Sure, here's the translation in simplified Chinese while keeping the HTML structure intact: ```html 询问OpenAI关于ICC T20世界杯 ```

最近我参加了一些与生成人工智能相关的Udemy课程，学习如何编写我们自己的ChatBot，并使用OpenAI。

Sure, here is the simplified Chinese translation of the provided English text: "另外，最近结束的ICC男子T20比赛中，印度获得了冠军。我想问ChatGPT关于这个事情，它并不能直接回答，而是在网上搜索了答案并提供给了我。"

To translate the English text to simplified Chinese while keeping the HTML structure, you can use the following: ```html

但如果我关闭聊天记录，明天回来再问同样的问题，ChatGPT 仍然会搜索网页来找答案。原因是，ChatGPT 的基础模型并未经过关于刚刚在几天前结束的 ICC T20 世界杯 2024 的信息训练。

``` This HTML snippet preserves the structure and translates the text into simplified Chinese.

Sure, here is the text translated into simplified Chinese while maintaining HTML structure: ```html

这些信息在维基百科上由强大的贡献者团队在几小时内更新。

``` This HTML snippet preserves the structure and provides the translation of the given English sentence.

Here is the translated text in simplified Chinese while keeping the HTML structure: ```html

那么我们如何利用这些信息或任何公开（互联网）或私密（公司内部、家庭等）可获取的信息来使用ChatGPT获得答案呢？

```

Sure, here's the text translated into simplified Chinese while keeping the HTML structure intact: ```html

我们可以通过以下的RAG架构来实现它。

```

1. Sure, here is the translated text in simplified Chinese while keeping the HTML structure intact: ```html 上传和处理信息 — 我复制了上述维基百科关于T20世界杯的部分，并将其保存为PDF。然后上传了该PDF文件，并进行了按行切分的处理。 ``` This HTML structure allows the translated text to be displayed correctly on a web page while maintaining the formatting and alignment as per the original English text.
2. 在向量存储中存储嵌入 - 分块的行被馈送到一个OpenAI嵌入算法中，该算法将数据转换为OpenAI可识别的格式集合。最终，它被存储在一个向量存储中，以供将来查询使用。这个向量存储是非持久的，但也有持久的存储方式。
3. Certainly! Here's the translation of the text into simplified Chinese while keeping the HTML structure intact: ```html 问题来自演员 — 现在演员可以询问关于T20世界杯的问题 ``` In this HTML snippet, `` is used to enclose the translated Chinese text, preserving the original structure of the sentence in English.
4. To translate the English text "Similarity search — The question will be used as input to perform a similarity search in the Vector store find any matching" into simplified Chinese while keeping the HTML structure intact, you can use the following: ```html 相似度搜索 — 问题将被用作输入，在向量存储中执行相似度搜索以找到任何匹配项 ``` This HTML code snippet ensures that the text is marked as simplified Chinese (`lang="zh"`) and preserves the structure for web display or processing.
5. Sure, here is the translation of the text into simplified Chinese, while keeping the HTML structure: ```html 发送匹配信息 — 匹配的信息被发送回一个使用PB级数据和数千小时计算训练的OpenAI大型语言模型（LLM）。 ``` In this translation: - "发送匹配信息" corresponds to "Send matched information". - "匹配的信息被发送回一个使用PB级数据和数千小时计算训练的OpenAI大型语言模型（LLM）" corresponds to "The matched information is sent back to an OpenAI large language model (LLM) which is trained using Peta-bytes of data and thousands of hours worth of computing."
6. 保持HTML结构，将以下英文文本翻译为简体中文： 生成自然语言答案 — OpenAI LLM 在自然语言中生成答案。
7. Sure, here's the translated text in simplified Chinese, keeping the HTML structure: ```html 答案 — 答案发送回演员 ``` This HTML code retains the original structure while displaying the simplified Chinese translation.

import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain_community.chat_models import ChatOpenAI

OPENAI_API_KEY = "YOUR_OPEN_AI_KEY" #Replace with OpenAI Key

#Upload PDF Files
st.header("My First Chatbot")

with st.sidebar:
    st.title("Your Documents")
    file = st.file_uploader("Upload a PDF file and start asking questions", type="pdf")

#Extract the text
if file is not None:
    pdf_reader = PdfReader(file)
    text=""
    for page in pdf_reader.pages:
        text += page.extract_text()
        #st.write(text)

#Break it into chunks
    text_splitter = RecursiveCharacterTextSplitter(
        separators="\n",
        chunk_size=500,
        chunk_overlap=150,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    #st.write(chunks)

    #Generating embeddings
    embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

    #Creating vector store - FAISS - FB AI Semantic Store
    # Open AI Embeddings
    # Initializing FAISS
    vector_store = FAISS.from_texts(chunks, embeddings)

    #Get user question
    user_question = st.text_input("Type your question here")

    #so similarity search
    if user_question:
        match = vector_store.similarity_search(user_question)
        #st.write(match)

        #Define LLM
        llm = ChatOpenAI(
            openai_api_key = OPENAI_API_KEY,
            temperature = 0,
            max_tokens = 1000,
            model_name = "gpt-3.5-turbo"
        )

        #output results
        chain = load_qa_chain(llm, chain_type="stuff")
        response = chain.run(input_documents = match, question = user_question)
        st.write(response)

Sure, here's the translation of the provided text into simplified Chinese while maintaining the HTML structure: ```html

我们现在可以看到这个系统正在运行。当我们向我们的聊天机器人提出相同的问题而不是OpenAI的聊天机器人时，它会根据本地拥有的信息来回答，而不是参考互联网。

``` In this translation: - "我们现在可以看到这个系统正在运行。" corresponds to "We can now see this system in action." - "当我们向我们的聊天机器人提出相同的问题而不是OpenAI的聊天机器人时，" corresponds to "When we ask the same questions from our ChatBot instead of OpenAI ChatBot" - "它会根据本地拥有的信息来回答，而不是参考互联网。" corresponds to "it is giving the answer from the information it has locally and not referring the internet."

In simplified Chinese, "Conclusion" would be translated as "结论".

在保持HTML结构的前提下，将以下英文文本翻译成简体中文： 有许多不同的方法可以让OpenAI学习最新信息。其中最广泛使用的是：

1. To translate "Retrieval Augmented Generation (RAG)" to simplified Chinese while keeping the HTML structure, you would use the following: ```html 检索增强生成 (RAG) ``` In this HTML structure: - `` indicates a ruby annotation, which is used in East Asian typography to annotate characters with phonetic information or translations. - `` is used to provide parentheses around the annotation. - `` contains the actual translation or annotation text. So, the translation "检索增强生成" corresponds to "Retrieval Augmented Generation", and "RAG" is provided as an annotation next to it.
2. To translate "Finetuning" into simplified Chinese while keeping HTML structure, you would use the following: ```html 微调 ``` This HTML code preserves the language attribute (`lang="zh-CN"`) for specifying simplified Chinese, and wraps the translated text "微调" (which means "Finetuning" in Chinese) in a `` tag.
3. Sure, here's the translation of "Prompt Engineering" into simplified Chinese while keeping HTML structure: ```html

   即时工程

   ```

Sure, here's the translated text in simplified Chinese while keeping the HTML structure: ```html

在这三者中，RAG 目前是实施成本最低且生成高度精确答案的选择。这对于那些拥有私密数据存储库但需要利用OpenAI和ChatGPT支持员工和客户查询的企业来说是理想的。

``` This HTML snippet preserves the original text while presenting it in simplified Chinese.

Sure, here's the translation of "References" into simplified Chinese, while keeping the HTML structure: ```html 参考文献 ``` This HTML code ensures that the text "参考文献" appears correctly in a web page, maintaining both the semantic structure and language specifications.

To translate "Recommended Udemy Course — https://www.udemy.com/course/generative-ai-for-beginners-b" into simplified Chinese while keeping the HTML structure, you would write: ```html 推荐的Udemy课程 — https://www.udemy.com/course/generative-ai-for-beginners-b ``` In simplified Chinese characters, it reads as: 推荐的Udemy课程 — https://www.udemy.com/course/generative-ai-for-beginners-b