Resource Library > Demo Library > ChatGPT-like Chatbot on Image-based Data

ChatGPT-like Chatbot on Image-based Data

Applicable Industries

  • Manufacturing
  • Engineering & Technical Services
  • Legal & Compliance
  • Healthcare
  • Finance & Insurance

Technologies Used & Their Role

  • Document Parsing: PyMuPDF, PDFMiner, LangChain
  • OCR & Text Extraction: Tesseract OCR, EasyOCR, LayoutLMv3
  • Diagram & Flowchart Processing: OpenCV, Detectron2, YOLOv8
  • Vector Search for RAG: ChromaDB, Pinecone, FAISS
  • LLM for Conversational AI: OpenAI GPT-4, LlamaIndex, Hugging Face Transformers
  • Data Storage & Processing: Snowflake, PostgreSQL, MongoDB
  • API & Deployment: FastAPI, Docker, Kubernetes
  • Monitoring & Feedback Loop: Prometheus, Grafana, MLflow

Summary of the AI Solution

Enterprises rely heavily on technical documents, flowcharts, schematics, and scanned reports for decision-making. However, most AI-driven chatbots focus only on text-based documents and fail to interpret image-heavy content. 

We have developed this AI-powered ChatGPT-like chatbot for an engineering company. This conversational AI assistant is capable of understanding, interpreting, and responding to queries based on diagrams, flowcharts, charts, and scanned PDFs. This chatbot integrates computer vision, OCR, and LLM-based Retrieval-Augmented Generation (RAG) to extract insights from both textual and visual information within documents. 

Problem Statement

Many organizations, especially in engineering, manufacturing, healthcare, finance, and R&D, work extensively with image-based documents such as: 

  • Technical drawings 
  • Electrical schematics 
  • Flowcharts 
  • Handwritten prescriptions 
  • Blueprints 
  • Annotated PDFs and scanned documents 

Traditional document retrieval systems and chatbots fail to understand or process non-text content, leading to: 

  • Inefficiencies: Users manually search for relevant details in large technical documents. 
  • Misinterpretation: Important insights hidden in diagrams and charts are ignored by standard AI models. 
  • Limited automation: Chatbots primarily rely on textual data and struggle to provide context-aware responses. 

Our solution bridges this gap by enabling AI to interpret images, recognize patterns in diagrams, and extract text from complex visual structures, making document-based conversational AI more accurate and useful. 

Solution Approach

To develop a ChatGPT-like chatbot for image-based data, we designed a hybrid AI system integrating: 

  1. Document Parsing & Image Segmentation: Breaking down complex PDFs, scanned reports, and flowcharts into structured components. 
  2. Text Extraction & NLP: Using OCR and LayoutLM to extract textual information from images, scanned text, and handwritten notes. 
  3. Diagram & Flowchart Understanding: Applying computer vision and deep learning models to interpret shapes, relationships, and connections in engineering drawings and business flowcharts. 
  4. RAG-based Conversational AI: Implementing Retrieval-Augmented Generation (RAG) with a vector database to provide context-aware responses to user queries. 
  5. Query Understanding & Response Generation: Using LLMs (like GPT-4) fine-tuned with domain-specific data to generate intelligent and accurate answers. 

Our Chatbot System Workflow for Image Based Data

Key Benefits & Value Proposition

  •  Understands Image-Based Documents Extracts insights from diagrams, flowcharts, and complex reports.
  • Domain-Specific Customization Fine-tuned for engineering, healthcare, manufacturing, and R&D industries.
  • Faster Document Insights Reduces manual searching time by 80%, improving decision-making speed.
  • Seamless Integration Works with existing document management systems, cloud storage, and enterprise databases.
  • Multi-Modal AI Approach Combines text, image, and vector-based retrieval for superior accuracy. 

Request a Demo to Watch It Live in Action and Try It on Your Datasets.

CONTACT US