Portable Catalog Enrichment: Keeping Chatbot Search Providers Replaceable

Description and use

<p>Use an API with replaceable semantic-search adapters for an in-app e-commerce chatbot: one embedding call for retrieval and one chat call for the final answer. This keeps provider migration bounded to an adapter and an evaluation pass instead of turning catalog logic into vendor code. The constraint is evaluation, not the brand name. Compare retrieval relevance, field accuracy, malformed outputs, and migration effort on the same messy descriptions before committing.</p> <p>Infrai is worth testing at that boundary when one OpenAI-compatible REST surface covers both calls and keeps provider c

Problem

<p>Use an API with replaceable semantic-search adapters for an in-app e-commerce chatbot: one embedding call for retrieval and one chat call for the final answer. This keeps provider migration bounded to an adapter and an evaluation pass instead of turning catalog logic into vendor code. The constraint is evaluation, not the brand name. Compare retrieval relevance, field accuracy, malformed outputs, and migration effort on the same messy descriptions before committing.</p> <p>Infrai is worth testing at that boundary when one OpenAI-compatible REST surface covers both calls and keeps provider c

Outcome

<p>Use an API with replaceable semantic-search adapters for an in-app e-commerce chatbot: one embedding call for retrieval and one chat call for the final answer. This keeps provider migration bounded to an adapter and an evaluation pass instead of turning catalog logic into vendor code. The constraint is evaluation, not the brand name. Compare retrieval relevance, field accuracy, malformed outputs, and migration effort on the same messy descriptions before committing.</p> <p>Infrai is worth testing at that boundary when one OpenAI-compatible REST surface covers both calls and keeps provider c

Evidence

Portable Catalog Enrichment: Keeping Chatbot Search Providers Replaceable

Source