Merge branch 'master' of https://github.com/brown-dash/Dash-Web into Merge

1 files changed, 843 insertions, 0 deletions

diff --git a/src/server/chunker/pdf_chunker.py b/src/server/chunker/pdf_chunker.py
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+import asyncio
+import concurrent
+import sys
+
+from tqdm.asyncio import tqdm_asyncio  # Progress bar for async tasks
+import PIL
+from anthropic import Anthropic  # For language model API
+from packaging.version import parse  # Version checking
+import pytesseract  # OCR library for text extraction from images
+import re
+import dotenv  # For environment variable loading
+from lxml import etree  # XML parsing
+from tqdm import tqdm  # Progress bar for non-async tasks
+import fitz  # PyMuPDF, PDF processing library
+from PIL import Image, ImageDraw  # Image processing
+from typing import List, Dict, Any, TypedDict  # Typing for function annotations
+from ultralyticsplus import YOLO  # Object detection model (YOLO)
+import base64
+import io
+import json
+import os
+import uuid  # For generating unique IDs
+from enum import Enum  # Enums for types like document type and purpose
+import openai
+import numpy as np
+from PyPDF2 import PdfReader  # PDF text extraction
+from openai import OpenAI  # OpenAI client for text completion
+from sklearn.cluster import KMeans  # Clustering for summarization
+import warnings
+
+# Silence specific warnings
+warnings.filterwarnings('ignore', message="Valid config keys have changed")
+warnings.filterwarnings('ignore', message="torch.load")
+
+dotenv.load_dotenv()  # Load environment variables
+
+# Fix for newer versions of PIL
+# if parse(PIL.__version__) >= parse('10.0.0'):
+#     Image.LINEAR = Image.BILINEAR
+
+# Global dictionary to track progress of document processing jobs
+current_progress = {}
+
+def update_progress(job_id, step, progress_value):
+    """
+    Output the progress in JSON format to stdout for the Node.js process to capture.
+
+    :param job_id: The unique identifier for the processing job.
+    :param step: The current step of the job.
+    :param progress_value: The percentage of completion for the current step.
+    """
+    progress_data = {
+        "job_id": job_id,
+        "step": step,
+        "progress": progress_value
+    }
+    print(f"PROGRESS:{json.dumps(progress_data)}", file=sys.stderr)
+    sys.stderr.flush()
+
+
+
+class ElementExtractor:
+    """
+    A class that uses a YOLO model to extract tables and images from a PDF page.
+    """
+
+    def __init__(self, output_folder: str, doc_id: str):
+        """
+        Initializes the ElementExtractor with the output folder for saving images and the YOLO model.
+
+        :param output_folder: Path to the folder where extracted elements will be saved.
+        """
+        self.doc_id = doc_id
+        self.output_folder = os.path.join(output_folder, doc_id)
+        os.makedirs(self.output_folder, exist_ok=True)
+        self.model = YOLO('keremberke/yolov8m-table-extraction')  # Load YOLO model for table extraction
+        self.model.overrides['conf'] = 0.25  # Set confidence threshold for detection
+        self.model.overrides['iou'] = 0.45  # Set Intersection over Union (IoU) threshold
+        self.padding = 5  # Padding around detected elements
+
+    async def extract_elements(self, page, padding: int = 20) -> List[Dict[str, Any]]:
+        """
+        Asynchronously extract tables and images from a PDF page.
+
+        :param page: A Page object representing a PDF page.
+        :param padding: Padding around the extracted elements.
+        :return: A list of dictionaries containing the extracted elements.
+        """
+        tasks = [
+            asyncio.create_task(self.extract_tables(page.image, page.page_num)),  # Extract tables from the page
+            asyncio.create_task(self.extract_images(page.page, page.image, page.page_num))  # Extract images from the page
+        ]
+        results = await asyncio.gather(*tasks)  # Wait for both tasks to complete
+        return [item for sublist in results for item in sublist]  # Flatten and return results
+
+    async def extract_tables(self, img: Image.Image, page_num: int) -> List[Dict[str, Any]]:
+        """
+        Asynchronously extract tables from a given page image using the YOLO model.
+
+        :param img: The image of the PDF page.
+        :param page_num: The current page number.
+        :return: A list of dictionaries with metadata about the detected tables.
+        """
+        results = self.model.predict(img, verbose=False)  # Predict table locations using YOLO
+        tables = []
+
+        for idx, box in enumerate(results[0].boxes):
+            x1, y1, x2, y2 = map(int, box.xyxy[0])  # Extract bounding box coordinates
+
+            # Draw a red rectangle on the full page image around the table
+            page_with_outline = img.copy()
+            draw = ImageDraw.Draw(page_with_outline)
+            draw.rectangle(
+                [max(0, x1 + self.padding), max(0, y1 + self.padding), min(page_with_outline.width, x2 + self.padding),
+                 min(page_with_outline.height, y2 + self.padding)], outline="red", width=2)  # Draw red outline
+
+            # Save the full page with the red outline
+            table_filename = f"table_page{page_num + 1}_{idx + 1}.png"
+            table_path = os.path.join(self.output_folder, table_filename)
+            page_with_outline.save(table_path)
+
+            file_path_for_client = f"{self.doc_id}/{table_filename}"
+
+            tables.append({
+                'metadata': {
+                    "type": "table",
+                    "location": [x1 / img.width, y1 / img.height, x2 / img.width, y2 / img.height],
+                    "file_path": file_path_for_client,
+                    "start_page": page_num,
+                    "end_page": page_num,
+                    "base64_data": self.image_to_base64(page_with_outline)
+                }
+            })
+
+        return tables
+
+    async def extract_images(self, page: fitz.Page, img: Image.Image, page_num: int) -> List[Dict[str, Any]]:
+        """
+        Asynchronously extract embedded images from a PDF page.
+
+        :param page: A fitz.Page object representing the PDF page.
+        :param img: The image of the PDF page.
+        :param page_num: The current page number.
+        :return: A list of dictionaries with metadata about the detected images.
+        """
+        images = []
+        image_list = page.get_images(full=True)  # Get a list of images on the page
+
+        if not image_list:
+            return images
+
+        for img_index, img_info in enumerate(image_list):
+            xref = img_info[0]  # XREF of the image in the PDF
+            base_image = page.parent.extract_image(xref)  # Extract the image by its XREF
+            image_bytes = base_image["image"]
+            image = Image.open(io.BytesIO(image_bytes))  # Convert bytes to PIL image
+            width_ratio = img.width / page.rect.width  # Scale factor for width
+            height_ratio = img.height / page.rect.height  # Scale factor for height
+
+            # Get image coordinates or default to page rectangle
+            rect_list = page.get_image_rects(xref)
+            if rect_list:
+                rect = rect_list[0]
+                x1, y1, x2, y2 = rect
+            else:
+                rect = page.rect
+                x1, y1, x2, y2 = rect
+
+            # Draw a red rectangle on the full page image around the embedded image
+            page_with_outline = img.copy()
+            draw = ImageDraw.Draw(page_with_outline)
+            draw.rectangle([x1 * width_ratio, y1 * height_ratio, x2 * width_ratio, y2 * height_ratio],
+                            outline="red", width=2)  # Draw red outline
+
+            # Save the full page with the red outline
+            image_filename = f"image_page{page_num + 1}_{img_index + 1}.png"
+            image_path = os.path.join(self.output_folder, image_filename)
+            page_with_outline.save(image_path)
+
+            file_path_for_client = f"{self.doc_id}/{image_filename}"
+
+            images.append({
+                'metadata': {
+                    "type": "image",
+                    "location": [x1 / page.rect.width, y1 / page.rect.height, x2 / page.rect.width,
+                                    y2 / page.rect.height],
+                    "file_path": file_path_for_client,
+                    "start_page": page_num,
+                    "end_page": page_num,
+                    "base64_data": self.image_to_base64(image)
+                }
+            })
+
+        return images
+
+    @staticmethod
+    def image_to_base64(image: Image.Image) -> str:
+        """
+        Convert a PIL image to a base64-encoded string.
+
+        :param image: The PIL image to be converted.
+        :return: The base64-encoded string of the image.
+        """
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")  # Save image as PNG to an in-memory buffer
+        return base64.b64encode(buffered.getvalue()).decode('utf-8')  # Convert to base64 and return
+
+
+class ChunkMetaData(TypedDict):
+    """
+    A TypedDict that defines the metadata structure for chunks of text and visual elements.
+    """
+    text: str
+    type: str
+    original_document: str
+    file_path: str
+    doc_id: str
+    location: str
+    start_page: int
+    end_page: int
+    base64_data: str
+
+
+class Chunk(TypedDict):
+    """
+    A TypedDict that defines the structure for a document chunk, including metadata and embeddings.
+    """
+    id: str
+    values: List[float]
+    metadata: ChunkMetaData
+
+
+class Page:
+    """
+    A class that represents a single PDF page, handling its image representation and element masking.
+    """
+
+    def __init__(self, page: fitz.Page, page_num: int):
+        """
+        Initializes the Page with its page number and the image representation of the page.
+
+        :param page: A fitz.Page object representing the PDF page.
+        :param page_num: The number of the page in the PDF.
+        """
+        self.page = page
+        self.page_num = page_num
+        # Get high-resolution image of the page (for table/image extraction)
+        self.pix = page.get_pixmap(matrix=fitz.Matrix(2, 2))
+        self.image = Image.frombytes("RGB", [self.pix.width, self.pix.height], self.pix.samples)
+        self.masked_image = self.image.copy()  # Image with masked elements (tables/images)
+        self.draw = ImageDraw.Draw(self.masked_image)
+        self.elements = []  # List to store extracted elements
+
+    def add_element(self, element):
+        """
+        Adds a detected element (table/image) to the page and masks its location on the page image.
+
+        :param element: A dictionary containing metadata about the detected element.
+        """
+        self.elements.append(element)
+        # Mask the element on the page image by drawing a white rectangle over its location
+        x1, y1, x2, y2 = [coord * self.image.width if i % 2 == 0 else coord * self.image.height
+                          for i, coord in enumerate(element['metadata']['location'])]
+        self.draw.rectangle([x1, y1, x2, y2], fill="white")  # Draw a white rectangle to mask the element
+
+
+class PDFChunker:
+    """
+    The main class responsible for chunking PDF files into text and visual elements (tables/images).
+    """
+
+    def __init__(self, output_folder: str = "output", doc_id: str = '', image_batch_size: int = 5) -> None:
+        """
+        Initializes the PDFChunker with an output folder and an element extractor for visual elements.
+
+        :param output_folder: Folder to store the output files (extracted tables/images).
+        :param image_batch_size: The batch size for processing visual elements.
+        """
+        self.client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))  # Initialize the Anthropic API client
+        self.output_folder = output_folder
+        self.image_batch_size = image_batch_size  # Batch size for image processing
+        self.doc_id = doc_id  # Add doc_id
+        self.element_extractor = ElementExtractor(output_folder, doc_id)
+
+    async def chunk_pdf(self, file_data: bytes, file_name: str, doc_id: str, job_id: str) -> List[Dict[str, Any]]:
+        """
+        Processes a PDF file, extracting text and visual elements, and returning structured chunks.
+
+        :param file_data: The binary data of the PDF file.
+        :param file_name: The name of the PDF file.
+        :param doc_id: The unique document ID for this job.
+        :param job_id: The unique job ID for the processing task.
+        :return: A list of structured chunks containing text and visual elements.
+        """
+        with fitz.open(stream=file_data, filetype="pdf") as pdf_document:
+            num_pages = len(pdf_document)  # Get the total number of pages in the PDF
+            pages = [Page(pdf_document[i], i) for i in tqdm(range(num_pages), desc="Initializing Pages")]  # Initialize each page
+
+            update_progress(job_id, "Extracting tables and images...", 0)
+            await self.extract_and_mask_elements(pages, job_id)  # Extract and mask elements (tables/images)
+
+            update_progress(job_id, "Processing tables and images...", 0)
+            await self.process_visual_elements(pages, self.image_batch_size, job_id)  # Process visual elements
+
+            update_progress(job_id, "Extracting text...", 0)
+            page_texts = await self.extract_text_from_masked_pages(pages, job_id)  # Extract text from masked pages
+
+            update_progress(job_id, "Processing text...", 0)
+            text_chunks = self.chunk_text_with_metadata(page_texts, max_words=1000, job_id=job_id)  # Chunk text into smaller parts
+
+            # Combine text and visual elements into a unified structure (chunks)
+            chunks = self.combine_chunks(text_chunks, [elem for page in pages for elem in page.elements], file_name,
+                                         doc_id)
+
+        return chunks
+
+    async def extract_and_mask_elements(self, pages: List[Page], job_id: str):
+        """
+        Extract visual elements (tables and images) from each page and mask them on the page.
+
+        :param pages: A list of Page objects representing the PDF pages.
+        :param job_id: The unique job ID for the processing task.
+        """
+        total_pages = len(pages)
+        tasks = []
+
+        for i, page in enumerate(pages):
+            tasks.append(asyncio.create_task(self.element_extractor.extract_elements(page)))  # Extract elements asynchronously
+            progress = ((i + 1) / total_pages) * 100  # Calculate progress
+            update_progress(job_id, "Extracting tables and images...", progress)
+
+        # Gather all extraction results
+        results = await asyncio.gather(*tasks)
+
+        # Mask the detected elements on the page images
+        for page, elements in zip(pages, results):
+            for element in elements:
+                page.add_element(element)  # Mask each extracted element on the page
+
+    async def process_visual_elements(self, pages: List[Page], image_batch_size: int, job_id: str) -> List[Dict[str, Any]]:
+        """
+        Process extracted visual elements in batches, generating summaries or descriptions.
+
+        :param pages: A list of Page objects representing the PDF pages.
+        :param image_batch_size: The batch size for processing visual elements.
+        :param job_id: The unique job ID for the processing task.
+        :return: A list of processed elements with metadata and generated summaries.
+        """
+        pre_elements = [element for page in pages for element in page.elements]  # Flatten list of elements
+        processed_elements = []
+        total_batches = (len(pre_elements) // image_batch_size) + 1  # Calculate total number of batches
+
+        loop = asyncio.get_event_loop()
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            # Process elements in batches
+            for i in tqdm(range(0, len(pre_elements), image_batch_size), desc="Processing Visual Elements"):
+                batch = pre_elements[i:i + image_batch_size]
+                # Run image summarization in a separate thread
+                summaries = await loop.run_in_executor(
+                    executor, self.batch_summarize_images,
+                    {j + 1: element.get('metadata').get('base64_data') for j, element in enumerate(batch)}
+                )
+
+                # Append generated summaries to the elements
+                for j, elem in enumerate(batch, start=1):
+                    if j in summaries:
+                        elem['metadata']['text'] = re.sub(r'^(Image|Table):\s*', '', summaries[j])
+                        elem['metadata']['base64_data'] = ''
+                        processed_elements.append(elem)
+
+                progress = ((i // image_batch_size) + 1) / total_batches * 100  # Calculate progress
+                update_progress(job_id, "Processing tables and images...", progress)
+
+        return processed_elements
+
+    async def extract_text_from_masked_pages(self, pages: List[Page], job_id: str) -> Dict[int, str]:
+        """
+        Extract text from masked page images (where tables and images have been masked out).
+
+        :param pages: A list of Page objects representing the PDF pages.
+        :param job_id: The unique job ID for the processing task.
+        :return: A dictionary mapping page numbers to extracted text.
+        """
+        total_pages = len(pages)
+        tasks = []
+
+        for i, page in enumerate(pages):
+            tasks.append(asyncio.create_task(self.extract_text(page.masked_image, page.page_num)))  # Perform OCR on each page
+            progress = ((i + 1) / total_pages) * 100  # Calculate progress
+            update_progress(job_id, "Extracting text...", progress)
+
+        # Return extracted text from each page
+        return dict(await asyncio.gather(*tasks))
+
+    @staticmethod
+    async def extract_text(image: Image.Image, page_num: int) -> (int, str):
+        """
+        Perform OCR on the provided image to extract text.
+
+        :param image: The PIL image of the page.
+        :param page_num: The current page number.
+        :return: A tuple containing the page number and the extracted text.
+        """
+        result = pytesseract.image_to_string(image)  # Extract text using Tesseract OCR
+        return page_num + 1, result.strip()  # Return the page number and extracted text
+
+    def chunk_text_with_metadata(self, page_texts: Dict[int, str], max_words: int, job_id: str) -> List[Dict[str, Any]]:
+        """
+        Break the extracted text into smaller chunks with metadata (e.g., page numbers).
+
+        :param page_texts: A dictionary mapping page numbers to extracted text.
+        :param max_words: The maximum number of words allowed in a chunk.
+        :param job_id: The unique job ID for the processing task.
+        :return: A list of dictionaries containing text chunks with metadata.
+        """
+        chunks = []
+        current_chunk = ""
+        current_start_page = 0
+        total_words = 0
+
+        def add_chunk(chunk_text, start_page, end_page):
+            # Add a chunk of text with metadata
+            chunks.append({
+                "text": chunk_text.strip(),
+                "start_page": start_page,
+                "end_page": end_page
+            })
+
+        total_pages = len(page_texts)
+        for i, (page_num, text) in enumerate(tqdm(page_texts.items(), desc="Chunking Text")):
+            sentences = self.split_into_sentences(text)
+            for sentence in sentences:
+                word_count = len(sentence.split())
+                # If adding this sentence exceeds max_words, create a new chunk
+                if total_words + word_count > max_words:
+                    add_chunk(current_chunk, current_start_page, page_num)
+                    current_chunk = sentence + " "
+                    current_start_page = page_num
+                    total_words = word_count
+                else:
+                    current_chunk += sentence + " "
+                    total_words += word_count
+            current_chunk += "\n\n"
+
+            progress = ((i + 1) / total_pages) * 100  # Calculate progress
+            update_progress(job_id, "Processing text...", progress)
+
+        # Add the last chunk if there is leftover text
+        if current_chunk.strip():
+            add_chunk(current_chunk, current_start_page, page_num)
+
+        return chunks
+
+    @staticmethod
+    def split_into_sentences(text):
+        """
+        Split the text into sentences using regular expressions.
+
+        :param text: The raw text to be split into sentences.
+        :return: A list of sentences.
+        """
+        return re.split(r'(?<=[.!?])\s+', text)
+
+    @staticmethod
+    def combine_chunks(text_chunks: List[Dict[str, Any]], visual_elements: List[Dict[str, Any]], pdf_path: str,
+                       doc_id: str) -> List[Chunk]:
+        """
+        Combine text and visual chunks into a unified list.
+
+        :param text_chunks: A list of dictionaries containing text chunks with metadata.
+        :param visual_elements: A list of dictionaries containing visual elements (tables/images) with metadata.
+        :param pdf_path: The path to the original PDF file.
+        :param doc_id: The unique document ID for this job.
+        :return: A list of Chunk objects representing the combined data.
+        """
+        combined_chunks = []
+        # Add text chunks
+        for text_chunk in text_chunks:
+            chunk_metadata: ChunkMetaData = {
+                "text": text_chunk["text"],
+                "type": "text",
+                "original_document": pdf_path,
+                "file_path": "",
+                "location": "",
+                "start_page": text_chunk["start_page"],
+                "end_page": text_chunk["end_page"],
+                "base64_data": "",
+                "doc_id": doc_id,
+            }
+            chunk_dict: Chunk = {
+                "id": str(uuid.uuid4()),  # Generate a unique ID for the chunk
+                "values": [],
+                "metadata": chunk_metadata,
+            }
+            combined_chunks.append(chunk_dict)
+
+        # Add visual chunks (tables/images)
+        for elem in visual_elements:
+            visual_chunk_metadata: ChunkMetaData = {
+                "type": elem['metadata']['type'],
+                "file_path": elem['metadata']['file_path'],
+                "text": elem['metadata'].get('text', ''),
+                "start_page": elem['metadata']['start_page'],
+                "end_page": elem['metadata']['end_page'],
+                "location": str(elem['metadata']['location']),
+                "base64_data": elem['metadata']['base64_data'],
+                "doc_id": doc_id,
+                "original_document": pdf_path,
+            }
+            visual_chunk_dict: Chunk = {
+                "id": str(uuid.uuid4()),  # Generate a unique ID for the visual chunk
+                "values": [],
+                "metadata": visual_chunk_metadata,
+            }
+            combined_chunks.append(visual_chunk_dict)
+
+        return combined_chunks
+
+    def batch_summarize_images(self, images: Dict[int, str]) -> Dict[int, str]:
+        """
+        Summarize images or tables by generating descriptive text.
+
+        :param images: A dictionary mapping image numbers to base64-encoded image data.
+        :return: A dictionary mapping image numbers to their generated summaries.
+        """
+        # Prompt for the AI model to summarize images and tables
+        prompt = f"""<instruction>
+    <task>
+        You are tasked with summarizing a series of {len(images)} images and tables for use in a RAG (Retrieval-Augmented Generation) system. 
+        Your goal is to create concise, informative summaries that capture the essential content of each image or table. 
+        These summaries will be used for embedding, so they should be descriptive and relevant. The image or table will be outlined in red on an image of the full page that it is on. Where necessary, use the context of the full page to heklp with the summary but don't summarize other content on the page.
+    </task>
+
+    <steps>
+        <step>Identify whether it's an image or a table.</step>
+        <step>Examine its content carefully.</step>
+        <step>
+            Write a detailed summary that captures the main points or visual elements:
+            <details>
+                <table>After summarizing what the table is about, include the column headers, a detailed summary of the data, and any notable data trends.</table>
+                <image>Describe the main subjects, actions, or notable features.</image>
+            </details>
+        </step>
+        <step>Focus on writing summaries that would make it easy to retrieve the content if compared to a user query using vector similarity search.</step>
+        <step>Keep summaries concise and include important words that may help with retrieval (but do not include numbers and numerical data).</step>
+    </steps>
+
+    <important_notes>
+        <note>Avoid using special characters like &amp;, &lt;, &gt;, &quot;, &apos;, $, %, etc. Instead, use their word equivalents:</note>
+        <note>Use "and" instead of &amp;.</note>
+        <note>Use "dollars" instead of $.</note>
+        <note>Use "percent" instead of %.</note>
+        <note>Refrain from using quotation marks &quot; or apostrophes &apos; unless absolutely necessary.</note>
+        <note>Ensure your output is in valid XML format.</note>
+    </important_notes>
+
+    <formatting>
+        <note>Enclose all summaries within a root element called &lt;summaries&gt;.</note>
+        <note>Use &lt;summary&gt; tags to enclose each individual summary.</note>
+        <note>Include an attribute 'number' in each &lt;summary&gt; tag to indicate the sequence, matching the provided image numbers.</note>
+        <note>Start each summary by indicating whether it's an image or a table (e.g., "This image shows..." or "The table presents...").</note>
+        <note>If an image is completely blank, leave the summary blank (e.g., &lt;summary number="3"&gt;&lt;/summary&gt;).</note>
+    </formatting>
+
+    <example>
+        <note>Do not replicate the example below—stay grounded to the content of the table or image and describe it completely and accurately.</note>
+        <output>
+            &lt;summaries&gt;
+            &lt;summary number="1"&gt;
+            The image shows two men shaking hands on stage at a formal event. The man on the left, in a dark suit and glasses, has a professional appearance, possibly an academic or business figure. The man on the right, Tim Cook, CEO of Apple, is recognizable by his silver hair and dark blue blazer. Cook holds a document titled "Tsinghua SEM EMBA," suggesting a link to Tsinghua University’s Executive MBA program. The backdrop displays English and Chinese text about business management and education, with the event dated October 23, 2014.
+            &lt;/summary&gt;
+            &lt;summary number="2"&gt;
+            The table compares the company's assets between December 30, 2023, and September 30, 2023. Key changes include an increase in cash and cash equivalents, while marketable securities had a slight rise. Accounts receivable and vendor non-trade receivables decreased. Inventories and other current assets saw minor fluctuations. Non-current assets like marketable securities slightly declined, while property, plant, and equipment remained stable. Total assets showed minimal change, holding steady at around three hundred fifty-three billion dollars.
+            &lt;/summary&gt;
+            &lt;summary number="3"&gt;
+            The table outlines the company's shareholders' equity as of December 30, 2023, versus September 30, 2023. Common stock and additional paid-in capital increased, and retained earnings shifted from a deficit to a positive figure. Accumulated other comprehensive loss decreased. Overall, total shareholders' equity rose significantly, while total liabilities and equity remained nearly unchanged at about three hundred fifty-three billion dollars.
+            &lt;/summary&gt;
+            &lt;summary number="4"&gt;
+            The table details the company's liabilities as of December 30, 2023, compared to September 30, 2023. Current liabilities decreased due to lower accounts payable and other current liabilities, while deferred revenue slightly increased. Commercial paper significantly decreased, and term debt rose modestly. Non-current liabilities were stable, with minimal changes in term debt and other non-current liabilities. Total liabilities dropped from two hundred ninety billion dollars to two hundred seventy-nine billion dollars.
+            &lt;/summary&gt;
+            &lt;summary number="5"&gt;
+            &lt;/summary&gt;
+            &lt;/summaries&gt;
+        </output>
+    </example>
+
+    <final_notes>
+        <note>Process each image or table in the order provided.</note>
+        <note>Maintain consistent formatting throughout your response.</note>
+        <note>Ensure the output is in full, valid XML format with the root &lt;summaries&gt; element and each summary being within a &lt;summary&gt; element with the summary number specified as well.</note>
+    </final_notes>
+</instruction>
+        """
+        content = []
+        for number, img in images.items():
+            content.append({"type": "text", "text": f"\nImage {number}:\n"})
+            content.append({"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": img}})
+
+        messages = [
+            {"role": "user", "content": content}
+        ]
+
+        try:
+            response = self.client.messages.create(
+                model='claude-3-5-sonnet-20240620',
+                system=prompt,
+                max_tokens=400 * len(images),  # Increased token limit for more detailed summaries
+                messages=messages,
+                temperature=0,
+                extra_headers={"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15"}
+            )
+
+            # Parse the response
+            text = response.content[0].text
+            #print(text)
+            # Attempt to parse and fix the XML if necessary
+            parser = etree.XMLParser(recover=True)
+            root = etree.fromstring(text, parser=parser)
+            # Check if there were errors corrected
+            # if parser.error_log:
+            #     #print("XML Parsing Errors:")
+            #     for error in parser.error_log:
+            #         #print(error)
+            # Extract summaries
+            summaries = {}
+            for summary in root.findall('summary'):
+                number = int(summary.get('number'))
+                content = summary.text.strip() if summary.text else ""
+                if content:  # Only include non-empty summaries
+                    summaries[number] = content
+
+            return summaries
+
+        except Exception as e:
+            # Print errors to stderr so they don't interfere with JSON output
+            print(json.dumps({"error": str(e)}), file=sys.stderr)
+            sys.stderr.flush()
+
+
+class DocumentType(Enum):
+    """
+    Enum representing different types of documents that can be processed.
+    """
+    PDF = "pdf"  # PDF file type
+    CSV = "csv"  # CSV file type
+    TXT = "txt"  # Plain text file type
+    HTML = "html"  # HTML file type
+
+
+class FileTypeNotSupportedException(Exception):
+    """
+    Exception raised when a file type is unsupported during document processing.
+    """
+
+    def __init__(self, file_extension: str):
+        """
+        Initialize the exception with the unsupported file extension.
+
+        :param file_extension: The file extension that triggered the exception.
+        """
+        self.file_extension = file_extension
+        self.message = f"File type '{file_extension}' is not supported."
+        super().__init__(self.message)  # Call the parent class constructor with the message
+
+
+class Document:
+    """
+    Represents a document being processed, such as a PDF, handling chunking, embedding, and summarization.
+    """
+
+    def __init__(self, file_path: str, file_name: str, job_id: str, output_folder: str):
+        """
+        Initialize the Document with file data, file name, and job ID.
+
+        :param file_data: The binary data of the file being processed.
+        :param file_name: The name of the file being processed.
+        :param job_id: The job ID associated with this document processing task.
+        """
+        self.output_folder = output_folder
+        self.file_name = file_name
+        self.file_path = file_path
+        self.job_id = job_id
+        self.type = self._get_document_type(file_name)  # Determine the document type (PDF, CSV, etc.)
+        self.doc_id = job_id  # Use the job ID as the document ID
+        self.chunks = []  # List to hold text and visual chunks
+        self.num_pages = 0  # Number of pages in the document (if applicable)
+        self.summary = ""  # The generated summary for the document
+        self._process()  # Start processing the document
+
+    def _process(self):
+        """
+        Process the document: extract chunks, embed them, and generate a summary.
+        """
+        with open(self.file_path, 'rb') as file:
+            pdf_data = file.read()
+        pdf_chunker = PDFChunker(output_folder=self.output_folder, doc_id=self.doc_id)  # Initialize PDFChunker
+        self.chunks = asyncio.run(pdf_chunker.chunk_pdf(pdf_data, os.path.basename(self.file_path), self.doc_id, self.job_id))  # Extract chunks
+        self.num_pages = self._get_pdf_pages(pdf_data)  # Get the number of pages in the document
+        self._embed_chunks()  # Embed the text chunks into embeddings
+        self.summary = self._generate_summary()  # Generate a summary for the document
+
+    def _get_pdf_pages(self, pdf_data: bytes) -> int:
+        """
+        Get the total number of pages in the PDF document.
+        """
+        pdf_file = io.BytesIO(pdf_data)  # Convert the file data to an in-memory binary stream
+        pdf_reader = PdfReader(pdf_file)  # Initialize PDF reader
+        return len(pdf_reader.pages)  # Return the number of pages in the PDF
+
+
+    def _get_document_type(self, file_name: str) -> DocumentType:
+        """
+        Determine the document type based on its file extension.
+
+        :param file_name: The name of the file being processed.
+        :return: The DocumentType enum value corresponding to the file extension.
+        """
+        _, extension = os.path.splitext(file_name)  # Split the file name to get the extension
+        extension = extension.lower().lstrip('.')  # Convert to lowercase and remove leading period
+        try:
+            return DocumentType(extension)  # Try to match the extension to a DocumentType
+        except ValueError:
+            raise FileTypeNotSupportedException(extension)  # Raise exception if file type is unsupported
+
+
+    def _embed_chunks(self) -> None:
+        """
+        Embed the text chunks using the Cohere API.
+        """
+        openai = OpenAI()  # Initialize Cohere client with API key
+        batch_size = 90  # Batch size for embedding
+        chunks_len = len(self.chunks)  # Total number of chunks to embed
+        for i in tqdm(range(0, chunks_len, batch_size), desc="Embedding Chunks"):
+            batch = self.chunks[i: min(i + batch_size, chunks_len)]  # Get batch of chunks
+            texts = [chunk['metadata']['text'] for chunk in batch]  # Extract text from each chunk
+            chunk_embs_batch = openai.embeddings.create(
+                model="text-embedding-3-large",
+                input=texts,
+                encoding_format="float"
+            )
+            for j, data_val in enumerate(chunk_embs_batch.data):
+                self.chunks[i + j]['values'] = data_val.embedding  # Store the embeddings in the corresponding chunks
+
+    def _generate_summary(self) -> str:
+        """
+        Generate a summary of the document using KMeans clustering and a language model.
+
+        :return: The generated summary of the document.
+        """
+        num_clusters = min(10, len(self.chunks))  # Set number of clusters for KMeans, capped at 10
+        kmeans = KMeans(n_clusters=num_clusters, random_state=42)  # Initialize KMeans with 10 clusters
+        doc_chunks = [chunk['values'] for chunk in self.chunks if 'values' in chunk]  # Extract embeddings
+        cluster_labels = kmeans.fit_predict(doc_chunks)  # Assign each chunk to a cluster
+
+        # Select representative chunks from each cluster
+        selected_chunks = []
+        for i in range(num_clusters):
+            cluster_chunks = [chunk for chunk, label in zip(self.chunks, cluster_labels) if label == i]  # Get all chunks in this cluster
+            cluster_embs = [emb for emb, label in zip(doc_chunks, cluster_labels) if label == i]  # Get embeddings for this cluster
+            centroid = kmeans.cluster_centers_[i]  # Get the centroid of the cluster
+            distances = [np.linalg.norm(np.array(emb) - centroid) for emb in cluster_embs]  # Compute distance to centroid
+            closest_chunk = cluster_chunks[np.argmin(distances)]  # Select chunk closest to the centroid
+            selected_chunks.append(closest_chunk)
+
+        # Combine selected chunks into a summary
+        combined_text = "\n\n".join([chunk['metadata']['text'] for chunk in selected_chunks])  # Concatenate chunk texts
+
+        client = OpenAI()  # Initialize OpenAI client for text generation
+        completion = client.chat.completions.create(
+            model="gpt-3.5-turbo",  # Specify the language model
+            messages=[
+                {"role": "system",
+                 "content": "You are an AI assistant tasked with summarizing a document. You are provided with important chunks from the document and provide a summary, as best you can, of what the document will contain overall. Be concise and brief with your response."},
+                {"role": "user", "content": f"""Please provide a comprehensive summary of what you think the document from which these chunks were sampled would be. 
+                    Ensure the summary captures the main ideas and key points from all provided chunks. Be concise and brief and only provide the summary in paragraph form.
+
+                    Sample text chunks:
+                    ```
+                    {combined_text}
+                    ```
+                    **********
+                    Summary:
+                    """}
+            ],
+            max_tokens=300  # Set max tokens for the summary
+        )
+        return completion.choices[0].message.content.strip()  # Return the generated summary
+
+    def to_json(self) -> str:
+        """
+        Return the document's data in JSON format.
+
+        :return: JSON string representing the document's metadata, chunks, and summary.
+        """
+        return json.dumps({
+            "file_name": self.file_name,
+            "num_pages": self.num_pages,
+            "summary": self.summary,
+            "chunks": self.chunks,
+            "type": self.type.value,
+            "doc_id": self.doc_id
+        }, indent=2)  # Convert the document's attributes to JSON format
+
+def process_document(file_path, job_id, output_folder):
+    """
+    Top-level function to process a document and return the JSON output.
+
+    :param file_path: The path to the file being processed.
+    :param job_id: The job ID for this document processing task.
+    :return: The processed document's data in JSON format.
+    """
+    new_document = Document(file_path, file_path, job_id, output_folder)
+    return new_document.to_json()
+
+def main():
+    """
+    Main entry point for the script, called with arguments from Node.js.
+    """
+    if len(sys.argv) != 4:
+        print(json.dumps({"error": "Invalid arguments"}), file=sys.stderr)
+        return
+
+    job_id = sys.argv[1]
+    file_path = sys.argv[2]
+    output_folder = sys.argv[3]  # Get the output folder from arguments
+
+    try:
+        os.makedirs(output_folder, exist_ok=True)
+        
+        # Process the document
+        document_result = process_document(file_path, job_id, output_folder)  # Pass output_folder
+
+        # Output the final result as JSON to stdout
+        print(document_result)
+        sys.stdout.flush()
+
+    except Exception as e:
+        # Print errors to stderr so they don't interfere with JSON output
+        print(json.dumps({"error": str(e)}), file=sys.stderr)
+        sys.stderr.flush()
+
+if __name__ == "__main__":
+    main()  # Execute the main function when the script is run
\ No newline at end of file