What Data Belongs to This Category
Data for this category comes from formula management systems, raw material batch records, internal mixers, Mooney viscometers, vulcanizers, and laboratory testing equipment. Daily work documents include formula sheets, weighing records, mixing curves, rubber quick-test reports, vulcanization process cards, mold usage records, and finished product performance reports.
Data updates occur per raw material batch, per rubber compound batch, and per molded product. Fields include formula component proportions, feeding sequence, mixing temperature and duration, vulcanization time and pressure, mold number, and more. Units include degrees Celsius, minutes, batch numbers, and others.
This document is written based on the configuration logic of FastGPT v4.17.0.
What Constraints Do These Characteristics Impose on the Vector Model and Index Link
This category has high data update frequency, diverse document types, and strong binding to equipment operation, which imposes multiple constraints on the vector model and index link. High-frequency updates require indexes to support incremental synchronization to avoid performance loss caused by full reconstruction. Multiple document types such as time-series mixing curves and text process cards need adapted chunking logic to ensure complete feature extraction. The associated requirement for equipment inspection and maintenance requires indexes to accurately bind equipment parameters, process records, and maintenance history fields to avoid irrelevant recall. Data errors directly lead to product scrap, so recall accuracy of indexes must be strictly controlled to reduce invalid returns.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the text lengths of different documents such as mixing curves and process cards, ensuring that each chunk contains complete process nodes or curve segments |
| Overlap Length | 50–100 characters | Retain process association information between adjacent chunks, avoiding chunking that cuts off key logic such as mixing temperature changes and vulcanization timelines |
| Similarity Threshold | 0.75–0.85 | Filter irrelevant records such as formulas and raw material batches that are unrelated to equipment inspection and maintenance issues, reducing invalid recall |
| Single Search Limit | Top 8 results | Match the high-frequency equipment fault troubleshooting scenarios of this category, control the information volume of returned results to avoid context overload |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Question Optimization | Enabled | Supplement context such as equipment model and process parameters to allow the vector model to more accurately match maintenance scenarios for mixing and vulcanization processes |
Three Common Mistakes
- Phenomenon: For inspection questions about abnormal mixing temperature, the knowledge base returns irrelevant raw material batch records. Cause: No reasonable similarity threshold is set, or documents are not filtered by fields for equipment inspection and maintenance scenarios.
- Phenomenon: Index status shows not ready, and search cannot be triggered. Cause: No incremental synchronization trigger rule is configured, or index construction is not completed due to high-frequency updates.
- Phenomenon: When using full table data as the knowledge base, returned results have no relation to the query. Cause: No limit is set on the recall range of the index, and fields strongly related to equipment maintenance are not extracted as index features.
How to Confirm Proper Configuration
- Submit typical inspection and maintenance questions such as abnormal mixing temperature and under-vulcanization, and verify whether returned results include relevant documents such as mixing curves and vulcanization process cards.
- Check the index status, and initiate search tests only after confirming the status shows ready.
- Adjust the similarity threshold to confirm whether the relevance of returned results meets business requirements.
- Test newly generated mixing records, process cards and other high-frequency updated documents from the same day to confirm whether they can be correctly indexed and recalled.
The values provided here are common starting points and should be measured against the reader's own samples.