What the documents look like
Overseas stock replenishment for third-party warehouses covers replenishment plans, inbound appointment records, inventory age tables and final-mile delivery notes. A third-party warehouse can serve several sales channels, and the seller allocates stock across those channels. Procurement and supplier material for this category typically carries purchase order numbers, supplier names, material codes, quantities, agreed delivery dates and inbound appointment numbers. These fields are spread across replenishment plans and inbound appointments, so parsing and chunking must keep the purchase quantity, delivery date and matching appointment of the same material retrievable back to the source text.
The category differs from first-party marketplace replenishment because the seller performs the allocation rather than the platform. Data arrives from the overseas warehouse WMS, ERP sales forecasts, freight forwarder systems and carrier bills, and it updates weekly or daily. The consequences of getting it wrong are stockouts, slow-moving inventory storage charges and final-mile delays. The three most time-consuming steps are forecasting sales, booking inbound appointments and reconciling inventory age costs. Confidence in this background is moderate.
In FastGPT, a knowledge base consists of libraries, collections and data. The smallest search unit is the library, so a search runs against the whole library, and collections only classify data. A set of data can map to multiple vectors, and when several vectors match the same original record, the vectors are merged and the highest score is used. Chunking therefore needs to keep conditions, explanations and the content they constrain inside the same data group, so that a delivery date condition is not split away from the quantity it governs. Semantic search relies on vector distance, full-text search suits exact queries such as codes and model numbers, and hybrid search combines both through RRF. The minimum relevance setting is a filter threshold that takes effect only in semantic search or when result reranking is used. Retrieval does not perform authorization checks, and a form collecting a value is not the same as an approval or a database write.
Hypothetical paper exercise sample: a cross-border seller holds one procurement and supplier material record. The source text reads: "Purchase order PO-2026-0417, supplier is a Ningbo trading entity, material code SKU-A12, purchase quantity 800 units, agreed delivery date 20 May 2026, corresponding inbound appointment number APT-0512." One draft sentence to be assessed reads: "This purchase order has completed inbound receiving, and the stock can be allocated to all channels." The source text records only the delivery date and the appointment number, and does not record any actual inbound completion status.
Constraints at the document chunking step
The library-level search unit means that chunking decisions affect what a single search can reach across the whole procurement and supplier material set. Keeping the purchase quantity, delivery date and appointment number of one material in one data group supports later retrieval back to the source. Splitting the delivery date condition away from the quantity it constrains weakens that link.
Multiple vectors per data group allow a longer passage to be indexed in several ways, and a match on any one vector recalls the whole data group. This supports keeping a complete condition together rather than shortening it to fit one index. The trade-off is that index content length and semantic richness pull against each other, so the chunking choice depends on whether the material code and supplier name need exact hits or whether natural-language questions need semantic recall.
The minimum relevance value is a filter threshold, not a permission or entity filter. It does not exclude records belonging to other suppliers or unauthorized records. Retrieval is not authorization. Forms collect values, and business verification, approval, database writes and external synchronization require their stated downstream implementation.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid search | Material codes and supplier names need exact hits while natural-language questions need semantic recall; compare returned content for the same question under each mode |
| Minimum relevance | Hypothetical starting point 0.4 | This value takes effect only in semantic search or with result reranking; compare returned item counts against 0.3 and 0.5 and judge manually |
| Reference limit | Hypothetical starting point 2000 tokens | Reference content length is controlled in tokens; compare reference completeness against 1500 and 3000 |
| Question optimization | Not enabled in this hypothetical exercise | The sample question has a complete subject; enabling it adds one model call; compare returned content with it enabled |
| Result reranking | Not enabled in this hypothetical exercise | Requires a rerank model enabled by configuration; compare ranking scores with it enabled |
| Training status check | Manual check after upload | A successful upload does not mean retrieval has updated; compare returned results for the same code before and after training |
Three common errors
- Treating a successful upload as an updated retrieval index. Input is the newly uploaded procurement material, and the comparison is between training status and retrieval returns. If retrieval still returns the previous version of the content, the index has not updated.
- Treating a pending inbound appointment as completed inbound receiving. Input is the draft sentence "has completed inbound receiving," and the comparison is against the delivery date and appointment number in the source text. The source text does not record a completion status, so the claim is unsupported.
- Treating the minimum relevance value as a permission or entity filter. Input is procurement material from different suppliers, and the comparison is whether returned content is isolated by supplier. This setting only filters low-relevance results and does not exclude unauthorized records.
How to verify the setup
- Input "SKU-A12" from the hypothetical source text and compare the returned content under full-text search and hybrid search. The condition is satisfied if both modes return the purchase quantity and delivery date for that material and do not split the delivery date away from the quantity.
- Input the draft sentence "This purchase order has completed inbound receiving" and compare it against whether the source text records an inbound completion field. If the source text contains only the delivery date and appointment number, the sentence is unsupported and the evidence gap remains.
- Input an empty reference list and check the downstream design for handling empty results. The condition is satisfied if the downstream explicitly shows no reference content and does not automatically trigger a replenishment action. These are paper checks only, and no search or training was actually executed.