What the documents look like
Overseas stock replenishment management for third-party warehouses works across four document types: replenishment plans, inbound appointment bookings, inventory age reports, and last-mile delivery orders. The data comes from warehouse management systems, ERP sales forecasts, freight forwarder systems, and carrier invoices. A third-party overseas warehouse can serve multiple sales channels, and the seller allocates stock across those channels. This means the same product record plays different roles in different documents. A product code in a replenishment plan points to purchasing and transfer. The same product code in an inbound appointment points to receiving and shelving. In an inventory age report, it points to storage duration and fees.
Document parsing and chunking must keep the product identifier, quantity, status, and the plan or appointment it constrains inside the same chunk. If chunking separates these fields, selection questions retrieve fragments that lack a subject. The brief states that data updates weekly or daily. This page does not derive batch synchronization, full synchronization, real-time processing, or transaction-triggered mechanisms from that update frequency. The update frequency does not affect the chunking task described here.
FastGPT organizes a knowledge base into libraries, collections, and data. The smallest search unit is the library. Collections only classify data and do not affect search. One data record can map to multiple vectors. The indexes field in the dataset.datas table records the vector IDs. During retrieval, the system recalls vectors first, then uses the vector IDs to find the original data in MongoDB, merges records that belong to the same original data, and takes the highest score. These structural facts mean that chunking must preserve the subject, object, and valid scope of product records.
Constraints at the document chunking step
Semantic search relies on vector distance. Full-text search suits codes, models, and proper nouns. Hybrid search merges multiple recall paths through RRF. Rerank produces a 0-1 score after a second ordering pass. The reference limit controls how many tokens of content each search can cite. The minimum relevance threshold applies only during semantic search or when result reranking is enabled. Retrieval parameters affect recall and ordering. They do not perform permission checks or entity isolation. A similarity score is not a category filter, an entity filter, or a permission filter. Retrieval is not authorization.
A hypothetical paper exercise sample follows. The source material reads: "Replenishment plan RP-2024-031, product SKU-HK-118, planned replenishment quantity 400 units, target warehouse Hong Kong third-party overseas warehouse, planned inbound date 10 June 2024, status pending review." The draft sentence under evaluation reads: "SKU-HK-118 has been approved for inbound of 400 units on 10 June." The evaluation point: the source status field reads pending review. The draft sentence states approved. The same subject, same period, and same status field show mutually exclusive values. The draft sentence is unsupported.
A pending request is not approval. An offered option is not execution. An unsigned termination is not a completed termination. When the source document does not state a field, that field remains unknown.
Configuration and implementation checks
| Configuration or check item | Value or practice on this page | Basis and comparison method |
|---|---|---|
| Library and collection split | Separate collections for replenishment plans, inbound appointments, inventory age reports, and delivery orders; search within one library | The smallest search unit is the library; collections only classify data |
| Fields kept in one chunk | Product code, quantity, target warehouse, date, and status remain in the same chunk | Conditions and the content they constrain stay together |
| Multi-vector mapping | A long text can carry multiple indexes entries | One data record can map to multiple vectors |
| Search mode | Code-type questions use full-text or hybrid search | Codes and models suit full-text matching |
| Reference limit and minimum relevance | Recorded as the starting values for this hypothetical exercise; record current values before comparing | Reference limit controls tokens; minimum relevance applies only during semantic search or reranking |
| Post-update review | After material changes, check actual training and index state | A successful upload does not equal an updated retrieval index |
Three common errors
- Writing a pending-review status as approved. Input: the source material and the draft sentence above. Comparison: the status field value "pending review" against "approved." These are mutually exclusive. The draft sentence is unsupported. A pending request is not approval.
- Treating a retrieval hit as authorization to execute. Input: one retrieval request. Comparison: the retrieval result against the authorization materials required for an operations handover. Retrieval does not produce authorization. When authorization materials are missing, record the gap.
- Associating libraries that use different vector models. Input: the model identifiers of two libraries. Comparison: whether the identifiers match. When they do not match, exclude that library and record the actual observation. Do not infer missed recalls or redundant results. Cross-model libraries cannot be jointly associated. Check identifiers first, exclude non-matching ones, and record what is observed.
How to verify the setup
- Chunk completeness check. Input: the source material above. Comparison: whether the parsed chunk contains SKU-HK-118, 400 units, the target warehouse, and the pending-review status together. All four in one chunk satisfies the condition. A missing status field does not satisfy it.
- Status judgment check. Input: the draft sentence and the source material. Comparison: the value of the same status field. When "pending review" and "approved" appear as mutually exclusive values, the draft is unsupported. Do not rewrite it as approved.
- Empty-result handling check. Input: the explicit paper input
[]. Comparison: whether the downstream design treats it as no reference content. Returning empty references is the expected behavior. Do not claim that a specific rule's retrieval necessarily returns empty. For cross-model libraries, check identifiers first and exclude non-matching ones. Do not run cross-model retrieval to compare missed recalls.
These checks describe what to compare and what remains unknown. They do not claim that a test, deployment, approval, or other business action has occurred.