Vision & Multimodal

SAGEA's vision capabilities provide intelligent visual understanding that seamlessly integrates with voice and language models for comprehensive multimodal AI experiences.

Overview

Our vision models can analyze images, understand scenes, and provide detailed descriptions while integrating with SAGEA's voice and language capabilities for truly multimodal interactions.

Key Capabilities

👁️ Image Analysis

Comprehensive visual understanding and detailed descriptions

🎬 Video Processing

Real-time video analysis and temporal understanding

🔗 Multimodal Integration

Seamless combination of vision, voice, and language

🎯 Scene Understanding

Context-aware visual reasoning and interpretation

Quick Start

import sagea
 
# Initialize vision client
client = sagea.VisionClient(api_key="your-api-key")
 
# Analyze an image
response = client.analyze(
    image_url="https://example.com/image.jpg",
    prompt="What's happening in this image?"
)
 
print(response.description)

Available Models

SAGEA-Vision

General-purpose visual understanding:

Image analysis: Detailed scene understanding and object detection
Text extraction: OCR and document analysis capabilities
Visual reasoning: Answer questions about image content

SAGEA-Multimodal

Advanced multimodal reasoning:

Cross-modal understanding: Combine vision with text and audio
Complex reasoning: Advanced logical reasoning across modalities
Creative generation: Generate content based on visual inputs

Features

Image Understanding

Comprehensive analysis of visual content:

# Detailed image analysis
response = client.analyze(
    image_path="./photo.jpg",
    prompt="Describe this image in detail, including objects, people, and setting",
    model="sagea-vision"
)

Document Processing

Extract and understand text from documents:

# OCR and document analysis
response = client.extract_text(
    image_path="./document.pdf",
    output_format="structured"
)

Video Analysis

Process video content frame by frame:

# Video understanding
response = client.analyze_video(
    video_path="./video.mp4",
    prompt="Summarize the key events in this video",
    sample_rate=1  # Analyze every second
)

Multimodal Conversations

Combine vision with chat for rich interactions:

# Multimodal chat
response = client.multimodal_chat(
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {"type": "image", "image_url": "https://example.com/photo.jpg"}
            ]
        }
    ]
)