LangChain速成课程_构建基于OpenAI_LLM的应用

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

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])