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videodb
VideoDB Documentation
  • Pages
    • Welcome to VideoDB Docs
    • Quick Start Guide
      • Video Indexing Guide
      • Semantic Search
      • How Accurate is Your Search?
      • Collections
      • Public Collections
      • Callback Details
      • Ref: Subtitle Styles
      • Language Support
      • Guide: Subtitles
    • Examples and Tutorials
      • Dubbing - Replace Soundtrack with New Audio
      • icon picker
        VideoDB x TwelveLabs: Real-Time Video Understanding
      • Beep curse words in real-time
      • Remove Unwanted Content from videos
      • Instant Clips of Your Favorite Characters
      • Insert Dynamic Ads in real-time
      • Adding Brand Elements with VideoDB
      • Eleven Labs x VideoDB: Adding AI Generated voiceovers to silent footage
      • Elevating Trailers with Automated Narration
      • Add Intro/Outro to Videos
      • Audio overlay + Video + Timeline
      • Building Dynamic Video Streams with VideoDB: Integrating Custom Data and APIs
      • AI Generated Ad Films for Product Videography: Wellsaid, Open AI & VideoDB
      • Fun with Keyword Search
      • AWS Rekognition and VideoDB - Effortlessly Remove Inappropriate Content from Video
      • Overlay a Word-Counter on Video Stream
      • Generate Automated Video Outputs with Text Prompts | DALL-E + ElevenLabs + OpenAI + VideoDB
    • Visual Search and Indexing
      • Scene Extraction Algorithms
      • Custom Annotations
      • Scene-Level Metadata: Smarter Video Search & Retrieval
      • Advanced Visual Search Pipelines
      • Playground for Scene Extractions
      • Deep Dive into Prompt Engineering : Mastering Video Scene Indexing
    • Multimodal Search
      • Multimodal Search: Quickstart
      • Conference Slide Scraper with VideoDB
    • Real‑Time Video Pipeline
    • Meeting Recording SDK
    • Generative Media Quickstart
      • Generative Media Pricing
    • Realtime Video Editor SDK
      • Fit & Position: Aspect Ratio Control
      • Trimming vs Timing: Two Independent Timelines
      • Advanced Clip Control: The Composition Layer
      • Caption & Subtitles: Auto-Generated Speech Synchronization
      • Notebooks
    • Transcoding Quickstart
    • director-light
      Director - Video Agent Framework
      • Agent Creation Playbook
      • How I Built a CRM-integrated Sales Assistant Agent in 1 Hour
      • Make Your Video Sound Studio Quality with Voice Cloning
      • Setup Director Locally
    • github
      Open Source Tools
      • llama
        LlamaIndex VideoDB Retriever
      • PromptClip: Use Power of LLM to Create Clips
      • StreamRAG: Connect ChatGPT to VideoDB
    • zapier
      Zapier Integration
      • Auto-Dub Videos & Save to Google Drive
      • Create & Add Intelligent Video Highlights to Notion
      • Create GenAI Video Engine - Notion Ideas to Youtube
      • Automatically Detect Profanity in Videos with AI - Update on Slack
      • Generate and Store YouTube Video Summaries in Notion
      • Automate Subtitle Generation for Video Libraries
      • Solve customers queries with Video Answers
    • n8n
      N8N Workflows
      • AI-Powered Meeting Intelligence: Recording to Insights Automation
      • AI Powered Dubbing Workflow for Video Content
      • Automate Subtitle Generation for Video Libraries
      • Automate Interview Evaluations with AI
      • Turn Meeting Recordings into Actionable Summaries
      • Auto-Sync Sales Calls to HubSpot CRM with AI
      • Instant Notion Summaries for Your Youtube Playlist
    • mcp
      VideoDB MCP Server
    • Edge of Knowledge
      • Building Intelligent Machines
        • Part 1 - Define Intelligence
        • Part 2 - Observe and Respond
        • Part 3 - Training a Model
      • Society of Machines
        • Society of Machines
        • Autonomy - Do we have the choice?
        • Emergence - An Intelligence of the collective
      • From Language Models to World Models: The Next Frontier in AI
      • The Future Series
      • How VideoDB Solves Complex Visual Analysis Tasks
    • videodb
      Building World's First Video Database
      • Multimedia: From MP3/MP4 to the Future with VideoDB
      • Dynamic Video Streams
      • Why do we need a Video Database Now?
      • What's a Video Database ?
      • Enhancing AI-Driven Multimedia Applications
      • Misalignment of Today's Web
      • Beyond Traditional Video Infrastructure
      • Research Grants
    • Customer Love
    • Team
      • videodb
        Internship: Build the Future of AI-Powered Video Infrastructure
      • Ashutosh Trivedi
        • Playlists
        • Talks - Solving Logical Puzzles with Natural Language Processing - PyCon India 2015
      • Ashish
      • Shivani Desai
      • Gaurav Tyagi
      • Rohit Garg
      • VideoDB Acquires Devzery: Expanding Our AI Infra Stack with Developer-First Testing Automation

VideoDB x TwelveLabs: Real-Time Video Understanding

Human Monitoring is Expensive, Exhausting, and Doesn't Scale

From baby monitors to warehouse cameras or endless CCTV feeds, many companies still depend on human eyes to monitor live video—a costly, tedious, and highly error-prone approach. Fatigue inevitably sets in, accuracy declines, and scaling up means hiring more personnel rather than enhancing systems.
But in an AI-driven era, monitoring shouldn’t be manual, miss critical details, or become a bottleneck. Imagine receiving instant alerts when packages are stolen, notifying parents the moment a baby attempts to climb out of a crib, proactively highlighting safety risks before they escalate, or doctors instantly knowing when ICU patients need immediate attention.
This is precisely the responsive monitoring delivers through its real-time infrastructure. VideoDB now provide first party integration with advanced Pegasus 1.2 AI model for precise frame understanding.

Live video to Instant Action

Real-time video analysis isn’t merely a nice-to-have—it's a fundamental shift in capability.
Safety and Security: Transforming reactive measures into proactive alerts, potentially saving lives during emergencies like flash floods or security breaches.
Enterprise Productivity: Converting passive meeting archives into interactive, searchable knowledge repositories, vastly enhancing collaboration.
Content Platforms: Automatically tagging, chaptering, and moderating content in unprecedented detail, elevating user experiences.
The opportunities are immense, yet technical barriers have historically prevented many development teams from unlocking this potential.

Reality of Building Video AI

If you've ever attempted to build a robust video understanding system, you know the pain firsthand. You're often stuck playing the role of a systems integrator, wrestling with:
API Spaghetti: Managing credentials, juggling rate limits, and navigating diverse SDKs across multiple video processing, AI modeling, and storage services.
Scaling Nightmares: Each component scales independently, causing bottlenecks and inefficiencies—particularly with resource-intensive GPU workloads.
Latency Issues: The delays involved in interactions between storage, AI models, and your applications undermine genuine real-time capabilities.
These challenges can quickly stall innovation, turning promising ideas into lengthy, frustrating engineering endeavors.

Simplifying Video AI Infrastructure with VideoDB

VideoDB is a purpose-built infrastructure for AI driven video management. It provides a unified, AI-native infrastructure handling the complete lifecycle of video content—from ingestion and indexing to alert management—all via a streamlined, developer-friendly API.
Leveraging VideoDB, you can effortlessly ingest multiple real-time video streams, manage customizable indexes uniquely tailored for specific analyses, and simultaneously analyze diverse aspects of a single video stream. Additionally, VideoDB’s targeted alerting system triggers automated webhooks, ensuring rapid responses to critical events.
And now, we've equipped that powerful infrastructure with an even more powerful intelligence.

Introducing the TwelveLabs Integration: Your Frame Understanding, Supercharged

We're excited to announce our native integration with TwelveLabs—going far beyond a traditional partnership. We've seamlessly embedded TwelveLabs' advanced AI, powered by the exceptional Pegasus 1.2 model, directly within VideoDB.
Screenshot 2025-07-31 at 10.05.03 PM.png

What does that mean for you?
No additional accounts.
No extra API keys.
Zero integration headaches.
Now, accessing TwelveLabs' sophisticated video understanding models, like Pegasus, is as easy as adding a single parameter to your indexing call. All the AI power you need is fully integrated into your VideoDB environment—effortlessly blending world-class intelligence with unmatched simplicity.

Real-World Use Cases: From Theory to Action

With VideoDB + TwelveLabs, sophisticated real-time video monitoring is just minutes away:
🌊 Real-Time Flash Flood Detection
Imagine a camera monitoring a dry riverbed in a flood-prone area. With TwelveLabs integrated into VideoDB, you can continuously detect critical events—like rapidly rising floodwaters—in real-time. The moment Pegasus recognizes the signs of a flash flood, VideoDB immediately triggers life-saving alerts. This isn't just video analysis; it's proactive, intelligent disaster prevention.
👶🏻Baby Crib Monitoring
After a long day, parents deserve restful, worry-free sleep. With VideoDB’s real-time monitoring powered by TwelveLabs' Pegasus, you'll instantly know if your baby tries climbing out of the crib or needs immediate attention. Sleep easy, knowing VideoDB and TwelveLabs have you covered—every second of the night.

How It Works: One Line of Code to Unlock a New World

Ready to see how simple this is? To index a video stream with TwelveLabs' powerful Pegasus model, you just specify it as the model_name.
from videodb import SceneExtractionType

# Your existing stream object
flood_stream = <...>

# Index scenes using TwelveLabs' model
flood_scene_index = flood_stream.index_scenes(
extraction_type=SceneExtractionType.time_based,
extraction_config={
"time": 10,
"frame_count": 6,
},
prompt="Monitor the dry riverbed and surrounding area. If moving water is detected across the land, identify it as a flash flood and describe the scene.",
# This is the magic line!
model_name="twelvelabs-pegasus-1.2",
name="Flash_Flood_Detection_Index"
)

print("Scene Index ID:", flood_scene_index.rtstream_index_id)

This integration lets you seamlessly test out TwelveLabs models right inside VideoDB's indexing pipeline—giving you maximum flexibility, control, and ease. Now, you can instantly build real-time visual understanding apps without friction. We can't wait to see what you create!



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