What This Category's Data Looks Like
Data for this category comes from dyeing, setting, quality control, and laboratory departments of dyeing and finishing plants. It includes dyeing formulas, chemical dosing records, dye vat operation curves, sample confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color samples, formula libraries, dye chemical batch records, dye vat control systems, colorimeters, setting machines, and environmental monitoring equipment. Update frequency is per vat, per batch, or per shift. Documents contain fields such as dyeing temperature curves, chemical mixing ratios, color difference values, and emission indicators. Units include degrees Celsius, grams per liter, pH value, and cubic meters, among others. Data updates in real time alongside corresponding processing batches.
Constraints Imposed by These Characteristics on HTTP Interfaces and External Systems
Category data updates in real time with each processing batch. Interfaces must support pulling the latest data using batch identifiers. Do not use expired dye vat curves or formulas. Multi-source heterogeneous data originates from equipment and business systems. Interfaces must support parameter passing in varied formats, adapting to numeric fields from colorimeters and text fields from process sheets. Single-batch data volume varies by vat run. Interfaces must support dynamic configuration of the number of data entries returned per request, to avoid timeouts or overflow. Sewage ledgers and environmental monitoring data must be synchronized strictly according to update schedules. Interfaces must carry batch timestamps to ensure data consistency.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {custom application key} | Complies with FastGPT API authentication standards, ensures secure and legitimate interface access |
variables | {"batchId": "current vat number", "workshop": "production workshop"} | Matches the unique identifier of dyeing batches, allows the Q&A model to associate dye vat curves, formulas, and maintenance history data for the corresponding vat run |
responseData | Return only structured Q&A results and associated document IDs | Enables external systems to quickly parse dye vat inspection items, maintenance suggestions, and other content, adapts to the integration parsing logic of business systems |
| Single request timeout | 30–60 seconds | Dyeing batch data requires loading multiple documents and completing model inference. This interval covers the full process, and can be adjusted based on actual testing |
| Context window length | 8000–12000 token | Must carry complete process sheets, dye vat parameters, and historical maintenance records for a single vat run, prevents critical business information from being truncated |
Three Common Configuration Mistakes
- The interface returns a
401 Unauthorizedstatus code. This occurs when theAuthorizationparameter is not carried correctly, the key configuration is incorrect, or the authentication information does not match the legitimate credentials for the corresponding FastGPT application. - The interface call returns empty Q&A results. This occurs when key identifiers such as batch numbers are not passed via
variables. The model cannot associate dye vat data and process documents for the corresponding vat run. - The interface call times out. This occurs when the volume of data loaded per request, such as dye vat curves and color difference reports, is too large. It exceeds the preset timeout interval, and the timeout setting is not adjusted based on actual data scale.
How to Verify Correct Configuration
- Call the interface with a test vat number. Check the returned authentication status. Ensure there are no 401 or 403 errors.
- Review the
responseDatareturned by the interface. Confirm that it includes the inspection item list and maintenance suggestions for the corresponding vat run. Associated document IDs should match locally stored dye vat data. - Adjust the batch number in
variables. Verify that data for different vat runs can be correctly associated. Ensure the content returned by the model changes with the batch. - Simulate a high-data-volume request. Verify that the timeout setting covers the data loading and inference process. Ensure no timeout errors occur.
The values given are common starting points and should be measured against the reader's own samples.