What data for this category looks like
Typical entities include garment OEM/ODM factories, brand supply chain management departments, and cutting and sewing workshops. Daily generated documents include process sheets, CAD patterns, layout plans, cutting orders, first-piece sealed samples, process quality sheets, and packaging size-matching lists. Data sources cover order and size charts, CAD design files, surface and auxiliary material inspection records, hanging production system data, needle detector test results, and customer sealed sample standards. Data update frequency is once per order, per style, per batch. Core fields include piece number, process requirements, size specifications, batch number, inspection results, etc. Units vary flexibly by document type, with no unified fixed standard.
What constraints these characteristics impose on vector models and indexes
Data for this category has diverse types and high update frequency. Each new order or style change generates a completely new dataset. This requires indexes to support incremental updates without full reconstruction. Cut pieces and processes are closely linked. When splitting chunks, cross-document associated fields must be retained to avoid losing core information for process matching after splitting. Wide variety of styles and strong seasonality cause large fluctuations in data volume. This requires adaptation to mixed input of short text and long image-to-text content. The irreversible nature of fabric cut pieces requires vector retrieval results to accurately associate batches and processes, to avoid traceability errors caused by fuzzy matching.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-page text length of garment process sheets and layout plans, avoiding damage to process association logic after splitting |
| Overlap Length | 100–150 characters | Retains core identifiers across chunks such as piece numbers and batch numbers, preventing loss of associated information at chunk boundaries |
defaultToken | 4096 | Matches the input limit of most general embedding models, adapting to the text length of a single process quality sheet |
maxToken | 8192 | Supports complete text vectorization of long layout plans, avoiding index failure due to exceeded length limits |
| Similarity Threshold | 0.75–0.85 | Combines the weight of process keywords for garment categories, filtering low-correlation non-target batch data |
| Single Search Limit | Top 20 results | Covers multiple inspection records for the same batch and same process, ensuring complete associated data can be obtained during traceability |
Three Common Misconfiguration Issues
- Phenomenon: After calling the pushdata API to upload data in chunk mode, the interface continuously displays the "Indexing" status. Cause:
defaultTokenis not configured, causing missing chunk metadata, so the index task cannot complete verification. - Phenomenon: All returned search results have similar relevance, making it impossible to accurately locate quality issues for the target batch. Cause: The similarity threshold is set unreasonably, and matching weights are not adjusted based on the process keywords for the garment category.
- Phenomenon: After connecting the bce-embedding-v1 model, search results are empty or have extremely low matching degrees. Cause: Vector dimension parameters are not aligned, causing inconsistent spatial mapping between index vectors and query vectors.
How to Confirm Proper Configuration
- Upload a single complete process sheet, check the chunk preview interface, confirm that core fields such as piece numbers and process requirements are not split or lost.
- Input precise questions such as "color difference of left sleeve for a certain batch", verify whether the returned results include surface and auxiliary material inspection records and first-piece sealed sample data for the corresponding batch.
- Check the vector task logs, confirm that the
defaultTokenandmaxTokenparameters do not trigger token overflow errors. - Adjust the similarity threshold to 0.78, verify whether the search results are sorted to prioritize quality records for the target process.
The values provided here are common starting points, and should be evaluated against the reader's own samples.