What this category's data looks like
Daily documents for this category include process sheets, patterns, layout plans, cutting orders, first sample seals, process quality sheets, and packaging size matching sheets. Data sources cover orders and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector results, and customer sealed samples. Data updates occur per order, per style, and per batch. Process details and cutting piece information fields for a single garment style include size, color number, cutting piece number, process node, and similar items. Common units are centimeters, pieces, and batches.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Documents for this category are mostly process-based details and updated frequently. Content volume per style is large. Therefore, the context length of multi-turn dialogue must be strictly limited to avoid redundant information interfering with audit judgments. Order data from different batches has strong correlation. Clear session identifiers must be used to distinguish different audit scenarios and prevent context confusion. Most document fields use non-standard customized content. Prompts must accurately bind variables such as order numbers and style numbers to ensure audit rules match actual production data. The irreversible nature of cutting pieces also requires complete retention of dialogue history. This prevents audit deviations caused by lost context.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 characters | Documents such as process sheets and layout plans for a single garment style have large content volume, requiring coverage of complete process details |
| Maximum single upload file count | 10–15 | The number of documents such as process sheets, patterns, and inspection records for a single batch of orders falls within this range |
variables variable binding | Bind order number and style number fields | Accurate association between production data and audit rules for the corresponding batch is required |
messages historical context truncation | first 20 conversation turns | Only recent quality verification and communication records need to be retained during multi-turn audits to avoid redundancy |
chatId session identifier configuration | Bind order batch number | Distinguish different order audit scenarios for different suppliers and prevent session context confusion |
| Session timeout setting | 600 seconds | The audit process for a single batch of orders usually takes less than 10 minutes, preventing premature session termination |
Three common mistakes
- The dialogue interface fails to correctly pass uploaded process sheet files. The cause is failure to bind the temporary file path parameter via
variables. - Deviations in process requirement matching appear in audit results. The cause is failure to clearly define the boundary between preset rules and actual audit requests in the prompt.
- Output content from multiple modules is displayed simultaneously on the front-end page without distinction. The cause is failure to add clear role identifiers for each audit link via the
messagesfield.
How to confirm correct configuration
- In the FastGPT v4.17.0 environment, call the dialogue interface to upload the process sheet and layout plan for a single garment style. Check whether the
messagesfield returned by the interface contains parsed text of the uploaded files. - Initiate a multi-turn audit dialogue. Confirm that the
chatIdremains consistent to verify that the session context has not been reset. - After configuring
variablesto bind the order number, initiate an audit request. Check whether the output result is associated with the size requirements of the corresponding order. - Test the multi-module output scenario. Confirm that dialogue content from different links is correctly distinguished via the
messagesfield.
The values provided are common starting points and should be measured against the reader's own samples.