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Multimodal AI in B-Bot Hub

Multimodal AI goes beyond text-only interactions, enabling experts to process and generate multiple types of content including voice, images, documents, and structured data.

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What is Multimodal AI?

Traditional AI chatbots handle only text. Multimodal AI can:

Understand

Process text, voice, images, and files

Generate

Create text, voice, images, and code

Combine

Work with multiple modalities simultaneously

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Supported Modalities

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Input Modalities

What your expert can receive:
  • Text
  • Voice
  • Images
  • Files
Traditional text input
  • Typed messages
  • Pasted content
  • Formatted text
  • Code snippets
  • URLs
Use for:
  • Questions and instructions
  • Code review
  • Text editing
  • General conversation

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Output Modalities

What your expert can produce:
  • Text
  • Voice/Audio
  • Images
  • Files
Written responses
  • Natural language
  • Formatted markdown
  • Code blocks
  • Lists and tables
  • Links and citations
Streaming: Real-time generation

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Multimodal Combinations

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Common Patterns

Input: Voice question Output: Text + Audio responseExample:
User: [Voice] "What's the weather today?"
Expert: [Text + Audio] "Today's weather is sunny..."
Use cases:
  • Hands-free interaction
  • Accessibility
  • Voice assistants
  • Mobile apps
Input: Image + Text question Output: Text analysisExample:
User: [Image] chart.png
User: [Text] "What trends do you see?"
Expert: [Text] "The chart shows an upward trend..."
Use cases:
  • Image analysis
  • OCR and text extraction
  • Visual Q&A
  • Diagram explanation
Input: Document/Data file Output: Processed filesExample:
User: [File] data.csv
User: [Text] "Analyze and create a report"
Expert: Creates:
  - analysis.py
  - cleaned_data.csv
  - report.md
  - charts.png
Use cases:
  • Data processing
  • Report generation
  • Code generation
  • Document transformation
Input: Multiple types Output: Multiple typesExample:
User: [Voice] "Make a landing page"
User: [Image] sketch.jpg
User: [File] brand_colors.json
Expert: Creates:
  - index.html [File]
  - styles.css [File]
  - preview.png [Image]
  - explanation [Text + Audio]
Use cases:
  • Complex projects
  • Rich interactions
  • Full workflows
  • Creative work

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Technical Architecture

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Input Processing Pipeline

User Input
    ↓
┌──────────────────┐
│ Modality Router  │ Detect input type(s)
└────────┬─────────┘
         ↓
   ┌────┴────┐
   │         │
[Text]   [Voice]   [Image]   [File]
   │         │         │         │
   │    Whisper    Vision    Parser
   │      API      Model      API
   │         │         │         │
   └────┬────┴────┬────┴────┬────┘
        │         │         │
    ┌───┴─────────┴─────────┴───┐
    │   Unified Representation   │
    └──────────────┬──────────────┘
                   ↓
              LLM Processing

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Output Generation Pipeline

LLM Response
    ↓
┌──────────────────┐
│ Output Analyzer  │ Determine what to generate
└────────┬─────────┘
         ↓
   ┌────┴────┐
   │         │
[Text]   [Audio]   [Image]   [Files]
   │         │         │         │
Stream    TTS API   DALL-E   Workspace
   │         │         │         │
   └────┬────┴────┬────┴────┬────┘
        │         │         │
    ┌───┴─────────┴─────────┴───┐
    │    Delivery to User        │
    └────────────────────────────┘

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Configuration

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Setting Up Modalities

1

Configure Providers

Set up API keys for voice and image providersSee: Provider Keys Management
2

Configure Input

Enable voice input and file uploadsSee: Input Modalities
3

Configure Output

Set up TTS and image generationSee: Output Modalities
4

Test

Try different combinations to ensure everything works

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Per-Expert Configuration

Each expert can have different modality settings: 
Expert 1: "Voice Assistant"
├── Input: Voice (Whisper)
└── Output: Audio (ElevenLabs)

Expert 2: "Data Analyst"
├── Input: Text + Files (CSV, Excel)
└── Output: Text + Files (Reports, Charts)

Expert 3: "Creative Designer"
├── Input: Text + Images
└── Output: Text + Images (Generated art)

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Best Practices

Choose Right Modalities

Match modality to use case:
  • Customer service → Voice I/O
  • Data analysis → File I/O
  • Creative work → Image I/O
  • General chat → Text
  • Accessibility → Voice I/O

Cost Management

Understand costs:
  • Voice: ~$0.006/minute (Whisper)
  • TTS: ~$15/1M chars (OpenAI)
  • Images: ~$0.04/image (DALL-E 3)
  • Text: ~$0.01-0.06/1K tokens
Optimize:
  • Use appropriate quality
  • Consider browser native for voice
  • Batch operations
  • Cache when possible

User Experience

Enhance UX:
  • Provide clear modality indicators
  • Show processing states
  • Enable/disable as needed
  • Offer quality settings
  • Test across devices

Error Handling

Handle failures gracefully:
  • Voice: Fall back to text
  • Image: Provide text alternative
  • Files: Show error messages
  • Network: Retry logic
  • Permissions: Clear instructions

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Use Cases by Industry

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Healthcare

  • Voice: Medical dictation
  • Image: X-ray/scan analysis
  • Files: Patient record processing
  • Output: Audio instructions for patients

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Education

  • Voice: Language learning
  • Image: Diagram explanations
  • Files: Assignment grading
  • Output: Audio lessons

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E-commerce

  • Voice: Product search
  • Image: Visual product search
  • Files: Inventory data
  • Output: Product recommendations

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Customer Support

  • Voice: Phone support automation
  • Image: Screenshot troubleshooting
  • Files: Log file analysis
  • Output: Audio responses

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Future Directions

Video Understanding

Processing and analyzing video content

Real-time Translation

Live translation across modalities

3D Generation

Creating 3D models and environments

Multimodal RAG

Retrieval across all content types

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Next Steps

Configure Modalities

Set up input and output modalities

Provider Keys

Add API keys for modality providers

AI Image Generation

Generate profile pictures and images

Chat Features

Use multimodal features in chat