Datasets:
File size: 2,264 Bytes
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import pandas as pd
import fitz # PyMuPDF
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from tqdm.notebook import tqdm
# Function to read a PDF and convert to text
def pdf_to_text(pdf_path):
pdf_text = ""
with fitz.open(pdf_path) as pdf_document:
for page_num in range(len(pdf_document)):
page = pdf_document.load_page(page_num)
pdf_text += page.get_text()
return pdf_text
# Load dataset
# Extract the last 30 English questions and answers
last_30_QA = QA.tail(80)
# Read PDF and convert to text
pdf_path = 'ahmed.pdf' # Replace with your actual PDF path
pdf_text = pdf_to_text(pdf_path)
# Setup LangChain with ChatOpenAI
llm = ChatOpenAI(
model="gpt-4",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key="""""", # Replace with your actual API key
)
# Define the prompt template manually
prompt_template = PromptTemplate(
template="You are a helpful assistant. Please rewrite the following into a RAG-able paragraph.\n\n{text}",
input_variables=["text"]
)
# Initialize list to store RAG-able texts
rag_able_texts = []
# Process each question and answer
for index, row in tqdm(last_30_QA.iterrows(), total=last_30_QA.shape[0]):
question = row['english']
answer = row['answer']
combined_text = f"\n\n{index + 1}. {question} correct answer {answer} + \t\n\n"
# Define the system and human messages
system_message = SystemMessage(content="You are a helpful assistant. Please rewrite the following into a RAG-able paragraph.")
human_message = HumanMessage(content=combined_text)
# Generate the RAG-able text
# response = llm([system_message, human_message])
# print(response)
# rag_able_text = response.content
rag_able_text = combined_text
# Append the generated text to the list
rag_able_texts.append(rag_able_text)
# Combine all the RAG-able texts
final_rag_able_text = pdf_text +"END__"+"\n\n".join(rag_able_texts)
# Save the final RAG-able text to a file
with open('final_rag_able_text.txt', 'w') as file:
file.write(final_rag_able_text)
print("RAG-able text created successfully.")
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