What This Category’s Data Looks Like
Core data for this category originates from garment OEM/ODM factories, brand supply chains, and cutting and sewing workshops. Daily stored documents include process sheets, layout plans, cutting orders, first sample seals, process quality forms, and more. Data sources cover order size charts, CAD pattern making files, fabric and accessory inspection records, hanging production system data, and customer sealed sample information. Update frequency adjusts alongside each order and production batch. For a single garment style, cut pieces and process documents are clearly linked, with fields including size specifications, fabric batches, sewing process requirements, and others. Cut piece data is irreversible and has no repeated reuse potential.
Constraints Imposed on Knowledge Base Retrieval and Recall
The high-frequency updates and single-style binding characteristics of category data require knowledge base retrieval to accurately match the exclusive parameters of the current production batch, avoiding interference from cross-batch data. The irreversible nature of cut piece data requires recall results to be strictly linked to the size and fabric requirements of the corresponding order, preventing batch scrap caused by mismatches. In scenarios where multiple document types (including CAD drawings, paper scans) are stored together, retrieval must support associated recall across document types. It also limits irrelevant entries per single recall to avoid disrupting the logic of drafting process documents and exception reports. Additionally, the dense process characteristic requires retrieval results to be sorted by process priority, assisting in quickly locating the process requirements for the current link.
Configuration Settings
This configuration is based on FastGPT v4.17.0. Specific parameter settings are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.70–0.85 | Adapt to the accurate matching requirements of garment process parameters, filter low-relevance cross-batch process documents |
limit | Top 5–7 entries | The number of associated process documents for a single garment style is limited, control the number of recalled entries to avoid information overload |
usingReRank | Enabled | Re-rank multiple types of recalled documents such as process sheets and layout plans, prioritize returning content required by the current process |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand retrieval keywords to cover hidden associated parameters such as order sizes and fabric batches |
collectionIds | Exclusive knowledge base ID for the current production batch (1000 MB calibrated based on actual testing) | Bind the exclusive data set of a single order to avoid cross-batch data interfering with retrieval results |
Three Common Configuration Mistakes
- Phenomenon: Retrieval results fail to return image documents such as CAD templates and layout plans, or the returned images fail to load properly. Cause: The vectorization index configuration for non-text format documents is not enabled, and only text content is vectorized.
- Phenomenon: When drafting exception reports, retrieval results fail to link to the historical exception records of the current order, and classification results are often assigned to default categories. Cause: The retrieval logic does not introduce context history parameters, and only performs recall based on current input keywords, unable to match batch-associated exception data.
- Phenomenon: The proportion of irrelevant entries with extremely low similarity returned by retrieval is too high, interfering with quick location of process requirements. Cause: The
similarityparameter is set too low, failing to filter low-relevance content.
How to Verify Correct Configuration
- Manually enter the size specification keywords of the current order, verify that retrieval results only return exclusive documents such as process sheets and cutting orders for this order.
- Upload a CAD template image of the current batch, confirm that retrieval results can correctly link to the process requirements and process instructions corresponding to this image.
- View the retrieval log, confirm that the number of recalled entries falls within the range specified by the preset
limitparameter, with no unexpected entries. - Simulate entering context keywords including historical exception records, confirm that retrieval results can link to the exception data and treatment plans of the corresponding batch.
The values given are common starting points and should be measured against the reader's own samples.