What experience do you have building large‑scale systems that generate embeddings, search data using vector databases, and integrate custom or pre‑trained models? How would you approach building such a product?

Building a large‑scale embedding‑based search system involves several key components. First, design a robust ingestion pipeline that can handle high‑volume data, generate embeddings using a chosen model (e.g., Sentence‑Transformers, OpenAI embeddings), and store them in a scalable vector database (Pinecone, Milvus). Second, implement efficient similarity search with approximate nearest neighbor (ANN) algorithms to keep query latency low. Third, integrate custom or fine‑tuned models for domain‑specific embeddings, and maintain a versioning strategy to roll out updates without downtime. Fourth, use a cache layer (Redis) for hot queries and a monitoring stack to track latency, throughput, and error rates. Finally, expose the search functionality via a REST or gRPC API, and consider rate limiting and authentication. Throughout, focus on data freshness by invalidating or updating embeddings when source data changes, and on scalability by sharding the vector store and using horizontal scaling for the API layer.