What the Data for This Category Looks Like
Core data for this category originates from production links in garment OEM/ODM workshops and brand supply chains. This includes documents such as process sheets, pattern parameters, layout plans, cutting lists, first sample sealing records, process quality sheets, and packaging size matching sheets. Data sources cover order size charts, CAD pattern files, fabric and accessory inspection records, hanging system production data, needle detector test results, and customer sample sealing requirements. Data is updated per order, per style, and per batch. Each individual document includes precise fields such as piece number, fabric direction, size ratio, and process steps. No general statistical abstract fields are included.
Constraints Imposed on Vector Models and Indexing
This category’s data characteristics impose multiple constraints on the vector model and indexing workflow. First, mixed-format data from multiple sources (structured process sheets and unstructured process descriptions) requires retaining field associations during chunking, to avoid damaging key linked information such as pieces and sizes. Second, frequently updated order-level data requires indexes to support incremental synchronization, preventing performance losses from full reconstruction. Third, subtle differences in process details for a single garment style require sufficient semantic matching accuracy from the vector model. Indexes must also support filtering retrieval scope by dimensions such as order and style number. Fourth, the irreversible nature of fabric cut pieces requires high accuracy in retrieval results, to avoid production deviations caused by chunking or matching errors.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 token | Adapts to the context limit of current mainstream vector models, covering the full paragraph length of garment process sheets |
| Chunk Length | 800–1200 characters | Matches the typical length of single process descriptions in process sheets, avoiding cutting that destroys key information such as piece numbers and fabric requirements |
| Overlap Length | 50–100 characters | Retains associated fields such as piece numbers and size ratios across chunks, ensuring coherence in semantic retrieval |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes subtle differences between similar process sheets, avoiding mismatched process requirements for non-target styles |
| Single Search Limit | Top 10 results | Focuses on precisely matched process results, avoiding excessive redundant information interfering with question-and-answer judgment |
defaultToken | 1024 token | Adapts to the typical input length of user process questions, complying with the model call requirements of FastGPT v4.17.0 |
Three Common Misconfigurations
- Symptom: An "undefined model must match" error occurs when switching to a vector model other than ada-002. Cause: The API key for the corresponding model has not been configured on the FastGPT platform, or call permissions for the corresponding model have not been enabled.
- Symptom: Associated data such as pieces and process sheets for a single garment style cannot be matched to the same retrieval result, even though the retrieval score is high but does not match actual process requirements. Cause: Key fields such as fabric direction and size ratio were not retained during chunking, or the similarity threshold was set too low, causing irrelevant results to be recalled first.
- Symptom: Only one vector model can be bound to a single data set, and multiple associated vector configurations cannot be set up. Cause: The multi-vector set function of FastGPT has not been enabled, or field mapping rules for associated data have not been configured per version requirements.
How to Verify Proper Configuration
- Upload a single complete process sheet document, check that the chunking results retain core fields such as process numbers, fabric requirements, and piece counts, with no critical information truncated.
- Submit a query containing specific process requirements, confirm that the chunk sources of the retrieval results match the structure of the original document, with no incorrect matching across unrelated processes.
- Test with different vector models, confirm that no model adaptation errors occur, the consistency of retrieval results meets expectations, and the threshold can be adjusted based on actual recall performance.
- Upload batch-updated order data, check that the index completes incremental updates automatically, with no duplicate or missing document entries.
The values provided are common starting points and should be measured against the reader's own samples.