What this category of data looks like
Typical data for this category comes from dyeing and finishing mills, fabric enterprises' dyeing, setting, laboratory and quality departments. Daily documents include dyeing formulas, chemical dosing records, dye vat curves, proofing confirmations, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color swatches, formula libraries, dye chemical batches, dye vat control equipment, colorimeters, setting machines, and environmental monitoring systems. Update frequency is per vat, per batch, per shift. Document fields include vat number, process temperature, holding time, dye chemical ratio, color difference ΔE value, and others. Conventional corresponding units are ℃, minutes, g/L, and ΔE units.
Constraints on knowledge base retrieval and recall
Data updates occur per vat, per batch, per shift. The retrieval and recall link must support high-frequency incremental synchronization to avoid data lag. Documents include structured numerical reports and unstructured process descriptions. Both semantic matching and precise field matching must be supported to meet retrieval needs across different formats. Multi-source device data and manual records are mixed. Retrieval of text and structured fields from diverse sources must be compatible. Single vat and single batch data have strong correlations. Rapid associated recall of related documents by vat number or batch must be supported to prevent irrelevant data from interfering with retrieval results.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Matches precise numerical values and process descriptions in dyeing formulas and color difference reports, avoids mixing in irrelevant documents with low matching scores |
limit | Top 6–10 entries | The number of valid documents associated with a single vat ranges from 5 to 9. Excessive entries increase context redundancy and reduce retrieval efficiency |
searchMode | mixedRecall | Balances semantic matching and structured field matching to adapt to mixed retrieval needs for process sheets, dye vat curves, and color difference reports |
usingReRank | Enabled | When multi-source documents are mixed, re-ranking improves the sorting priority of highly relevant dye vat control data and color difference reports |
datasetSearchUsingExtensionQuery | Enabled | Optimizes query expressions for industry-specific terminology, improves recall accuracy for specialized process documents |
collectionIds | Bound to three dedicated collections: dyeing, setting, and laboratory | Only recalls valid documents from the relevant departments, avoids interference from irrelevant data across workshops or processes |
Three common mistakes
- Phenomenon: GPU resources are not invoked after executing a retrieval request, and the interface shows search unresponsiveness. Cause: GPU acceleration parameters for the retrieval engine are not configured correctly, or the vector database supporting GPU acceleration is not bound to the knowledge base collection.
- Phenomenon: Retrieval results do not include image content in PDF documents, only returning text fragments. Cause: Multimodal recall configuration is not enabled, or documents in formats that support image-text parsing are not uploaded.
- Phenomenon: After saving retrieval configuration, parameters revert to default values after page refresh. Cause: Configuration changes were not submitted correctly, or browser caching caused interface display abnormalities without actually writing to the knowledge base configuration.
How to verify successful configuration
- Check the configuration items of the knowledge base collection, confirm that parameters such as
similarityandlimithave been saved as preset values. - Enter an industry-specific query, verify that the retrieval results include relevant documents corresponding to the target vat number or batch.
- Upload a test dye vat curve PDF, verify that the retrieval results can recall both document text and image content.
- Restart the FastGPT service, reopen the configuration page, confirm that parameters have not reverted to default values.
The values provided here are common starting points and should be measured against the reader's own samples.