Python Retrieval Architecture — Evaluation Sets for a Restaurant Menu Assistant
Short answer: build the restaurant menu assistant around a versioned evaluation set before choosing a retrieval method. Measure whether the retriever finds the right menu fact, rejects unavailable items, and prefers the newest applicable record; then retain only the artifacts needed to reproduce a failed decision. The bill is made of menu ingestion, index storage, retrieval work, generation,…
The Python Retrieval Architecture for a Restaurant Menu Assistant emphasizes building the system around a versioned evaluation set before deciding on a retrieval method. The evaluation set measures whether the retriever finds the right menu fact, rejects unavailable items, and prefers the newest applicable record. The cost model separates work caused by menu changes from work caused by diner questions, as freshness and chunking have opposite effects on retrieval cost and retention.
The dominant factor in the architecture's cost is not the model size or index type but the ability to separate changed menu versions from unchanged ones and run the smallest evaluation slice that still covers the affected restaurant, service window, dietary claim, and availability state. Retaining only the necessary artifacts to reproduce failed decisions and measuring the daily cost with specific units charged or consumed by the actual stack are crucial.
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