Short answer: for an in-app chatbot, the right API for semantic search integration is an embeddings call followed by /v1/chat/completions . This keeps the search contract clear while tenant-scoped catalog matches stay in the application.

Short answer: use embeddings to turn messy catalog descriptions and support questions into comparable search inputs, then pass tenant-scoped matches to /v1/chat/completions . Keep the retrieval store, tenant boundary, and usage ledger in your application. The model call should explain evidence, not decide who is allowed to see it.

That is the practical shape for an in-app customer-support chatbot that enriches a product catalog. It gives the search layer a chance to understand paraphrases, while the chat layer turns selected records into a useful answer. The contract stays stable while the service behind it changes, which matters when a small CLI or SDK grows into several workers and runtimes.

The hard constraint is per-tenant cost visibility.…