What data for this category looks like
This category's data comes from daily business outputs of dyeing and finishing workshops' dyeing, setting, laboratory, and quality departments. It includes dyeing formulas, chemical weighing 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. Updates occur per dye vat, per batch, or per shift. Individual data entries contain fields such as process parameters, batch information, and test results. Some entries include numeric units like temperature, concentration, and time.
What constraints these characteristics impose on model access and configuration
This category's data updates frequently, and individual batch data is highly correlated. Process parameters for each dye vat or batch must be synchronized to the knowledge base in real time. This creates real-time configuration requirements. The data includes many numeric process parameters and time-series curves. The model must accurately match fields and units. Therefore, configuration must limit context and retrieval token usage to avoid interference from redundant information. Process Q&A also requires association with multi-source business data. Configuration must support multi-document associated retrieval. Errors directly affect production batches, so retrieval and generation stability must be ensured. Timeouts or incorrect model calls must be avoided.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category includes long-text data such as dye vat operation curves and multi-batch process sheets. The setting must accommodate associated process parameters and test results to avoid truncating critical information |
quoteMaxToken | 2000–3000 token | Core document fragments such as dyeing formulas, color difference reports, and setting process requirements must be retained. This ensures Q&A can reference complete standardized process details |
temperature | 0.1–0.3 | Process specification and technical standard Q&A must strictly match predefined parameters. Generated content should not deviate from standards, to ensure output accuracy |
AIPROXY_API_ENDPOINT | Fill in a compliant proxy address based on deployment region, to comply with FastGPT v4.17.0 version specifications | Ensures stable model call links and reduces latency risks from cross-network access |
| Workflow/API tool selection | auto | Automatically adapts to different Q&A scenarios such as process parameter queries and abnormal problem troubleshooting, and triggers knowledge base retrieval as needed |
Three common configuration mistakes
- Configuring a Tongyi Qianwen series model returns a 404 status code with no response content. The cause is an incorrectly configured model access path for
AIPROXY_API_ENDPOINT, or using a model version not supported by the current FastGPT version. - Knowledge base retrieval latency increases significantly. Retrieval duration exceeds expectations under the same configuration. The cause is failure to reasonably limit the values of
quoteMaxTokenandmaxContext, leading to loading excessive redundant business documents during each retrieval. - Single-round conversation responses take more than 10 seconds. The cause is failure to adjust the
maxTokensparameter limit. The model generates overly long content, or retrieval matches too many batches of process data, exceeding the call duration threshold.
How to confirm successful configuration
- Check model call logs to confirm there are no abnormal status codes such as 404 or timeouts. Verify that the
AIPROXY_API_ENDPOINTconfiguration matches the model version. - Submit a query related to process parameters. Confirm that retrieved document fragments match the fields and units of actual business data, and verify that the
quoteMaxTokenvalue can cover core document content. - Simulate multi-round conversations. Test process Q&A for different batches. Confirm that response duration meets expectations. Adjust the values of
maxContextandmaxTokensto balance content completeness and response speed. - Check knowledge base update records. Confirm that data is synchronized according to the per-dye-vat, per-batch update rhythm. Verify that the configuration's update trigger rules match the business update rhythm.
The values provided here are common starting points. They should be evaluated against the reader’s own samples.