What data looks like for this category
The primary entities for this category are tire, seal, hose, and rubber product manufacturers. Daily documents include formulation sheets, weighing records, mixing curves, rubber material quick test reports, vulcanization process cards, mold records, and finished product performance reports. Data is sourced from formulation management systems, raw material batch ledgers, internal mixers, Mooney viscometers, rheometers, vulcanizers, and laboratory testing equipment. Update frequency is adjusted according to production rhythms, covering each raw material batch per truckload, each production batch, and each molded product. Documents contain structured parameters and non-continuous text. Some documents include time-varying process curve data. Fields include business parameters with clear units such as feeding weight, temperature, pressure, and duration.
What constraints these characteristics impose on the vector model and indexing workflow
The high-frequency, small-batch update rhythm requires indexes to support incremental construction and updates, to avoid excessive system resource usage from full reconstruction. The coexistence of multiple document types and structured parameters requires a chunking strategy that balances semantic association between continuous process text and discrete parameters, to avoid breaking logical connections between parameters. Clearly unitized fields must retain unit information during indexing, to prevent incorrect association of similar parameters with different units. At the same time, errors in process parameters can lead to batch scrap or product failure, so index recall must ensure precise matching of key parameters, to avoid irrelevant results interfering with Q&A.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Documents in this category include continuous process parameters and curve data. Excessively long chunks will break parameter associations, while excessively short chunks will damage the integrity of process logic |
| Overlap Length | 100–150 characters | Continuous parameter segments spanning multiple paragraphs exist in mixing curves and vulcanization process cards. Overlap length preserves cross-chunk process association information |
| Similarity Threshold | 0.75–0.85 | It is necessary to distinguish similar rubber material ratios and process parameters, to avoid recalling irrelevant historical batch data |
| Single Search Limit | Top 8 results | Process Q&A for this category requires matching specific batches and equipment models. A small number of precise results can support effective decision-making |
maxToken | 4000–6000 tokens | Associated data from mixing curves, vulcanization process cards, and finished product reports must be integrated, to avoid truncation of key parameters in the context |
| Question Optimization | Calibrated via actual testing | Q&A for this category requires clear association with raw material batches and production equipment. Optimized queries can improve recall accuracy |
Three common configuration mistakes
- Symptom: Recalled mixing parameters do not match the current production batch, and the number of results is far higher than expected. Cause: Indexes are associated only based on text semantics, without using raw material batches and equipment models as chunk metadata for index association.
- Symptom: Index construction times out, and the backend returns a 504 status code. Cause: Incremental indexing mode is not configured. Full reconstruction processes high-frequency updated per-truckload raw material and production data, exceeding system processing time limits.
- Symptom: Some fields in returned vulcanization process cards are empty. Cause: Metadata of key structured fields such as mold model and vulcanization pressure are not retained during chunking, and the vector index is not associated with corresponding business information.
How to confirm correct configuration
- Upload a single batch’s formulation sheet and mixing curve, execute Q&A for corresponding process parameters, and verify that the recalled results include the batch’s feeding data and process parameters.
- Simulate a high-frequency update scenario, add multiple recently produced vulcanization process cards, and verify that the index can complete incremental updates in a short time.
- Adjust the similarity threshold, compare recall results under different thresholds, and confirm that they fall within the accuracy range required by the business.
- View the index metadata management panel, and confirm that business fields such as raw material batches and equipment models have been associated.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.