System Design: Add Semantic Search to an Existing CRUD Service Using Chroma

Context

You own a document CRUD service (create/read/update/delete) that stores documents with an id, text body, and optional metadata. Extend this service by integrating a Chroma vector store to support semantic search over documents. Assume you can add a background worker if needed, but aim for a minimal, production-ready design.

Tasks

1. Define the vector collection schema and explain how text embeddings are produced (model choice, dimensionality, normalization, and how metadata is stored).
2. Implement API endpoints to:
   • Upsert documents into the vector store.
   • Delete documents (by id and/or by metadata filter).
   • Query by vector similarity (semantic search) with optional metadata filters.
3. Implement a search_query function that returns a response object with a required results list (may be empty; never None ). Include per-result scores and metadata.
4. Describe how you would handle:
   • Indexing strategy and parameters.
   • Pagination for vector search results.
   • Filtering by metadata.
   • Eventual consistency between the CRUD store and the vector index.
5. Discuss production concerns: latency targets, batching strategies, and error handling (including retries, timeouts, and idempotency).