LangChain 速成课程：构建基于 OpenAI LLM 的应用

LangChain 速成课程：构建基于 OpenAI LLM 的应用 | 极客日志

特点	描述
基于语言模型	LangChain 是一个专为语言模型（如 GPT-4）设计的开发框架。
模型输入/输出	支持灵活的模型输入和输出处理，可以适应各种不同的应用需求。
数据感知	能够将语言模型与其他数据源（如维基百科、个人文件等）连接，提供更丰富的信息和功能。
代理交互	允许语言模型与其运行环境（如网络搜索、电子邮件等）进行交互，实现更复杂和动态的响应。
开发 AI 应用	综合以上特点，开发者可以利用高级语言模型与外部数据和环境结合，开发多样的 AI 应用程序。

特点	描述
基础功能	LangChain 是专为基于语言模型的应用程序开发设计的框架。
数据感知能力	可以将语言模型连接到各种外部数据源，如个人文件、实时互联网数据，以及整合维基百科和谷歌等来源的信息。
代理性	除了数据连接，还允许语言模型与运行环境（如网络、电子邮件等）进行交互。
行动能力	LangChain 的代理可以决定如何执行各种操作，不仅能提供信息，还能执行复杂任务如运行 Python 代码。
无缝集成	允许开发者轻松地将高级语言模型（如 GPT-4）与首选的数据源和环境连接起来。
多功能应用	综合以上特点，可用于开发一系列复杂和多功能的 AI 应用程序。

步骤	描述
文本向量化	输入文本（如问答、文章段落等）被转换为数值向量，通常通过词嵌入或句子嵌入等机器学习技术完成。
向量存储	转换后的数值向量被存储在专用的向量数据库或存储空间，以便于后续快速检索和对比。
用户查询处理	当用户发出查询或搜索时，该文本也被转换为数值向量，通常使用与文本向量化相同的机器学习技术。
相似性搜索	用户查询的数值向量与数据库中的向量进行相似性对比，常使用方法如余弦相似度。
检索信息	数据库识别与查询最相似的向量，并返回这些向量对应的原始文本，以提供与查询最相关的信息或答案。
生成 LLM 的完成	在特定应用场景下，找到最相似的文本向量后，可以通过语言模型（如 LLM）生成更完整或更具解释性的回应。

应用领域	核心功能	主要特点
YouTube 剧本生成器	利用像 GPT-4 这样的语言模型生成 YouTube 视频剧本。	- 用户定义的关键词或主题 - 结构化剧本（引言、正文、结论） - 包括视频元素如时间戳和字幕
网络研究工具	处理和总结大量的在线文本数据。	- 处理新闻文章、研究论文和书籍 - 快速获取特定主题的概览
文本总结器	作为一个高级的文本总结工具。	- 从多个数据源收集信息 - 生成一个精炼的总结
问答系统	基于上传的文档构建一个问答系统。	- 允许上传文档、PDF 或书籍 - 生成一个知识库 - 用户可以提出问题并从知识库中获取相应的答案

import os
from apikey import apikey

import streamlit as st
from langchain.llms import OpenAI

os.environ["OPENAI_API_KEY"] = apikey

st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')

import os
from apikey import apikey

import streamlit as st
from langchain.llms import OpenAI

os.environ["OPENAI_API_KEY"] = apikey

st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')

# 新增
llm = OpenAI(temperature=0.9)
if topic:
    response = llm(topic)
    st.write(response)

import os
from apikey import apikey
import streamlit as st
from langchain.llms import OpenAI

os.environ["OPENAI_API_KEY"] = apikey
st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')

# 从 LangChain 导入提示模板
from langchain.prompts import PromptTemplate
# 创建一个 Prompt Template 实例
# PromptTemplate 将接受输入变量，在 input_variables 数组中指定了主题和语言作为输入变量
title_template = PromptTemplate(
    input_variables=['topic', 'language'],
    template='Give me medium article title on {topic} in {language}'
)

llm = OpenAI(temperature=0.9)
if topic:
    response = llm(title_template.format(topic=topic, language='english'))
    st.write(response)

import os
from apikey import apikey
import streamlit as st
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

os.environ["OPENAI_API_KEY"] = apikey
st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')
language = st.text_input('Input language')
title_template = PromptTemplate(
    input_variables=['topic', 'language'],
    template='Give me medium article title on {topic} in {language}'
)
llm = OpenAI(temperature=0.9)
from langchain.chains import LLMChain
# verbose=True 会详细输出过程
title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True)
if topic:
    # 我们的标题模板需要两个输入变量，所以我们使用了一个字典。
    response = title_chain.run({'topic': topic, 'language': language})
    st.write(response)

> Entering new LLMChain chain...
Prompt after formatting:
Give me medium article title on investing in english

> Finished chain.

import os
from apikey import apikey
import streamlit as st
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

os.environ["OPENAI_API_KEY"] = apikey
st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')
title_template = PromptTemplate(
    input_variables=['topic'],
    template='Give me medium article title on {topic}'
)
llm = OpenAI(temperature=0.9)
from langchain.chains import LLMChain
# verbose=True 会详细输出过程
title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True)
if topic:
    # 一个参数只需要一个字符串变量
    response = title_chain.run(topic)
    st.write(response)

import os
from apikey import apikey
import streamlit as st
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain

os.environ ["OPENAI_API_KEY"] = apikey
st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')
title_template = PromptTemplate(
    input_variables = ['topic'],
    template = 'Give me medium article title on {topic}'
)
article_template = PromptTemplate(
    input_variables = ['title'],
    template = 'Give me medium article for {title}'
)
llm = OpenAI(temperature=0.9)
title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True)
article_chain = LLMChain(llm=llm, prompt=article_template, verbose=True)

if topic:
    # 首先，根据主题生成标题。
    title_response = title_chain.run({'topic': topic})
    generated_title = title_response if isinstance(title_response, str) else title_response.get('content', '')

    if generated_title:
        # 然后，根据生成的标题生成文章内容。
        article_response = article_chain.run({'title': generated_title})
        st.write(article_response if isinstance(article_response, str) else article_response.get('content', ''))

import os
from apikey import apikey
import streamlit as st
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain

os.environ ["OPENAI_API_KEY"] = apikey
st.title('Medium Article Generator')
topic = st.text_input('Input your topic of interest')
title_template = PromptTemplate(
    input_variables = ['topic'],
    template = 'Give me medium article title on {topic}'
)
article_template = PromptTemplate(
    input_variables = ['title'],
    template = 'Give me medium article for {title}'
)
llm = OpenAI(temperature=0.9)
title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True)

llm2 = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.9)
article_chain = LLMChain(llm=llm2, prompt=article_template, verbose=True)

overall_chain =SimpleSequentialChain(chains=[title_chain, article_chain], verbose=True)
if topic:
    response = overall_chain.run(topic)
    st.write(response)

> Entering new LLMChain chain...Prompt after formatting:Give me medium article for "5 Strategies for Investing Wisely in Today's Markets"> Finished chain.I'm sorry, but I cannot provide you with a specific Medium article as I am an AI language model and do not have direct access to the internet or a database of articles. However, I can offer you a summary of five strategies for investing wisely in today's markets:1. Diversify your portfolio: Diversification involves spreading your investments across different asset classes, sectors, and regions. By diversifying, you reduce the risk of one particular investment negatively impacting your overall portfolio. This strategy allows you to potentially earn profits while minimizing potential losses.2. Conduct thorough research: Knowledge is key when it comes to investing wisely. Before investing, take the time to do thorough research on the companies or assets you plan to invest in. Analyze their financials, industry trends, and competitive advantages. Additionally, stay updated on market news and trends to make informed decisions.3. Set clear investment goals: Define your investment goals and time horizon. Are you investing for short-term gains or long-term wealth accumulation? By setting clear goals, you can tailor your investment strategy accordingly and avoid making impulsive decisions based on short-term market fluctuations.4. Have a long-term perspective: Investing wisely often involves a long-term perspective. Trying to time the market and make short-term gains can be speculative and risky. Instead, focus on investing in solid companies or assets that have long-term growth potential. Warren Buffett famously said, "The stock market is a device for transferring money from the impatient to the patient."5. Control your emotions: Investing in volatile markets can be nerve-wracking, leading to emotional decision making. It's crucial to control your emotions and avoid making impulsive investment choices based on fear or greed. Develop a disciplined approach, stick to your investment plan, and avoid being swayed by short-term market sentiment.Remember, investing always carries some level of risk, and it's advisable to consult with a financial advisor or conduct further research before making any investment decisions.> Finished chain.

import os
from apikey import apikey
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType

os.environ["OPENAI_API_KEY"] = apikey
# 我们将温度设为 0，因为我们希望得到一个没有幻觉的客观研究工具
llm = OpenAI(temperature=0.0)
tools = load_tools(['wikipedia', 'llm-math'], llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
prompt = input('Input Wikipedia Research Task\n')
agent.run(prompt)

pip install chromadb

import os
from apikey import apikey
import streamlit as st # 用于创建我们的 UI 前端
from langchain.chat_models import ChatOpenAI # 用于 GPT3.5/4 模型
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA

os.environ["OPENAI_API_KEY"] = apikey
st.title('Chat with Document') # 我们网页的标题
loader = TextLoader('./constitution.txt') # 加载文本文档
documents = loader.load()
print(documents) # 打印以确保文档正确加载

os.environ["OPENAI_API_KEY"] = apikey
st.title('Chat with Document') # 我们网页的标题
loader = TextLoader('./constitution.txt')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)
st.write(chunks[0])
st.write(chunks[1])

pip install tiktoken

import os
from apikey import apikey
import streamlit as st # 用于创建我们的用户界面前端
from langchain.chat_models import ChatOpenAI # 用于 GPT3.5/4 模型
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma

from langchain.chains import RetrievalQA

os.environ["OPENAI_API_KEY"] = apikey
st.title('与文档聊天') # 我们网页的标题
loader = TextLoader('./constitution.txt')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vector_store = Chroma.from_documents(chunks, embeddings)
# 初始化 OpenAI 实例
llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=0)
retriever=vector_store.as_retriever()
chain = RetrievalQA.from_chain_type(llm, retriever=retriever)
# 从用户输入获取问题
question = st.text_input('输入你的问题')
if question:
    # 运行链
    response = chain.run(question)
    st.write(response)

import os
from apikey import apikey
import streamlit as st # 用于创建我们的用户界面前端
from langchain.chat_models import ChatOpenAI # 用于 GPT3.5/4 模型
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain

os.environ["OPENAI_API_KEY"] = apikey
st.title('与文档聊天') # 我们网页的标题
loader = TextLoader('./constitution.txt')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vector_store = Chroma.from_documents(chunks, embeddings)
# 初始化 OpenAI 实例
llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=0)
retriever=vector_store.as_retriever()
crc = ConversationalRetrievalChain.from_llm(llm, retriever)
# 从用户输入获取问题
question = st.text_input('输入你的问题')
if question:
    if 'history' not in st.session_state:
        st.session_state['history'] = []

        response = crc.run({
            'question': question,
            'chat_history': st.session_state['history']
        })
        st.session_state['history'].append((question, response))
    st.write(response)

import os
from apikey import apikey
import streamlit as st # 用于创建我们的用户界面前端
from langchain.chat_models import ChatOpenAI # 用于 GPT3.5/4 模型
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain

os.environ["OPENAI_API_KEY"] = apikey
st.title('与文档聊天') # 我们网页的标题
loader = TextLoader('./constitution.txt')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vector_store = Chroma.from_documents(chunks, embeddings)
# 初始化 OpenAI 实例
llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=0)
retriever=vector_store.as_retriever()
crc = ConversationalRetrievalChain.from_llm(llm, retriever)
# 从用户输入获取问题
question = st.text_input('输入你的问题')
if question:
    if 'history' not in st.session_state:
        st.session_state['history'] = []

    response = crc.run({
        'question': question,
        'chat_history': st.session_state['history']
    })
    st.session_state['history'].append((question, response))
    st.write(response)
    st.write(st.session_state['history']) # 输出聊天历史记录

for prompts in st.session_state['history']:
        st.write("Question: " + prompts[0])
        st.write("Answer: " + prompts[1])

def clear_history():
    if 'history' in st.session_state:
        del st.session_state['history']

uploaded_file = st.file_uploader('Upload file:',type=['pdf','docx', 'txt'])
add_file = st.button('Add File', on_click=clear_history)

uploaded_file = st.file_uploader('Upload file:',type=['pdf','docx', 'txt'])
add_file = st.button('Add File', on_click=clear_history)

if uploaded_file and add_file:
    bytes_data = uploaded_file.read()
    file_name = os.path.join('./', uploaded_file.name)
    with open (file_name, 'wb') as f:
        f.write(bytes_data)
    loader = TextLoader(file_name)
    documents = loader.load()

import os
from apikey import apikey
import streamlit as st # 用于创建我们的用户界面前端
from langchain.chat_models import ChatOpenAI # 用于 GPT3.5/4 模型
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain

os.environ["OPENAI_API_KEY"] = apikey

def clear_history():
    if 'history' in st.session_state:
        del st.session_state['history']


st.title('Chat with Document') # 我们网页的标题
uploaded_file = st.file_uploader('Upload file:',type=['pdf','docx', 'txt'])
add_file = st.button('Add File', on_click=clear_history)

if uploaded_file and add_file:
    bytes_data = uploaded_file.read()
    file_name = os.path.join('./', uploaded_file.name)
    with open (file_name, 'wb') as f:
        f.write(bytes_data)
    loader = TextLoader(file_name)
    documents = loader.load()

    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    chunks = text_splitter.split_documents(documents)

    embeddings = OpenAIEmbeddings()
    vector_store = Chroma.from_documents(chunks, embeddings)
    # 初始化 OpenAI 实例
    llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=0)
    retriever=vector_store.as_retriever()
    crc = ConversationalRetrievalChain.from_llm(llm, retriever)
    st.session_state.crc = crc
    # success message when file is chunked & embedded successfully
    st.success('File uploaded, chunked and embedded successfully')  

    # 从用户输入获取问题
question = st.text_input('输入你的问题')
if question:
    if 'crc' in st.session_state:
        crc = st.session_state.crc
        
    if 'history' not in st.session_state:
        st.session_state['history'] = []

    response = crc.run({
        'question': question,
        'chat_history': st.session_state['history']
    })
    st.session_state['history'].append((question, response))
    st.write(response)
    # st.write(st.session_state['history']) # 输出聊天历史记录
    for prompts in st.session_state['history']:
        st.write("Question: " + prompts[0])
        st.write("Answer: " + prompts[1])

name, extension = os.path.splitext(file_name)
if extension == '.pdf':
    from langchain.document_loaders import PyPDFLoader
    loader = PyPDFLoader(file_name)
elif extension == '.docx':
    from langchain.document_loaders import Docx2txtLoader
    loader = Docx2txtLoader(file_name)
elif extension == '.txt':
    from langchain.document_loaders import TextLoader
    loader = TextLoader(file_name)
else:
    st.write('Document format is not supported!')

pip install youtube-transcript-api

import os
from apikey import apikey
import streamlit as st # 用于创建我们的用户界面前端
from langchain.chat_models import ChatOpenAI # 用于 GPT3.5/4 模型
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import YoutubeLoader

os.environ["OPENAI_API_KEY"] = apikey

def clear_history():
    if 'history' in st.session_state:
        del st.session_state['history']


st.title('Chat with Youtube')
youtube_url = st.text_input('Input your Youtube URL')
if youtube_url:
    loader = YoutubeLoader.from_youtube_url(youtube_url)
    documents = loader.load()

    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    chunks = text_splitter.split_documents(documents)

    embeddings = OpenAIEmbeddings()
    vector_store = Chroma.from_documents(chunks, embeddings)
    # 初始化 OpenAI 实例
    llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=0)
    retriever=vector_store.as_retriever()
    crc = ConversationalRetrievalChain.from_llm(llm, retriever)
    st.session_state.crc = crc
    # success message when file is chunked & embedded successfully
    st.success('File uploaded, chunked and embedded successfully')  

    # 从用户输入获取问题
question = st.text_input('输入你的问题')
if question:
    if 'crc' in st.session_state:
        crc = st.session_state.crc
        
    if 'history' not in st.session_state:
        st.session_state['history'] = []

    response = crc.run({
        'question': question,
        'chat_history': st.session_state['history']
    })
    st.session_state['history'].append((question, response))
    st.write(response)
    # st.write(st.session_state['history']) # 输出聊天历史记录
    for prompts in st.session_state['history']:
        st.write("Question: " + prompts[0])
        st.write("Answer: " + prompts[1])

LangChain 速成课程：构建基于 OpenAI LLM 的应用

LangChain 速成课程：构建基于 OpenAI LLM 的应用

思维导图

什么是 LangChain

LangChain 的工作原理

创建自动化的 AI 工作流程

安装、设置和我们的第一个 LangChain 应用程序

连接到 OpenAI LLM

提示模板

简单链条

单个参数

顺序链条

顺序链灵活性

顺序链 - 链接

代理

与文档聊天

嵌入

添加记忆（聊天记录）

输出聊天记录

上传自定义文档

上传和读取文件

完整源码

输出

加载不同文件类型

与 YouTube 聊天

总结与展望

更多推荐文章

相关免费在线工具

LangChain 速成课程：构建基于 OpenAI LLM 的应用

LangChain 速成课程：构建基于 OpenAI LLM 的应用

思维导图

什么是 LangChain

LangChain 的工作原理

创建自动化的 AI 工作流程

安装、设置和我们的第一个 LangChain 应用程序

连接到 OpenAI LLM

提示模板

简单链条

单个参数

顺序链条

顺序链灵活性

顺序链 - 链接

代理

与文档聊天

嵌入

添加记忆（聊天记录）

输出聊天记录

上传自定义文档

上传和读取文件

完整源码

输出

加载不同文件类型

与 YouTube 聊天

总结与展望

微信扫一扫，关注极客日志

更多推荐文章

相关免费在线工具