Regional dealer stock: after-sales questions with retrieval

Review retrieval for after-sales questions in Regional dealer stock. Check document scope, failure paths and source evidence through controlled tests.

What the documents look like

For after-sales questions in Regional dealer stock, this page examines FastGPT retrieval. Retrieve carton observations, replacement-box requests and actual replenishment separately. The examples are hypothetical.

Constraints at the retrieval step

A proposed distributor case reports a damp outer carton, requests a new carton and includes dispatch of earlier goods. The question concerns arrival of the replacement box. Observation, request and prior shipment have different objects and stages. Retrieve carton and batch identity without extending moisture to the contents or joining a request with old dispatch into a current arrival. Compare new-carton phrases, the full arrival question and earlier-batch wording. No receiving evidence for this replacement is supplied, and the packet does not establish completion of the current replacement-box delivery.

Configuration and implementation checks

Material checkConditions in this testImplementation evidence
Observed objectOuter cartonDo not infer damaged contents.
Current actionNew box requestedDo not make dispatched.
Logistics batchEarlier goodsExclude replacement mismatch.
Output scopeCarton and contents separateInspect references and additions.
Later outcomeCurrent dispatch or receiptUpdate stages separately.

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, an earlier logistics record may carry the replacement’s progress; inspect batch and shipment object. Second, optimization may turn requested carton into dispatched carton; compare original and rewritten question for the request stage. Third, a recalled request may yield an answer claiming damaged contents; inspect quotations and added objects, since an outer-carton observation does not establish the condition of the contents. These concern source batch, question stage and conclusion scope. Removing lower-scoring passages creates no current handoff record and cannot repair the evidential gap by changing ranking alone.

How to verify the setup

Ask what was observed, what was requested and which event old logistics describes, inspecting actual references. Compare semantic, full-text and hybrid retrieval with the earlier shipment as a distractor. Token-limited text needs batch and request action together. Later dispatch or receipt evidence changes only its supported stage; dispatched does not itself mean arrived. This exercise judges no inner quality, orders no carton and performs no compensation. Missing receipt material remains unresolved rather than proving the supplier refused dispatch or that an unprovided replacement outcome actually occurred.

Material contextScope
Document typesReplenishment Request, Inventory Turnover Sheet, Outbound Order and Payment Collection Plan
Candidate sourcesDealer ERP, Brand Ordering System, Warehouse Inventory and Sales Collection
Technical baselineFastGPT v4.17.2