What the Data for This Category Looks Like
Core data for this category originates from daily business systems and hardware equipment of garment OEM/ODM, brand supply chains, and cutting and sewing workshops. This includes process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality forms, and packaging and size matching documents. Data sources cover order and size charts, raw and auxiliary material inspection records, hanging system process data, needle detector test results, and customer sample seal materials. Updates are triggered per order, per style, and per batch. The data structure centers around pattern parameters, process requirements, size ratios, and raw and auxiliary material specifications. Due to the irreversible nature of fabric cut pieces, data must strictly align with production batches.
Constraints on Vector Models and Indexes from This Category’s Characteristics
The high-frequency incremental updates and multi-dimensional, scattered nature of this category’s data create multiple constraints for vector indexes. The per-order, per-style update rhythm requires indexes to support incremental synchronization, avoiding excessive resource usage from full reconstruction. The multi-field data structure focused on patterns, processes, and sizes requires vector models to accurately capture fine-grained process semantics, preventing parameter confusion across different styles. The irreversible nature of fabric cut pieces requires index metadata to be bound to production batches and order numbers, avoiding data misalignment that reduces quotation accuracy. For bidding scenarios, rapid recall of process and quotation data from historical similar orders is required. Indexes must support precise filtering by style number and order dimensions.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph semantic length of documents such as garment process sheets and layout plans, avoiding splitting that disrupts the integrity of process descriptions |
| Overlap Length | 50–80 characters | Retains overlapping content between adjacent chunks, preventing cross-chunk process parameters from being split and lost |
| Similarity Threshold | 0.72–0.85 | Filters low-match historical process data, adapting to the characteristics of the garment category where there are many styles but large differences in process details |
| Single Search Limit | Top 10 entries | Controls the number of recalled historical orders, avoiding overly long context that impacts the generation efficiency of bidding documents |
maxToken | Set to the selected embedding model/service input limit | This is the configured embedding model's maximum token count, not a batch-processing limit. Check the selected model or provider documentation. See the v4.17.2 model-field reference |
defaultToken | Calibrate using representative process sheets | This is the default token count used when splitting text. Check the resulting chunks against representative source documents; see the v4.17.2 model-field reference |
Three Common Misconfigurations
- Symptom: After creating an index model, the language model cannot read recalled vector data. Cause: The binding configuration between the index model and language model was not completed. As a result, vector retrieval results cannot flow to the language model link, failing to support the process description and quotation logic of bidding documents.
- Symptom: After migrating vector storage from PGSQL, historical order-related process data cannot be recalled. Cause: Metadata fields bound to the index (such as style number, order batch) were not migrated synchronously. The new storage cannot filter and match data by business dimensions.
- Symptom: An indexing task times out. Check the task log, document parsing and chunking results, and embedding provider errors or latency before changing token settings.
defaultTokencontrols the default text-splitting token count; it is not a batch-processing timeout threshold.
How to Verify Proper Configuration
- In FastGPT v4.17.2, upload a single process sheet document, create a vector index, and confirm the index status returned by the interface is successful.
- Enter a query containing size requirements for a specific garment style, and verify that the recall results include historical process data for the corresponding style number.
- Adjust the similarity threshold, compare changes in the number of recall results, and confirm the threshold configuration takes effect.
- Check the task log for indexing errors; verify
maxTokenagainst the selected embedding model/service limit and inspect sample chunks to confirmdefaultTokenproduces the intended splits.
The values provided above are common starting points, and should be evaluated against the reader’s own samples.