What this category’s data looks like
Daily documents for this category include process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality sheets, packaging size matching sheets, and other materials. Data comes from order and size charts, CAD design files, fabric and accessory inspection records, workshop hanging system data, needle detector test results, and customer sample seal information. Teams update data in sync with each order, each garment style, and each production batch. Document fields include specific sizes, fabric color numbers, cut piece quantities, process quality inspection standards, and other details. Formats adjust flexibly to meet order requirements.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
This category has many document types that update frequently with orders. Multi-turn dialogue must bind exclusive context for the current order to avoid mixing data across orders. Cut pieces are irreversible, so review results must accurately match process requirements. Prompts must clearly verify core business fields. Process disassembly relies on multi-dimensional documents. Multi-turn dialogue must guide users to supplement information step by step per process nodes. This avoids omissions caused by inputting too much content at once. Data formats vary widely across sources. Prompts must unify field extraction rules to ensure consistent cross-system data verification.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current order ID | Ensure multi-turn dialogue context only associates document data for the current order, avoiding interference across orders |
messages | Pass in segments according to process nodes | Adapt to the characteristics of many category documents and long single-item content, avoiding model overload |
variables | Inject the current order’s size chart and layout plan ID | Accurately call exclusive business data to match personalized order requirements |
| Maximum context length | 2500–3000 token | Cover complete information of core review documents for a single order, adapt to total business document volume |
| Number of recalled results | Top 6–10 entries | Cover core documents for the current order, avoiding redundant data interfering with review logic |
| Response token limit | 1000–3000 token, calibrated based on actual testing | Adjust based on actual scenarios, as single review content length varies widely |
This configuration is based on FastGPT v4.17.0.
Three Common Misconfigurations
- An error appears in the debug preview interface dialogue, prompting missing required fields. The cause is failure to inject exclusive data for the current order via
variables, causing the dialogue to fail to call process sheets, layout plans, and other documents for the corresponding order. - The maximum response token for some questions automatically changes to 200, resulting in overly short generated content. The cause is failure to explicitly configure the response token limit, or the context length exceeding the preset range, triggering automatic system truncation.
- The dialogue process cannot associate knowledge base content for the current order. The cause is failure to bind context for the current order via
chatId, causing the dialogue to call knowledge base data from other orders.
How to Verify Correct Configuration
- Launch a test dialogue bound to the current order ID, check whether the dialogue context only includes relevant documents for that order, with no mixed data from other orders.
- Input review questions of different complexity levels, confirm that response content length meets expectations, with no unprompted automatic truncation.
- Attempt to supplement new quality inspection results in the dialogue, confirm that the system can associate the information with exclusive knowledge base entries for the current order.
- Check whether core fields from documents, such as sizes, fabric color numbers, are automatically extracted during the dialogue, with no missing fields.
The values provided are common starting points and should be evaluated against samples from the specific deployment context.