What data for this category looks like
Daily data for this category includes documents such as process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality sheets, and packaging and size matching sheets. Data sources include order and size charts, CAD design files, surface and auxiliary material inspection records, workshop hanging system data, needle detector records, and customer sample materials. Data updates follow the rhythm of each order, each style, and each batch. Fields include size specifications, cut piece quantity, process nodes, fabric batches, packaging and size matching, and more. Cut piece-related data is irreversible, and most process records are generated through collaboration between humans and equipment.
What constraints these characteristics impose on vector models and indexing
This category has many styles and strong seasonality. Single-batch data volume fluctuates with order scale. Cut piece and process data are closely linked, requiring precise matching of associated fields. Data update frequency is high, requiring indexes to support near-real-time incremental updates and avoid full-table repeated scans. Document types include structured tables and unstructured drawings, requiring adaptation to mixed-format text and image feature extraction. Since cut piece data is irreversible and process records are dense, indexes must retain full historical associated data, not just the latest batch. At the same time, recalled data must cover the complete process and material chain, avoiding missing key verification fields.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-block text length of documents such as process sheets and process quality sheets, avoiding cross-process information fragmentation |
| Overlap Length | 100–150 characters | Retains cross-block associated information such as process nodes and size specifications, preventing key fields from being split across different chunks |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Matches highly similar fields such as process descriptions and size parameters for the garment category, avoiding false recalls or missed recalls |
| Single Search Limit | Top 10 results | Covers associated data for multiple processes and multiple cut pieces in a single order, avoiding excessive redundant information in a single recall |
maxToken | 4096 token | Adapts to the long-text generation requirements of batch ledger summary, ensuring complete order, cut piece, and packaging information is included |
| Maximum Context | 8000 characters | Retains associated data across multiple batches, supporting cross-order process comparison and verification |
Three common configuration errors
- The symptom is that the index does not show a ready status, and retrieval requests cannot be initiated. The cause is that the access parameters for the vector model are not configured correctly, or the incremental update task has not been initialized.
- The symptom is that retrieval results include unrelated fields across the entire table, and target ledger content cannot be accurately matched. The cause is that a reasonable similarity threshold is not set, or the chunk length is too large, causing associated information to be wrapped redundantly.
- The symptom is that retrieval returns empty fields, and key data from cutting orders and packaging and size matching sheets cannot be extracted. The cause is that target fields are not included in the extraction scope of the vectorized index, or data updates have not been synchronized to the index nodes.
How to confirm the configuration is correct
- Access the index management page of FastGPT v4.17.0, confirm that the target index status shows ready, and check that the vector model access configuration matches the selected model.
- Enter the process description of a single order as the retrieval term, verify that the retrieval results include associated fields from the corresponding cutting order and process quality sheet, and confirm that the field extraction scope meets requirements.
- Submit a generation request for batch ledger summary, check whether the returned content covers all key data of the current batch, and verify that the parameter values adapt to long-text generation requirements.
- Simulate a single data incremental update, confirm that the index status does not report errors and new data can be retrieved normally.
The values listed are common starting points and should be evaluated against the user’s own samples.