System Design: Multimodal File Ingestion and Embedding Service

Context

Design a backend service that accepts user-uploaded files (documents, images, videos), computes embeddings for search/retrieval, and stores results for querying. The service must be reliable, secure, and scalable. Assume a maximum file size of X MB (e.g., X = 512 MB for estimates) and bursty traffic patterns.

Requirements

Specify and justify the following:

1. API design
   • Endpoints for upload, status, and search.
   • Idempotency, authentication, and callbacks/webhooks.
2. Storage schema
   • Where raw files live (object storage) and how they’re organized.
   • Database schema for file metadata, chunk metadata, embeddings, jobs, and errors.
3. Chunking strategies per modality
   • Documents (PDF, DOCX, TXT), images, videos (frames + audio transcript).
4. Model choices and versioning
   • Text/image/video embedding models, dimensions, and upgrade strategy.
5. Asynchronous processing pipeline
   • Queues, workers, orchestration, and status tracking.
6. Idempotency and retries
   • Content hashing, idempotency keys, retry/backoff, exactly-once semantics where needed.
7. Failure handling
   • Dead-letter queues, partial failures, quarantining, and operator tooling.
8. Vector index selection and updates
   • Technology choice(s), filtering, partitioning, upsert/delete, and reindex strategy during model migrations.
9. Security and PII handling
   • Encryption, malware scanning, DLP/PII detection, access control, auditability, and deletion (e.g., GDPR).
10. Cost/performance trade-offs
    • Model size vs cost, batching, compression, storage lifecycle, and caching/deduplication.
11. Scalability
    • Throughput spikes and long-running video jobs (segmentation, checkpointing, autoscaling, priorities).

Provide a cohesive design with brief diagrams-as-text or bullet points as needed.