- What the Image & Vision Chatbot Is
- Where Vision Chat Is Available
- How Vision Chat Works
- Provider and Model Requirements
- Enabling Image Input
- Supported Image Use Cases
- Combining Vision with Personas
- Vision + Conversation Context
- Frontend vs Backend Vision Chat
- Limits, Performance, and Cost
- What the Image & Vision Chatbot Does Not Do
- Privacy and Safety Considerations
- Best Practices
- Summary
The Image & Vision Chatbot extends the Aimogen chatbot with visual understanding. It allows users to upload images directly into the chat and receive AI-generated responses based on what the image contains. This is true vision processing, not metadata reading or filename guessing.
Vision is optional and must be explicitly enabled per chatbot.
What the Image & Vision Chatbot Is #
The Image & Vision Chatbot allows the AI to:
- receive images from users
- analyze visual content
- describe scenes, objects, or layouts
- answer questions about the image
- combine visual input with conversation context
Images become part of the conversation, not separate uploads.
Where Vision Chat Is Available #
Image and vision support can be enabled for:
- frontend chatbots
- backend chatbot (Playground)
Each chatbot decides independently whether image input is allowed.
How Vision Chat Works #
The vision flow is straightforward:
- the user uploads or attaches an image in chat
- the image is sent to a vision-capable AI model
- the model analyzes the image
- the response is generated using both:
- the image
- the conversation context
The chatbot can reference the image naturally in its reply.
Provider and Model Requirements #
Vision chat requires:
- a provider that supports image input
- a vision-capable model
Not all models support vision. If a non-vision model is selected, image uploads will either be disabled or ignored, depending on configuration.
Vision capability is model-specific, not global.
Enabling Image Input #
Image & vision features are enabled per chatbot.
You typically:
- enable image uploads in chatbot settings
- select a vision-capable model
- save the chatbot configuration
If image uploads are disabled, users cannot send images.
Supported Image Use Cases #
Vision chat works well for:
- explaining screenshots
- identifying objects
- commenting on photos
- analyzing UI layouts
- reviewing designs
- answering questions about diagrams
- visual troubleshooting
It is conversational, not forensic or scientific analysis.
Combining Vision with Personas #
Vision responses are still governed by the chatbot persona.
This means:
- tone and style rules still apply
- constraints still apply
- the chatbot stays in its role
A support persona will describe images differently than a creative or educational persona, even when using the same model.
Vision + Conversation Context #
Images are not treated as one-off inputs.
The chatbot can:
- reference previously uploaded images
- answer follow-up questions about the same image
- combine visual analysis with text context
How long image context is retained depends on provider and conversation settings.
Frontend vs Backend Vision Chat #
On the frontend:
- users can upload images directly
- UI controls manage image selection
- privacy and consent rules may apply
In the backend (Playground):
- vision is used for testing and experimentation
- no frontend visibility rules apply
- useful for validating image understanding
Limits, Performance, and Cost #
Vision requests:
- consume more tokens than text-only chat
- are slower than text-only responses
- are more expensive on most providers
Usage limits and logging still apply.
Large images or frequent uploads can significantly increase API usage.
What the Image & Vision Chatbot Does Not Do #
It does not:
- edit images
- generate images
- perform OCR unless the model supports it
- guarantee accurate identification
- replace professional image analysis
- store images permanently unless configured elsewhere
It analyzes images only for the purpose of generating responses.
Privacy and Safety Considerations #
You should:
- inform users that images are sent to AI providers
- avoid allowing sensitive or personal images
- configure consent where required
- review provider image data policies
Aimogen does not anonymize images automatically.
Best Practices #
- use clear persona constraints
- choose models explicitly marked as vision-capable
- limit image size where possible
- test with real-world images
- combine vision with concise follow-up questions
Vision works best when users ask specific questions about the image.
Summary #
The Image & Vision Chatbot allows users to upload images into chat and receive AI responses based on visual understanding. Enabled per chatbot and dependent on vision-capable models, it integrates seamlessly into conversational context while respecting personas, limits, and privacy rules. It is ideal for visual assistance, troubleshooting, and explanation—not for image editing or guaranteed analysis accuracy.
