Design pipeline using classification and embedding services

You are given two **black-box ML services**: 1. **Classification Service** - Input: One or more text documents. - Output: A label for each document (e.g., topic or category). 2. **Embedding Service** - Input: One or more text documents. - Output: A vector embedding (e.g., 768-dim float vector) for each document. You need to design a system that: - Accepts file uploads from users (each file contains one or more text documents). - Supports both **single-file** and **bulk** upload (up to **1,000 files** in one request). - For each document: - Computes a classification label using the classification service. - Computes an embedding using the embedding service. - Stores results so they can be queried later (e.g., by user, file, or semantic search). - Satisfies both: - **Low latency** for small/single uploads. - **High throughput** for large/bulk uploads. **Task** Design the end-to-end pipeline and APIs. Specifically address: 1. **API Design** - How clients upload files (single and bulk up to 1,000 files). - What responses they receive (synchronous vs asynchronous). 2. **Architecture** - How you orchestrate calls to the classification and embedding services. - How you store raw files, parsed text, labels, and embeddings. - How you achieve both low latency and high throughput. 3. **Scalability & Performance** - How to handle 1,000-file uploads without running out of memory or violating latency goals. - Batching, queuing, and concurrency strategies when talking to the ML services. 4. **Reliability & Observability** - Error handling for partial failures (e.g., some files fail to process). - Monitoring, logging, and metrics. Assume you cannot change the internals of the classification and embedding services; you may only call their APIs.