Video RAG used to mean frame extraction, audio separation, multiple embeddings, and complex orchestration pipelines.

A year ago, if you wanted to build semantic search over video content, you'd extract frames, transcribe audio separately, generate embeddings for each modality, calculate similarities, and orchestrate everything yourself. Three hundred lines of Python. Six different libraries. Multiple failure points.

Then multimodal models that natively understand video arrived—and everything changed.

I'll show you both approaches through working code:

The Traditional Pipeline:
- Frame extraction and key frame selection using cosine similarity
- Audio transcription with speaker diarization and timestamps
- Separate embedding generation for visual and audio content
- Vector storage and semantic search with `pgvector`
- Custom orchestration logic to tie it all together

The Modern Approach :
- Native video understanding without decomposition
- Unified temporal embeddings across modalities
- Agent-based architectures that handle orchestration
- Production-ready patterns in a fraction of the code

Join me for a live demonstration of native video understanding without the need to create embedding pipelines. Build a complete video analysis agent in minutes.