Self-Improving Multi-Modal Platform with Edge Serving

Scenario

A content platform ingests documents and videos via OSS+EventBridge, performing real-time HLS transcoding and OpenSearch vectorization while simultaneously feeding processed data through DataWorks into PAI to continuously retrain domain-specific embedding and reranking models — updated models are deployed back to OpenSearch for hybrid BM25+vector search served through Cloudflare edge, with Supabase tracking all processing status and triggering tiered notifications (DingTalk for failures, Resend email for completions, Twilio SMS for critical alerts) including model retraining pipeline status.

How the products combine

1. dataworks · multi-modal-content-platform-with-search-and-str-b003fb — Multi-Modal Content Platform with Search and Streaming

See _combos/multi-modal-content-platform-with-search-and-str-b003fb.

2. dataworks · multi-modal-platform-with-database-driven-notifi-0b7106 — Multi-Modal Platform with Database-Driven Notifications

See _combos/multi-modal-platform-with-database-driven-notifi-0b7106.

3. eb · event-driven-media-processing-pipeline-66b40c — Event-Driven Media Processing Pipeline

See _combos/event-driven-media-processing-pipeline-66b40c.

4. airec · custom-trained-rag-with-event-pipeline-and-edge--3d05fe — Custom-Trained RAG with Event Pipeline and Edge Serving

See _combos/custom-trained-rag-with-event-pipeline-and-edge--3d05fe.

Typical questions

• multi-modal platform with self-improving search and edge serving
• auto-retrain embeddings from content pipeline usage data
• upload documents and video with continuous model improvement
• EventBridge content ingestion feeds PAI training and OpenSearch edge
• database-tracked processing with custom model retraining pipeline
• Cloudflare edge serving for continuously retrained hybrid search
• 上传内容自动改进搜索模型并通过边缘节点服务
• 内容处理状态触发通知同时数据反馈训练嵌入模型

Q: How does the self-improving multi-modal platform ingest content, retrain models, and serve search results? A: The platform ingests documents and videos via OSS and EventBridge to continuously improve its search capabilities. Processed data flows through DataWorks into PAI for continuous embedding and reranking model retraining, enabling updated models to power hybrid BM25 and vector search on OpenSearch. Supabase monitors the entire workflow and triggers tiered notifications for processing milestones and retraining pipeline status.