Design a Text-to-Video and Video-to-Video Search System

Context

You are tasked with designing an end-to-end multimodal retrieval system that supports both:

• Text-to-video search (user enters a query, returns the most relevant video segments)
• Video-to-video search (user provides a reference video, returns visually/semantically similar segments)

Assume a catalog that grows to billions of video clips/segments, with global users and strict latency, cost, and safety requirements.

Requirements

1. Model architecture
   • Encoders for text and video frames/clips (e.g., dual encoders)
   • Temporal aggregation for video (frame-to-clip, clip-to-video)
   • Shared embedding space, similarity function, and scoring
   • Pretraining/fine-tuning objectives (contrastive, multi-task), negative sampling strategies
   • Handling long videos (clip sampling, segment-level indexing)
   • Optional cross-encoder re-ranking for top-K
2. Infrastructure
   • Offline/online feature extraction pipelines
   • Ingestion and metadata enrichment (e.g., ASR, OCR, tags)
   • Indexing in a vector store (HNSW/IVF, PQ/OPQ, sharding, replication)
   • Storage layout for thumbnails and short preview clips
   • Query routing and aggregation across shards
   • Latency and cost targets; caching strategies
   • Scalability to billions of clips/segments
3. Monitoring, safety, and evaluation
   • System and model monitoring (SLOs, drift, recall)
   • Abuse/content safety (NSFW, spam, PII)
   • Evaluation via offline metrics and online A/B tests
   • Relevance feedback and incremental model updates