What the documents look like
For product questions in Regional dealer stock, this page examines FastGPT hybrid retrieval. Compare the material received with the sources used in the answer. The examples are hypothetical.
Constraints at the hybrid retrieval step
The proposed regional replenishment request uses “medium” for suggested replenishment volume and “standard carton” for packaging. Retrieval and reranking should preserve these qualitative descriptions, without converting them into an invented piece or carton count. Check that both passages for the requested model enter candidates, then inspect whether the complete question retains support for volume and packaging. An actual order quantity requires corresponding business evidence, beyond interpreting these sample terms.
Configuration and implementation checks
| Item or implementation check | Input or applicable condition | Traceable check |
|---|---|---|
| Fused candidates | Medium replenishment volume and standard carton | Preserve qualitative wording without inventing piece counts. |
| Complete question | Scope of sufficient and low inventory descriptions | Check product, timing and measurement scope. |
| Minimum relevance | Inspect filtering during semantic retrieval or reranking | A scored state label is not an inventory-verification result. |
| Citation token limit | Retain the state with its conditions | Do not calculate replenishment from a qualitative low state. |
| Missing-source retest | Missing shelf-life and near-term sales-condition evidence | Recheck support for the question after adding sources. |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Hybrid retrieval | Vector and full-text results use RRF fusion. |
| Reranking | Reranking reorders candidates using the full question. |
Minimum relevance | Filtering applies to semantic search or reranking. |
Citation limit | Citation content is limited by tokens. |
Question optimization | Compare subjects and restrictions after optimization. |
Three common errors
“Sufficient” in the request and “low” in the turnover sheet require product, timing and measurement-scope checks. The words may describe different scopes and do not alone establish an inventory-record error. Retain both statements and source identities in candidate inspection, then check whether reranking leaves only the side closest to the question. Investigate recall scope if one side is missing; where both exist, inspect order and citations for conditions affecting comparability. Inventory-state labels cannot be directly converted into a recommended purchase quantity. If the question turns into an ordering decision, inspect the quantity and condition evidence separately. “Low” is not a supplied numerical stock value from which the model can calculate replenishment.
How to verify the setup
No shelf-life information has been supplied. The request therefore lacks evidence for whether the model meets near-term sales requirements, which also need their own source. Inspect whether the answer infers suitability from sufficient inventory or invents a date. After adding corresponding material, repeat the question and inspect new candidates, filtering and final citations separately. A reranking adjustment cannot create a shelf-life record or replace examination of the specific sales conditions.
| Material context | Scope |
|---|---|
| Document types | Replenishment Request, Inventory Turnover Sheet, Outbound Order and Payment Collection Plan |
| Candidate sources | Dealer ERP, Brand Ordering System, Warehouse Inventory and Sales Collection |
| Technical baseline | FastGPT v4.17.2 |