Lumina

Overview

Lumina is a multi-modal AI agent framework designed for building agents that can understand and generate content across text, images, audio, and video. It provides unified abstractions for multi-modal reasoning, enabling developers to create agents that can process and reason about diverse content types seamlessly.

Lumina's key innovation is its multi-modal attention mechanism that allows agents to maintain context across different modalities. This enables powerful use cases like analyzing charts in documents, generating visual explanations, and creating multimedia content from text prompts.

Features

• Multi-Modal Understanding: Process text, images, audio, and video in a single context
• Unified API: Single interface for all modalities
• Cross-Modal Reasoning: Reason across different content types
• Visual Generation: Generate images and diagrams from text
• Audio Processing: Speech-to-text and text-to-speech capabilities
• Video Analysis: Extract insights from video content
• Context Preservation: Maintain context across modality switches
• Extensible Architecture: Add custom modalities and processors

Installation

pip install lumina-ai

Quick Start

Multi-Modal Agent

from lumina import Agent, MultiModalContext

# Create a multi-modal agent
agent = Agent(
    model="lumina-pro",
    capabilities=["text", "image", "audio", "video"]
)

# Process multi-modal input
context = MultiModalContext()
context.add_text("Analyze this chart and explain the trends")
context.add_image("chart.png")
context.add_audio("explanation.mp3")

response = agent.generate(context)
print(response.text)
print(response.images)  # Generated visual explanation

Visual Reasoning

from lumina import Agent

agent = Agent(model="lumina-pro")

# Analyze a document with charts
response = agent.generate(
    prompt="Extract all data from these charts and create a summary",
    images=["sales_q1.png", "sales_q2.png", "sales_q3.png", "sales_q4.png"]
)

print(response.text)
print(response.data)  # Structured data extraction

Core Concepts

Modalities

Lumina supports multiple content types:

from lumina import Modality

# Available modalities
Modality.TEXT      # Text input/output
Modality.IMAGE     # Image input/output
Modality.AUDIO     # Audio input/output
Modality.VIDEO     # Video input/output
Modality.CODE      # Code input/output
Modality.DOCUMENT  # PDF, DOCX, etc.

Context

Multi-modal context management:

from lumina import MultiModalContext

context = MultiModalContext()

# Add content from different sources
context.add_text("What's in this image?")
context.add_image_from_url("https://example.com/image.jpg")
context.add_image_from_file("local.png")
context.add_audio_from_file("speech.wav")

# Context is automatically aligned
# The agent understands relationships between modalities

Models

Different models for different tasks:

# Vision-language model
vlm = Agent(model="lumina-vlm", capabilities=["text", "image"])

# Audio-language model
alm = Agent(model="lumina-audio", capabilities=["text", "audio"])

# Full multi-modal
mm = Agent(model="lumina-pro", capabilities=["text", "image", "audio", "video"])

Advanced Features

Cross-Modal Generation

# Generate images from text
response = agent.generate(
    prompt="Create a diagram showing the system architecture",
    images=[]  # Request image generation
)

# Generate audio from text
response = agent.generate(
    prompt="Read this summary aloud",
    audio=True
)

# Generate video from images + text
response = agent.generate(
    prompt="Create a slideshow from these images with narration",
    images=["slide1.png", "slide2.png", "slide3.png"],
    video=True
)

Multi-Modal RAG

from lumina import MultiModalRAG

# Index multi-modal documents
rag = MultiModalRAG(index_path="my_index")

# Query with mixed modalities
results = rag.query(
    query="Find documents about the product launch",
    image="product_photo.jpg",  # Visual similarity
    text="product launch marketing"  # Text similarity
)

for doc in results:
    print(f"Score: {doc.score}")
    print(f"Text: {doc.text[:200]}")
    if doc.image:
        print(f"Image: {doc.image.path}")

Agent with Memory

from lumina import Agent, Memory

memory = Memory(
    store="vector",
    modalities=["text", "image"]
)

agent = Agent(
    model="lumina-pro",
    memory=memory
)

# Agent remembers previous interactions
# across different modalities
response = agent.generate("What did we discuss about the design?")
# Recalls relevant images and text from memory

Examples

Document Analysis Agent

from lumina import Agent, MultiModalContext

agent = Agent(model="lumina-pro")

def analyze_document(pdf_path: str) -> dict:
    context = MultiModalContext()
    context.add_document(pdf_path)
    
    response = agent.generate(
        context=context,
        prompt="""Analyze this document:
        1. Extract key findings
        2. Identify all charts and their data
        3. Summarize recommendations
        4. Generate a visual summary"""
    )
    
    return {
        "findings": response.text,
        "charts": response.charts,
        "summary_image": response.images[0] if response.images else None
    }

Educational Tutor

from lumina import Agent, MultiModalContext

tutor = Agent(model="lumina-pro")

def explain_concept(concept: str, student_level: str = "beginner"):
    # Generate explanation with visual aids
    response = tutor.generate(
        prompt=f"Explain {concept} to a {student_level} student",
        images=[],  # Request diagrams
        audio=True  # Request audio explanation
    )
    
    return {
        "explanation": response.text,
        "diagram": response.images[0] if response.images else None,
        "audio": response.audio  # Audio file path
    }

Content Creation Pipeline

from lumina import Agent

content_agent = Agent(model="lumina-pro")

def create_blog_post(topic: str) -> dict:
    # Research and generate content
    research = content_agent.generate(
        prompt=f"Research {topic} and provide key points"
    )
    
    # Generate images for the post
    images = []
    for point in research.key_points[:3]:
        img = content_agent.generate(
            prompt=f"Create an illustration for: {point}",
            images=[]
        )
        images.append(img.images[0])
    
    # Generate audio summary
    audio = content_agent.generate(
        prompt=f"Create a 60-second audio summary about {topic}",
        audio=True
    )
    
    return {
        "text": research.text,
        "images": images,
        "audio": audio.audio
    }

Pros

• ✅ True multi-modal understanding
• ✅ Unified API for all modalities
• ✅ Cross-modal reasoning capabilities
• ✅ Visual generation from text
• ✅ Audio processing built-in
• ✅ Video analysis support
• ✅ Good documentation and examples
• ✅ Active development

Cons

• ❌ Newer framework with smaller ecosystem
• ❌ Higher compute requirements
• ❌ More expensive than text-only models
• ❌ Limited model options
• ❌ Steeper learning curve
• ❌ Python-only

When to Use

• Multi-modal applications — Need text + image + audio
• Visual reasoning tasks — Charts, diagrams, screenshots
• Content creation — Generate multimedia content
• Educational tools — Visual + audio explanations
• Document analysis — PDFs with charts and images
• Accessibility — Text-to-speech, image descriptions

Use Cases

Use Case	Why Lumina
Multi-Modal AI	Unified text, image, audio, video processing
Visual Reasoning	Analyze charts, diagrams, screenshots
Content Creation	Generate multimedia from text prompts
Educational Tools	Visual + audio explanations for learning

Comparison with Alternatives

Feature	Lumina	GPT-4V	Claude 3	LLaVA
Modalities	✅ Text+Image+Audio+Video	⚠️ Text+Image	⚠️ Text+Image	⚠️ Text+Image
Generation	✅ All modalities	⚠️ Text only	⚠️ Text only	❌ No
Cross-Modal	✅ Yes	❌ No	❌ No	❌ No
Self-Hostable	✅ Yes	❌ No	❌ No	✅ Yes
Learning Curve	Medium	Low	Low	High
Best for	Full multi-modal	Image analysis	Image analysis	Research

Best Practices

1. Use appropriate model — Match model capabilities to your modality needs
2. Structure context clearly — Label modalities when adding to context
3. Test cross-modal generation — Verify output quality for each modality
4. Optimize compute — Use smaller models for simple tasks
5. Handle async processing — Audio/video generation takes time

Troubleshooting

Issue	Solution
Image not processing	Verify image format and size limits
Audio generation fails	Check TTS provider configuration
Video analysis slow	Use frame sampling for long videos
Cross-modal alignment	Ensure context has clear modality labels

Resources

• Official Documentation
• GitHub Repository
• Model Zoo
• Examples