On-demand retail fulfillment: after-sales questions with retrieval

Review retrieval for after-sales questions in On-demand retail fulfillment. Check sources, input boundaries, error handling and repeatable test evidence.

What the documents look like

For after-sales questions in On-demand retail fulfillment, this page examines FastGPT retrieval. Distinguish rider arrival at store, estimates and customer receipt in immediate-delivery answers. The examples are hypothetical.

Constraints at the retrieval step

A proposed immediate-retail packet says the rider reached the store, estimates delivery later and records a customer saying nothing has arrived. The question asks whether the order is delivered. Retrieve location and speaker with each passage. Store arrival is not customer arrival, and an estimate records no actual event. The customer statement also needs order correspondence; it cannot by itself prove another record false. Compare store-arrival, not-received and full-status questions so a delivery keyword does not let forecast wording replace evidence of what actually happened.

Configuration and implementation checks

Material checkConditions in this testImplementation evidence
Event locationAt storeRetain location in reference.
Estimate statusExpected laterDo not make actual event.
Task preservationWhether differs from whenCompare optimization.
Receipt attributionCurrent order and recipientExclude other orders.
Conclusion scopeReceipt unresolvedDo not decide from score.

Documented conditions

Documented item / implementation checkCondition
Associated knowledge basesKnowledge bases associated with one search node must use the same embedding model.
Search modeSemantic, full-text and hybrid modes are available.
Citation limitCitation limits are measured in tokens.
Minimum relevanceRange 0–1; active for semantic search or reranking.
Question optimizationResolves references and expands questions.

Three common errors

First, references may lose at the store; compare original candidate and delivered wording for location. Second, optimization may change whether delivered into when delivery; retain the original status task instead of replacing it with a forecast answer. Third, another order’s receipt may be adopted; inspect order, receiving subject and version. These concern location retention, task change and source identity. A high score or receipt title determines no current status, and the corresponding actual receiving record remains necessary before claiming a real delivery outcome.

How to verify the setup

Ask where the rider is, what the estimate states and what the customer asks, expanding each reference. Compare semantic, full-text and hybrid retrieval on the same questions, keeping forecasts and event messages as different sample types. Token limits should retain store as the location. Added current-order receipt needs its event scope checked without borrowing another order. This exercise changes no delivery state and contacts no rider. It decides no responsibility or compensation; missing records remain unresolved rather than a delivered order inferred from one relevant search result.

Material contextScope
Document typesInstant Order, Picking List, Out-of-Stock Report and Delivery Exception Order
Candidate sourcesDouyin Instant Retail, Meituan or JD Daojia Backend, Store Inventory and Rider Tracking
Technical baselineFastGPT v4.17.2