Hello, let&#39;s use RAG today. We&#39;re working on report creation. It&#39;s tough to keep up with recent articles on quantum computing and generative AI, especially when you need to refer back to past articles or revisit previous knowledge. This time, let&#39;s see if we can use RAG for creating reports. We&#39;ll be using Mistral, Langchain, and Gradio for this project. <pre class="ql-syntax" spellcheck="false">pip install --quiet transformers accelerate langchain langchain-community sentence-transformers faiss-gpu pypdf gradio
</pre> <pre class="ql-syntax" spellcheck="false">from transformers import AutoTokenizer, pipeline

model_id = &#34;mistralai/Mistral-7B-Instruct-v0.2&#34;
tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(&#34;text-generation&#34;, model=model_id, tokenizer=tokenizer, device=0, max_new_tokens=300)

query = &#39;what is sandbox and softbank doing on quantum business?&#39;
pipe(query)
</pre> <pre class="ql-syntax" spellcheck="false">Sandbox is a subsidiary of SoftBank, and they are indeed working on quantum computing. They have a quantum computing division called Quantum Matter Inc.
</pre> It gave a completely nonsensical answer, clearly wrong and full of hallucinations. Next, as a practice, we will load a website. <pre class="ql-syntax" spellcheck="false">from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loader = WebBaseLoader([&#34;https://www.digicert.com/jp/faq/cryptography/what-is-post-quantum-cryptography#:~:text=%E8%80%90%E9%87%8F%E5%AD%90%E6%9A%97%E5%8F%B7%E6%96%B9%E5%BC%8F%EF%BC%88%E9%87%8F%E5%AD%90,%E6%8C%87%E3%81%97%E3%81%A6%E3%81%84%E3%82%8B%E7%94%A8%E8%AA%9E%E3%81%A7%E3%81%99%E3%80%82&#34;, &#34;https://www.sbbit.jp/article/cont1/85249&#34;, &#34;https://quantumcomputingreport.com/softbank-leverages-sandboxaqs-aqtive-guard-to-identify-it-infrastructure-vulnerabilities/&#34;])

documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = HuggingFaceEmbeddings(
 model_name=&#34;intfloat/multilingual-e5-large&#34;
)

db = FAISS.from_documents(docs, embeddings)
retriever = db.as_retriever()
print(db.index.ntotal)
</pre> We will set it up to retrieve relevant information from several websites. The index number has become 33. <pre class="ql-syntax" spellcheck="false">from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain.llms import HuggingFacePipeline

llm = HuggingFacePipeline(pipeline=pipe)

template = &#34;&#34;&#34;次のコンテキストを踏まえた上で日本語で答えて下さい:

{context}

Question: {question}
&#34;&#34;&#34;

prompt = ChatPromptTemplate.from_template(template)

def format_docs(docs):
 return &#34;\n\n&#34;.join([d.page_content for d in docs])

chain = (
 {&#34;context&#34;: retriever | format_docs, &#34;question&#34;: RunnablePassthrough()}
 | prompt
 | llm
 | StrOutputParser()
)

query = &#39;What is the name of quantum computing product provided by sandbox in this article?&#39;
response = chain.invoke(query)  
if &#34;Answer:&#34; in response:
 response = response.split(&#34;Answer: &#34;)[1]
if &#34;Question:&#34; in response:
 response = response.split(&#34;Question: &#34;)[0]
if &#34;Japanese Translation:&#34; in response:
 response = response.split(&#34;Japanese Translation: &#34;)[1]

response = response.replace(&#34;\n\n&#34;, &#34;&#34;)
response
</pre> I have tried changing the query. <pre class="ql-syntax" spellcheck="false">&#39;AQtive Guard is the name of the quantum computing product provided by Sandbox in this article. It is a cryptography management platform that helps identify IT infrastructure vulnerabilities and supports compliance with NIST initiatives on post-quantum cryptography.&#39;
</pre> It provided a very detailed explanation. <pre class="ql-syntax" spellcheck="false">import gradio as gr
import os

def add_text(history, text):
 history = history + [(text, None)]
 return history, gr.Textbox(value=&#34;&#34;, interactive=False)

def bot(history):
 query = history[-1][0]
   
 response = chain.invoke(query)  
 if &#34;Answer:&#34; in response:
  response = response.split(&#34;Answer:&#34;)[1]
 if &#34;Question:&#34; in response:
  response = response.split(&#34;Question:&#34;)[0]
 if &#34;Japanese Translation:&#34; in response:
  response = response.split(&#34;Japanese Translation:&#34;)[1]
 response = response.replace(&#34;\n\n&#34;, &#34;&#34;)
   
 history[-1][1] = &#34;&#34;
 for character in response:
  history[-1][1] += character
  yield history

with gr.Blocks() as demo:
 chatbot = gr.Chatbot([])
 with gr.Row():
  txt = gr.Textbox(
   scale=4,
   show_label = False,
   container = False
  )
 clear = gr.Button(&#34;Clear&#34;) 

 txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue = False).then(bot, chatbot, chatbot)
 txt_msg.then(lambda: gr.Textbox(interactive = True), None, [txt], queue = False)
 clear.click(lambda: None, None, chatbot, queue=False)

demo.queue()  
demo.launch(share=True)
</pre> Gradio is quite simple, isn&#39;t it? <img src="https://assets.blueqat.com/public/uploads/us-east-2:4805ff4b-c3cc-4344-b165-86544c34d0bf/2024/04/12/Gradio.png"/> We have created a nice interface. It looks like we&#39;ll be able to handle full-fledged report creation in the future. It&#39;s helpful that it even explains related terms thoroughly. That&#39;s all.

Create an LLM using RAG to explain articles related to quantum computing.

Yuichiro Minato