmessage 和 chatmessage 类型。您将主要使用 humanmessage、aimessage 和 systemmessage。 下面是使用聊天模型的示例：

from langchain.chat_models import chatopenai
from langchain.schema import (
    aimessage,
    humanmessage,
    systemmessage
)
chat = chatopenai(temperature=0)

您可以通过传递一条消息来完成:

chat([humanmessage(content="translate this sentence from english to french. i love programming.")])
# -> aimessage(content="j'aime programmer.", additional_kwargs={})

messages = [
    systemmessage(content="you are a helpful assistant that translates english to chinese."),
    humanmessage(content="translate this sentence from english to chinese. i love programming.")
]
chat(messages)
# -> aimessage(content="我喜欢编程。(wǒ xǐhuān biānchéng.)", additional_kwargs={})

batch_messages = [
    [
        systemmessage(content="you are a helpful assistant that translates english to chinese."),
        humanmessage(content="translate this sentence from english to chinese. i love programming.")
    ],
    [
        systemmessage(content="you are a helpful assistant that translates english to chinese."),
        humanmessage(content="translate this sentence from english to chinese. i love artificial intelligence.")
    ],
]
result = chat.generate(batch_messages)
result
# -> llmresult(generations=[[chatgeneration(text="我喜欢编程。(wǒ xǐhuān biānchéng.)", generation_info=none, message=aimessage(content="我喜欢编程。(wǒ xǐhuān biānchéng.)", additional_kwargs={}))], [chatgeneration(text="我喜爱人工智能。(wǒ xǐ'ài rén gōng zhì néng.)", generation_info=none, message=aimessage(content="我喜爱人工智能。(wǒ xǐ'ài rén gōng zhì néng.)", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})

result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}

from langchain.chat_models import chatopenai
from langchain.prompts.chat import (
    chatprompttemplate,
    systemmessageprompttemplate,
    humanmessageprompttemplate,
)
chat = chatopenai(temperature=0)
template="you are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = systemmessageprompttemplate.from_template(template)
human_template="{text}"
human_message_prompt = humanmessageprompttemplate.from_template(human_template)
chat_prompt = chatprompttemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="english", output_language="chinese", text="i love programming.").to_messages())
# -> aimessage(content="我喜欢编程。(wǒ xǐhuān biānchéng.)", additional_kwargs={})

from langchain.chat_models import chatopenai
from langchain import llmchain
from langchain.prompts.chat import (
    chatprompttemplate,
    systemmessageprompttemplate,
    humanmessageprompttemplate,
)
chat = chatopenai(temperature=0)
template="you are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = systemmessageprompttemplate.from_template(template)
human_template="{text}"
human_message_prompt = humanmessageprompttemplate.from_template(human_template)
chat_prompt = chatprompttemplate.from_messages([system_message_prompt, human_message_prompt])
chain = llmchain(llm=chat, prompt=chat_prompt)
chain.run(input_language="english", output_language="chinese", text="i love programming.")
# -> "我喜欢编程。(wǒ xǐhuān biānchéng.)"

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import agenttype
from langchain.chat_models import chatopenai
from langchain.llms import openai
# first, let's load the language model we're going to use to control the agent.
chat = chatopenai(temperature=0)
# next, let's load some tools to use. note that the `llm-math` tool uses an llm, so we need to pass that in.
llm = openai(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent=agenttype.chat_zero_shot_react_description, verbose=true)
# now let's test it out!
agent.run("who is olivia wilde's boyfriend? what is his current age raised to the 0.23 power?")

from langchain.prompts import (
    chatprompttemplate, 
    messagesplaceholder, 
    systemmessageprompttemplate, 
    humanmessageprompttemplate
)
from langchain.chains import conversationchain
from langchain.chat_models import chatopenai
from langchain.memory import conversationbuffermemory
prompt = chatprompttemplate.from_messages([
    systemmessageprompttemplate.from_template("the following is a friendly conversation between a human and an ai. the ai is talkative and provides lots of specific details from its context. if the ai does not know the answer to a question, it truthfully says it does not know."),
    messagesplaceholder(variable_name="history"),
    humanmessageprompttemplate.from_template("{input}")
])
llm = chatopenai(temperature=0)
memory = conversationbuffermemory(return_messages=true)
conversation = conversationchain(memory=memory, prompt=prompt, llm=llm)
conversation.predict(input="hi there!")
# -> 'hello! how can i assist you today?'
conversation.predict(input="i'm doing well! just having a conversation with an ai.")
# -> "that sounds like fun! i'm happy to chat with you. is there anything specific you'd like to talk about?"
conversation.predict(input="tell me about yourself.")
# -> "sure! i am an ai language model created by openai. i was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. i can answer questions, provide information, and even have conversations like this one. is there anything else you'd like to know about me?"