Chat with Graphic PDFs: Understand How AI PDF Summarizers Work

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Chat with Graphic PDFs: Understand How AI PDF Summarizers Work

Have you ever struggled to extract meaningful information from PDFs (Portable Document Formats) filled with complex layouts, images, and tables? Traditional PDF processing tools often fall short when dealing with visually rich documents. But what if we could build an AI (Artificial Intelligence) system that not only understands the text but also comprehends the visual elements, allowing us to have natural conversations about any PDF?

Welcome to the 1st of a 2-part series on the Vision-Language RAG pipeline, where we aim to explore and implement a powerful multimodal, chat-based RAG (Retrieval-Augmented Generation) pipeline for PDF analysis using the ColPali and LLaVA (Large Language and Vision Assistant) models.

In this tutorial, we will provide an overview of key concepts and terminologies highlighted in bold above. These foundational insights will set the stage for understanding how these technologies work together seamlessly.

In the 2nd tutorial, we will dive straight into implementation. By the end of this series, we will have a comprehensive understanding of the theory and practice behind creating a robust multimodal question-answering RAG system for analyzing PDFs.

Ready to get started? Let’s begin by building a strong conceptual foundation in this first tutorial.

This lesson is the 1st of a 2-part series on the Vision-Language RAG Pipeline:

1. Chat with Graphic PDFs: Understand How AI PDF Summarizers Work (this tutorial)
2. Chat with Graphic PDFs: Building an AI PDF Summarizer

To learn how to build a conceptual understanding of Vision-Language RAG pipelines for PDF analysis, just keep reading.

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The Challenge of Processing Complex PDFs

Traditional PDF processing pipelines face numerous challenges that can make working with complex documents a nightmare. Let’s break down these challenges with the following real-world examples.

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Layout Complexity

Imagine trying to process a financial report with multiple columns, embedded charts, and footnotes. Traditional OCR (Optical Character Recognition) systems might read the text in the wrong order, mixing up column contents and creating nonsensical output. A typical scenario might involve a two-column research paper where the OCR reads across columns instead of down each column, completely distorting the meaning of the text.

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Table and Figure Recognition

Consider a research paper with complex tables containing merged cells and mathematical notation. Standard PDF processors often struggle to maintain the structural relationships between cells, turning organized data into a confusing stream of text. Figures present another challenge — captions might be separated from their images, and important visual information gets lost in translation.

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Mathematical and Special Characters

Scientific papers and technical documents often contain equations, special symbols, and domain-specific notation. Traditional systems frequently misinterpret these elements, turning a simple equation into gibberish or losing critical technical information.

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Enter the World of Multimodal Models

Imagine having an AI assistant that can see, read, and understand documents just like a human would. This is where multimodal AI comes into play. Unlike traditional AI systems that work with only one type of data (e.g., text or images), multimodal systems can process multiple data types simultaneously, creating a more comprehensive understanding of the content.

Think of it like this: when you read a scientific paper, you don’t just read the text in isolation. You look at the graphs, examine the tables, and connect the visual information with the written content. Multimodal AI aims to replicate this natural way of processing information.

For instance, a multimodal system can:

• Analyze text embedded within images.
• Interpret visual scenes to answer questions.

This capability makes multimodal models exceptionally powerful for tasks (e.g., visual question answering (VQA), image captioning, and document understanding), where synthesizing different types of data is critical.

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The Power of RAG

At the heart of our project lies a sophisticated technology called Retrieval-Augmented Generation (RAG). Think of RAG as an AI system with a photographic memory and the ability to have meaningful conversations about what it remembers.

It combines the power of information retrieval and text generation to handle complex queries. It is designed to enhance the performance of generative models by providing them with highly relevant context retrieved from a large database or knowledge base. This approach is particularly effective in tasks where the model’s built-in knowledge might be insufficient or outdated.

Here’s how it works:

• When you upload a PDF, the system first analyzes and indexes all the content
• When you ask a question, it searches through this indexed information to find relevant details
• It then uses this retrieved context to generate accurate, informed responses

This approach ensures that the AI’s responses are always grounded in the actual content of your documents rather than relying on potentially outdated or incorrect pre-trained knowledge.

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Key Components of a RAG Pipeline

Retriever: The retriever acts as the first step in the pipeline. It searches a structured or unstructured knowledge base to find the most relevant pieces of information related to a user query. Typically, dense vector embeddings and similarity search algorithms (e.g., FAISS (Facebook AI Similarity Search) or BM25) are used for this purpose.

• Dense Retriever: Encodes the query and documents into embeddings for similarity matching.
• Sparse Retriever: Uses term frequency techniques (e.g., TF-IDF (term frequency-inverse document frequency)) for keyword-based search.

Generator: The generator is a pre-trained language model (e.g., GPT (Generative Pre-trained Transformer), T5 (Text-to-Text Transfer Transformer)) that takes the retrieved information as input and generates coherent and contextually accurate responses. The inclusion of external context ensures the responses are not only fluent but also grounded in the most relevant knowledge available.

In our pipeline, ColPali acts as the retriever, efficiently locating relevant content within graphic-rich documents, while LLaVA serves as the generator, producing detailed and context-aware textual responses.

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Why Choose ColPali as the Retriever?

Recently, pretrained language models have significantly advanced text embedding models, enabling better semantic understanding for tasks (e.g., document retrieval). However, in industrial applications, the main bottleneck in efficient document retrieval often lies in the data ingestion pipeline rather than the embedding model’s performance.

Traditional document retrieval techniques typically involve complex preprocessing steps that can hinder performance and scalability:

• PDF Parsing and OCR: Extracting text from documents requires robust PDF parsers or Optical Character Recognition (OCR) systems. These systems often struggle with complex layouts or noisy inputs.
• Layout Detection: Segmenting documents into meaningful sections (e.g., paragraphs, titles, tables, or images) requires layout detection models that can handle diverse formatting styles.
• Chunking Strategy: Defining how to group text passages for semantic coherence is another critical step. Inefficient chunking can lead to incomplete or irrelevant retrieval results.
• Captioning for Visual Elements: For visually rich documents, adding captions to describe tables, figures, and images in natural language is essential for embedding models to understand the content effectively.

Optimizing this pipeline is crucial for extracting meaningful data that aligns with the capabilities of advanced retrieval systems.

ColPali addresses these challenges by streamlining the data ingestion pipeline, enabling efficient document retrieval for visually rich and complex inputs.

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What Is ColPali?

ColPali (Faysse et al., 2025) is an advanced document retrieval model designed to efficiently index and retrieve information from documents by leveraging Vision-Language Models (VLMs). It builds on recent advancements in VLMs, combining the strengths of Large Language Models (LLMs) and Vision Transformers (ViTs) to process textual and visual data in documents seamlessly. This capability makes ColPali a powerful tool for multimodal document understanding and retrieval tasks.

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Key Features of ColPali

• Integrated Vision-Language Models (VLMs)
  • ColPali uses VLMs (e.g., PaliGemma), trained on extensive datasets containing text, images, and layouts.
  • It maps visual features into a latent space aligned with textual embeddings, ensuring meaningful integration of visual and textual content.
• Enhanced Contextual Understanding
  • Unlike traditional OCR systems, ColPali analyzes the entire document layout.
  • It identifies relationships between tables, figures, and surrounding text, enabling a deeper understanding of document content.
• Dynamic Retrieval-Augmented Generation (RAG)
  • ColPali seamlessly integrates into RAG frameworks, allowing for real-time retrieval and response generation.
  • It dynamically retrieves context-relevant information, ensuring responses are accurate and contextually rich.
• Efficient Indexing and Querying
  • Eliminates the need for complex preprocessing steps (e.g., manual chunking or OCR), simplifying the ingestion pipeline.
  • Maintains low latency during querying, making it suitable for real-time applications.

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How Does ColPali Work?

• Offline Indexing Phase
  • Document pages are processed by a vision encoder (e.g., SigLIP (Sigmoid loss for Language-Image Pre-training)) to generate image patch embeddings.
  • These embeddings are passed through a language model (e.g., Gemma-2B) to align visual and textual features.
  • A projection layer maps the embeddings into a lower-dimensional space, creating a multi-vector representation for each document page.
• Online Querying Phase
  • User queries are encoded into token embeddings using the same language model.
  • A late interaction mechanism, inspired by ColBERT, matches query tokens with document embeddings to compute similarity scores.
  • The system retrieves the most relevant documents based on these scores, ensuring both efficiency and accuracy.

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Why ColPali Is Unique

• Eliminates Traditional Bottlenecks: Traditional systems require multiple preprocessing steps (e.g., OCR and manual chunking), which are both time-consuming and error-prone. ColPali bypasses these steps by operating directly on document images.
• Late Interaction Mechanism: Inspired by ColBERT, ColPali uses a late interaction technique that compares query and document vectors at search time. This allows for computational efficiency without sacrificing accuracy.
• Multimodal Compatibility: Handles various document elements (e.g., text, tables, images, and figures) through integrated vision and language processing.
• End-to-End Trainability: The entire ColPali system is trainable as a unified framework, optimizing both retrieval accuracy and efficiency.

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Performance and Benchmarking

To evaluate ColPali’s capabilities, researchers developed a new benchmark named ViDoRe (Visual Document Retrieval). This extensive benchmark encompasses a diverse set of tasks across multiple domains, modalities, and languages, providing a robust evaluation framework.

ColPali delivers superior performance compared to other approaches (Table 1), showcasing its remarkable proficiency in multimodal document retrieval tasks.

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What Is LLaVA?

LLaVA (Large Language and Vision Assistant) (Liu et al., 2023) represents a significant leap forward in the multimodal AI landscape. This end-to-end trained model combines a powerful visual encoder and language model to process and respond to both images and text. Designed for tasks requiring deep understanding and interaction across visual and textual domains, LLaVA excels in visual question answering (VQA), descriptive text generation, and complex reasoning.

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Bridging Visual and Textual Domains

Inputs and Outputs of LLaVA

LLaVA processes two primary inputs:

• Visual Input: Images analyzed for features, objects, and context.
• Textual Instructions: Questions or commands that direct the model’s attention to specific tasks.

The outputs are versatile and include:

• Descriptive Text: Detailed descriptions of images, identifying objects and actions.
• Answers to Questions: Precise responses to questions about the visual content.
• Complex Reasoning: In-depth explanations requiring logical inference.

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How LLaVA Stands Out

LLaVA distinguishes itself from models like CLIP (Contrastive Language-Image Pre-training) and BLIP (Bootstrapping Language-Image Pre-training) by leveraging GPT-4 for data curation, enabling a more robust and human-like interaction. While CLIP pioneered multimodal AI with strong visual-text alignment and BLIP enhanced precise recognition, LLaVA excels in reasoning and conversational abilities.

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What Makes LLaVA Unique

LLaVA’s innovation lies in its dynamic data curation. Instead of relying on static datasets, it uses GPT-4 to generate instruction-following data across diverse scenarios. This method brings the model closer to replicating real-world intelligence.

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Data Curation in LLaVA

Data preparation in LLaVA is a three-tiered process:

• Conversational Data: Curating dialogues for interaction-focused tasks.
• Detailed Descriptions: Promoting comprehensive image understanding.
• Complex Reasoning: Training on questions requiring layered logic.

This unique approach enriches the model’s training, ensuring it performs exceptionally well across descriptive, interactive, and inferential tasks.

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Architecture of LLaVA

The LLaVA model integrates:

• Vision Encoder: A pre-trained CLIP visual encoder (ViT-L/14), extracting features from images.
• Language Model: Vicuna, a large language model for robust text generation.
• Linear Projection: Aligning visual features with the language model’s embedding space.

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Training Process

LLaVA’s training comprises two stages:

• Feature Alignment: Aligning visual and textual features.
• Fine-Tuning: Enhancing multimodal capabilities for specific use cases (e.g., VQA and scientific Q&A).

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Performance and Benchmarking

LLaVA’s capabilities are evaluated using LLaVA-Bench, achieving superior results compared to BLIP-2 and OpenFlamingo:

• Descriptive Tasks: 52.5% accuracy in generating detailed image descriptions.
• Conversational Accuracy: 57.3% in interactive tasks.
• Complex Reasoning: Leading with 81.7%, showcasing its advanced reasoning abilities.

Its overall score of 67.3% surpasses competitors by a significant margin, affirming its multimodal prowess.

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Summary

In this tutorial, we covered the essential concepts for understanding the technologies powering multimodal AI solutions.

In the 2nd part of this series, we’ll move beyond theory and dive into the implementation of this multimodal RAG pipeline. By the end of the next tutorial, you’ll be equipped with the practical knowledge to build your own AI solution capable of interacting with graphic-rich documents.

Stay tuned!

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Citation Information

Thakur, P. “Chat with Graphic PDFs: Understand How AI PDF Summarizers Work,” PyImageSearch, P. Chugh, S. Huot, and G. Kudriavtsev, eds., 2025, https://pyimg.co/of6yx

@incollection{Thakur_2025_chat-w-graphic-pdfs-understand-ai-pdf-summarizers,
  author = {Piyush Thakur},
  title = {{Chat with Graphic PDFs: Understand How AI PDF Summarizers Work}},
  booktitle = {PyImageSearch},
  editor = {Puneet Chugh and Susan Huot and Georgii Kudriavtsev},
  year = {2025},
  url = {https://pyimg.co/of6yx},
}

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