What the data for this category looks like
Data for this category originates from raw fiber inspection records, ERP systems, real-time spinning and loom data, evenness tester results, fabric inspection machine collected data, and order specification requirements. Daily documents include cotton blending lists, process setup sheets, equipment operation logs, yarn/grey fabric inspection reports, and fabric defect records. Data updates align with each raw material batch, each production shift, and each finished fabric piece. Document fields cover fiber metrics, twist parameters, weave specifications, defect types and locations. No unified fixed units exist; measurement methods such as weight, length, or quantity are matched to the corresponding indicator type.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
This category has high data update frequency, scattered data sources, and document structures that change dynamically with production nodes. Multi-turn dialogue must strictly limit context turns to avoid mixing expired raw material or equipment parameters. Field units vary across different documents. Prompts must explicitly require matching corresponding measurement standards when outputting, and specify the data source type to call at the start of the dialogue. Continuous production defects can spread across fabric. Multi-turn dialogue must retain historical defect records to support associated analysis of exception reports, and avoid missing cross-process abnormal associations.
Configuration Settings
The following configurations are based on FastGPT v4.17.0, and are adapted for drafting process documents and exception reports for spinning and weaving scenarios:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain the first 12–18 dialogue context entries; automatically truncate the earliest non-core records if exceeded, calibrated via actual testing | This category has frequent data updates. Excessively long context can mix expired raw material or equipment parameters, reducing the accuracy of exception report drafting |
variables | Pre-set three business variables: fiber grade, spinning twist, and grey fabric weave | Matches core process fields for this category, reduces repeated input content, and improves dialogue efficiency |
chatId | Bind an independent chatId to each production batch | Isolates production data across batches, avoids cross-batch context confusion, and ensures exception reports are linked to the corresponding batch’s process and inspection data |
| Maximum context token count | 8000–12000 tokens | This category has many document fields, requiring sufficient context to carry process parameters and multi-turn inspection records |
| Image recognition toggle | Enabled | Can directly recognize fabric defect images collected by fabric inspection machines, supplementing fabric surface details not covered by text records |
Three Common Mistakes
- Scenario: After enabling the multimodal image recognition function, uploading a defect image collected by a fabric inspection machine results in a system prompt that image content cannot be provided. Cause: The permission to allow image uploads is not configured in the dialogue node, or the trigger condition for image recognition is not specified.
- Scenario: The generated process document or exception report automatically includes cited document links at the end, and this cannot be disabled via interface settings. Cause: The citation display switch is not turned off in the global configuration, or the prompt does not explicitly require hiding citation content.
- Scenario: After multi-turn dialogue exceeds the set number of turns, subsequent inputs do not trigger valid responses. Cause: The context truncation rule for
messagesis not configured correctly, causing old expired production data to occupy all available context space.
How to Verify Proper Configuration
- Initiate a dialogue covering cotton blending metrics and spinning twist, check if returned content matches the pre-set
variablesbusiness variables. - Upload a defect image, confirm the system can recognize and output a corresponding description of the defect.
- Initiate multi-turn dialogue, gradually increase the number of turns, check if the system automatically clears early dialogue records beyond the set range.
- Generate an exception report, confirm it meets the preset citation display requirements, with no additional unspecified attached content.
The values provided are common starting points and should be measured against the reader's own samples.