Image Q&A Chatbot

A chatbot that lets users upload images and ask questions about their content. It uses OCR and visual recognition to analyze the image and provides relevant answers through an interactive chat interface.

How the AI Flow works - Image Q&A Chatbot

How the AI Flow works

User Opens Chat

The chat interface is opened, triggering a welcome message for the user.

User Uploads Image or Sends Message

User submits an image and/or a question via the chat input.

Image and Question Processed

The system receives the image and question, and prepares them for analysis.

Content Analyzed with OCR & Visual Recognition

The uploaded image and question are analyzed with AI and OCR to extract relevant information.

Answers Delivered in Chat

The chatbot replies to the user with answers about the image in the chat interface.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

Flow description

Purpose and benefits

Workflow Description: Question Answering from Image

Overview

This workflow implements a chatbot that enables users to upload an image and ask questions about its content. Using a combination of Optical Character Recognition (OCR) and visual recognition technologies, the chatbot analyzes the image and provides accurate, context-sensitive answers. This automation is highly valuable for scaling tasks where users need to extract information from images or interact with visual data conversationally.

Step-by-Step Flow

  1. Chat Initialization

    • When the chat session is opened, the workflow triggers a welcome message using the Message Widget.
    • The message introduces users to the chatbot’s capabilities, explaining that they can upload images and ask questions about the content.

  2. User Input Handling

    • Users can interact with the chatbot by:
      • Typing a question about an image.
      • Uploading an image file.
    • The Chat Input node captures both the question (text message) and the uploaded image (file input).

  3. Image and Question Processing

    • The Generator node receives:
      • The uploaded image (for OCR/visual recognition).
      • The user’s question (as context for the large language model).
    • The generator analyzes the image, extracts information (e.g., text via OCR or visual features), and formulates a relevant answer to the question.

  4. Response Delivery

    • The answer generated by the model is routed to a Chat Output node, which displays the response to the user in the chat interface.
    • If an image was uploaded, it can also be displayed in the chat for reference.

Workflow Structure

Here’s a simplified structure of the workflow:

StepNode TypeFunction
Chat openedChatOpenedTriggerTriggers the welcome message
Display welcome messageMessageWidgetShows introduction and instructions
Show message to userChatOutputPresents the welcome message in chat
User inputs question / uploads imageChatInputCollects user text and image file
Process image & questionGeneratorPerforms OCR/visual recognition, answers query
Display generated answer (and image)ChatOutputShows the answer (and possibly image) to user

Benefits and Use Cases

  • Automation & Scalability: This workflow automates the process of extracting information from images, enabling rapid and consistent answers to visual questions without human intervention.
  • Versatility: Useful for customer support, educational tools, document analysis, and any scenario where users need to query or understand images.
  • Enhanced User Experience: Provides a conversational interface, making it easy and intuitive for users to interact with complex image analysis tools.
  • Seamless Integration: The modular node-based design allows for future expansion or integration of more advanced recognition models.

Example Use Cases

  • Document Digitization: Users upload pictures of documents and ask for summaries or specific details.
  • Product Support: Customers send images of products and inquire about specifications or issues.
  • Educational Tools: Students upload diagrams or charts and ask explanatory questions.

By automating visual question answering with this workflow, organizations can make powerful image analysis tools accessible to a broad audience, reduce manual effort, and deliver faster, smarter responses at scale.

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