Generator
Generate text using input prompt and selected LLM model.
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.
Flows
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
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.
ChatInput
The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.
Chat Opened Trigger
The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.
Message Widget
The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.
Generator
Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.
Chat Output
Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.
Flow description
Purpose and benefits
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
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.
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).
- Users can interact with the chatbot by:
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.
- The Generator node receives:
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:
| Step | Node Type | Function |
|---|---|---|
| Chat opened | ChatOpenedTrigger | Triggers the welcome message |
| Display welcome message | MessageWidget | Shows introduction and instructions |
| Show message to user | ChatOutput | Presents the welcome message in chat |
| User inputs question / uploads image | ChatInput | Collects user text and image file |
| Process image & question | Generator | Performs OCR/visual recognition, answers query |
| Display generated answer (and image) | ChatOutput | Shows 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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