What Data for This Category Looks Like
Data for this category originates from dyeing plants’ dyeing, setting, laboratory, and quality departments. It includes documents such as dyeing formulas, chemical weighing records, dye vat operation curves, sample confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources cover customer color samples, formula libraries, dye chemical batch records, dye vat control systems, colorimeters, setting machine parameters, and environmental monitoring data. New data is generated per vat, per batch, or per shift. Fields include process parameters, batch numbers, test values, timestamps, and more. Units involve degrees Celsius, grams per liter, gram weight, CIE chromaticity values, and others.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The batched, frequently updated data of this category requires multi-turn dialogue to associate full-link data for a single vat or single batch. Prompts must clearly limit retrieval scope to documents for the current order or batch. Wet chemical processing has strong process correlation. Multi-turn dialogue must progress in the order of processes such as dyeing and setting. Prompts must include field verification logic for process nodes to avoid mixing cross-process data. The data includes real-time test values and historical formulas. Prompts must distinguish between historical data for traceability and current production data to avoid mixing batch information. The data volume for a single vat is concentrated, so the context window must be limited to avoid redundant information interfering with inference.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context | First 8 turns of dialogue + historical data from the last 2 vats | Matches single-vat/cross-vat traceability needs for dyeing and finishing batch processing, avoids excessive redundant data interfering with inference |
| Question Optimization | Calibrate based on actual measurements | Adapts to specialized terminology of dyeing processes, optimizes semantic understanding of user queries |
| Single Search Limit | Top 6 retrieval results | Controls information input volume per dialogue turn, adapts to batch correlation of dyeing and finishing data |
variables Variable Binding | Bind batch number and order number fields | Accurately associates production batch data corresponding to chatId, narrows retrieval scope |
messages Context Truncation | Retain the last 3 turns of process dialogue | Focuses on problem analysis for the current process, avoids irrelevant historical dialogue affecting results |
Three Common Configuration Errors
- Phenomenon: Empty results are returned when calling the knowledge base association interface in the debug preview interface. Cause: The current batch number is not bound via
variables, and the retrieval scope covers all data, which cannot match the target vat data. - Phenomenon: Single-turn dialogue response content is suddenly truncated, and the maximum response token is automatically limited to 200. Cause: No
messagescontext truncation rule is configured, and excessive historical data triggers the platform’s built-in length limit. - Phenomenon: The question optimization step takes more than 10 seconds. Cause: The specialized term optimization switch is not enabled, or the prompt does not limit the retrieval scope of dyeing and finishing processes, leading to invalid semantic expansion.
How to Confirm Successful Configuration
- Enter the FastGPT v4.17.0 debug interface, enter a quality problem that includes a batch number, and check whether retrieval results only return dye vat curves and color difference reports for the corresponding vat.
- Initiate a multi-turn dialogue, sequentially ask about the dyeing process, color difference cause, and rectification plan, and confirm that retained dialogue turns conform to the configured maximum context rules.
- View the
variablesvariable binding status, confirm that the batch number field is associated with the current dialogue’schatIdto avoid mixing cross-batch data. - Test long-text queries with high token requirements, and confirm that response length is not automatically limited and conforms to preset maximum response parameters.
The values provided are common starting points and should be measured against the reader's own samples.