前期准备：

1.登录华为云官方账号：

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

1. 安装LangChain

 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

2.环境设置

在 Jupyter notebook 或 Python 脚本中工作，您可以像这样设置环境变量:

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?"