What the documents look like
Fruit procurement for fresh stores relies on wholesale market conditions and grade-based acceptance rather than head-office standard meal planning. The working documents are purchase orders, acceptance records, pesticide-residue test reports, and loss records, and they are refreshed daily or per batch. Purchase orders carry item name, quantity, unit price, and supplier. Acceptance records carry grade determination and actual delivered quantity. Pesticide-residue reports carry test items, results, and batch numbers. Loss records carry loss reason and quantity.
In cross-border work these materials may appear in different languages, so retrieval has to locate corresponding content across language differences. The structure of each material directly shapes parsing. When fields such as grade conditions and grade results are split apart during chunking, a retrieved fragment cannot restore the full business meaning, and the citation handed to the model is incomplete.
Constraints at the document chunking step
In FastGPT the smallest search unit is the library, and a collection serves only to classify and manage data. One group of data can map to multiple vectors; if any one of those vectors is recalled, the whole group is recalled. Chunking therefore needs to keep meaning complete and singular, and conditions, descriptions, and the content they constrain should stay in the same chunk. The same original data group can map to several indexes. A more concise index generally raises retrieval precision while narrowing retrieval scope.
Multilingual retrieval depends on the cross-language capability of the vector model. The supplied document does not state a specific language support range, so that capability remains unknown. Under a PostgresSQL vector store, full-text retrieval uses MongoDB $text with jieba tokenization, and the document does not describe how jieba handles multilingual text. Hybrid retrieval merges multiple result paths through RRF, and ReRank performs a second ordering of candidate text and returns a 0-1 score. Image retrieval requires corresponding image input and model capability. Optional paths in the overall retrieval flow do not mean every path runs on every request.
Configuration and implementation checks
The following settings concern a hypothetical drill sample and list only documented settings or implementation checks relevant to this feature.
| Configuration item | Hypothetical starting value for this drill | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains grade numbers and natural-language description, so precise hits and semantic matching both matter. Compare recall coverage against semantic retrieval alone. |
| Minimum relevance | 0.3 | A hypothetical starting point to observe filter strength. Compare with 0.2 and 0.5 and observe changes in returned count and relevance. |
| Citation limit | 2000 tokens | A hypothetical starting point because the sample materials are short. Compare with 1000 and 4000 and observe whether cited content covers the fields needed for the judgment. |
| Question optimization | Enabled | The sample includes a multi-turn follow-up scenario. Compare continuous-conversation retrieval against the disabled state. |
| Chunking approach | Set separately by material type | Purchase orders, acceptance records, and test reports have different field structures. Check whether conditions and the content they constrain share a chunk. |
| Index creation | Confirm training completion | A successful upload does not equal an updated index. Check the actual training state against retrieval results. |
Three common errors
- Treating a successful upload as an updated index. Upload and training completion are two different states. Before retrieval, the actual training state needs to be checked, otherwise old indexed content may be retrieved.
- Splitting conditions from the content they constrain. If the grade determination condition in an acceptance record sits in a different chunk from the determination result, the retrieved fragment cannot restore the basis of the judgment, and the citation is incomplete.
- Treating a retrieval result as authorized execution. Retrieving purchase-order or acceptance-record content does not mean the document has been approved or can be executed. A pending-review state does not demonstrate a lack of operating permission, and retrieval does not carry authorization.
How to verify the setup
Hypothetical drill sample: a store fresh-produce supervisor submits an acceptance record whose source text reads, "March 10, 2025, batch A-0310, Red Fuji apple, grade one, delivered quantity 200 boxes, acceptance passed." A draft sentence is also submitted for judgment: "This batch passed acceptance and payment can be arranged." The materials needed for the check are the acceptance record source text and the payment approval record.
- Verification one: input the acceptance record source text and the draft sentence, and compare "payment can be arranged" in the draft against "acceptance passed" in the source. The source states only that acceptance passed and does not state a payment approval status, so the payment arrangement in the draft is not supported by the source and is judged as pending verification.
- Verification two: input "Red Fuji apple grade one" for full-text retrieval and "apple arrival acceptance situation" for semantic retrieval. Compare whether both retrieval results can locate the acceptance record for batch A-0310. The expectation is that both retrievals return that record; if only one returns it, record the actual observation.
- Verification three: input an empty citation list
[]and check whether the downstream citation-generation logic handles it as an empty result. The expectation is that downstream generates no citation content; if non-empty content is generated, record the actual observation.