法律文件摘要

details_to_extract = [
    'Parties involved (sublessor, sublessee, and original lessor)',
    'Property details (address, description, and permitted use)', 
    'Term and rent (start date, end date, monthly rent, and security deposit)',
    'Responsibilities (utilities, maintenance, and repairs)',
    'Consent and notices (landlord\'s consent, and notice requirements)',
    'Special provisions (furniture, parking, and subletting restrictions)'
]

from io import BytesIO
import re

import pypdf
import requests

def get_llm_text(pdf_file):
    reader = pypdf.PdfReader(pdf_file)
    text = "\n".join([page.extract_text() for page in reader.pages])

    # Remove extra whitespace
    text = re.sub(r'\s+', ' ', text) 

    # Remove page numbers
    text = re.sub(r'\n\s*\d+\s*\n', '\n', text) 

    return text


# Create the full URL from the GitHub repository
url = "https://raw.githubusercontent.com/anthropics/anthropic-cookbook/main/skills/summarization/data/Sample Sublease Agreement.pdf"
url = url.replace(" ", "%20")

# Download the PDF file into memory
response = requests.get(url)

# Load the PDF from memory
pdf_file = BytesIO(response.content)

document_text = get_llm_text(pdf_file) 
print(document_text[:50000]) 

import anthropic

# Initialize the Anthropic client
client = anthropic.Anthropic()

def summarize_document(text, details_to_extract, model="claude-sonnet-4-5", max_tokens=1000):

    # Format the details to extract to be placed within the prompt's context
    details_to_extract_str = '\n'.join(details_to_extract)
    
    # Prompt the model to summarize the sublease agreement
    prompt = f"""Summarize the following sublease agreement. Focus on these key aspects:

    {details_to_extract_str}

    Provide the summary in bullet points nested within the XML header for each section. For example:

    <parties involved>
    - Sublessor: [Name]
    // Add more details as needed
    </parties involved>
    
    If any information is not explicitly stated in the document, note it as "Not specified". Do not preamble.

    Sublease agreement text:
    {text}
    """

    response = client.messages.create(
        model=model,
        max_tokens=max_tokens,
        system="You are a legal analyst specializing in real estate law, known for highly accurate and detailed summaries of sublease agreements.",
        messages=[
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": "Here is the summary of the sublease agreement: <summary>"}
        ],
        stop_sequences=["</summary>"]
    )

    return response.content[0].text

sublease_summary = summarize_document(document_text, details_to_extract)
print(sublease_summary)

import anthropic

# Initialize the Anthropic client
client = anthropic.Anthropic()

def chunk_text(text, chunk_size=20000):
    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

def summarize_long_document(text, details_to_extract, model="claude-sonnet-4-5", max_tokens=1000):

    # Format the details to extract to be placed within the prompt's context
    details_to_extract_str = '\n'.join(details_to_extract)

    # Iterate over chunks and summarize each one
    chunk_summaries = [summarize_document(chunk, details_to_extract, model=model, max_tokens=max_tokens) for chunk in chunk_text(text)]
    
    final_summary_prompt = f"""
    
    You are looking at the chunked summaries of multiple documents that are all related. 
    Combine the following summaries of the document from different truthful sources into a coherent overall summary:

    <chunked_summaries>
    {"".join(chunk_summaries)}
    </chunked_summaries>

    Focus on these key aspects:
    {details_to_extract_str})

    Provide the summary in bullet points nested within the XML header for each section. For example:

    <parties involved>
    - Sublessor: [Name]
    // Add more details as needed
    </parties involved>
    
    If any information is not explicitly stated in the document, note it as "Not specified". Do not preamble.
    """

    response = client.messages.create(
        model=model,
        max_tokens=max_tokens,
        system="You are a legal expert that summarizes notes on one document.",
        messages=[
            {"role": "user",  "content": final_summary_prompt},
            {"role": "assistant", "content": "Here is the summary of the sublease agreement: <summary>"}

        ],
        stop_sequences=["</summary>"]
    )
    
    return response.content[0].text

long_summary = summarize_long_document(document_text, details_to_extract)
print(long_summary)