What Data for This Category Looks Like
The data for this category mainly comes from orders and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector test results, and customer sealed samples. Daily documents include process sheets, patterns, layout plans, cutting orders, first article sealed samples, process quality sheets, and packaging size matching sheets. Data updates follow the rhythm of each order, each style, and each batch. Core fields include size specifications, cut piece quantity, process requirements, fabric batch number, packaging ratio, and more. Cut piece data is irreversible and synced in real time with production progress.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Words
Since data updates are tied to orders, styles, and batches, multi-turn dialogue must bind the context of the corresponding production batch via chatId to avoid mixing cross-order data. Core documents include multiple types of process and quality inspection fields, so variables must be used to pass exclusive parameters such as the current order’s size chart and process sheet. This ensures audit prompt words accurately match the requirements of the current style. The irreversible nature of cut piece data requires every dialogue turn to verify the compliance of the current link, preventing deviations that cannot be corrected later. Additionally, the wide range of styles and strong seasonality require prompt words to explicitly bind the rules of the current production style each time they are called, avoiding interference from cross-style data.
How to Configure
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind production order number | Distinguish context for different orders to avoid mixing cross-batch data |
variables | Pass the current style’s process sheet and size chart ID | Accurately deliver exclusive parameters for the current production style without repeated entry |
messages context retention count | First 3 valid dialogue turns | Filter redundant history and focus on dialogue content for the current audit link |
| Maximum tokens per single dialogue | 8000 token | Adapt to content input for long documents such as process sheets and layout plans |
| Timeout threshold | 600 seconds | Wait for process data synchronization to complete during multi-turn verification |
| Context input upper limit | 10000 characters | Adapt to spliced input for multiple process documents |
Three Common Misconfigurations
- Phenomenon: The reply from a previous AI dialogue in a workflow is used as input for a second dialogue, and the final result includes content from the first dialogue. Cause: No context filtering rules are set in the dialogue configuration, so historical messages are not truncated.
- Phenomenon: A workflow is configured with an HTTP request for online search, but the AI dialogue is called directly during actual execution. Cause: The HTTP request node is not set as a pre-trigger node, or the flow logic between nodes is configured incorrectly.
- Phenomenon: Voice input-related tools cannot be called in a container deployment environment. Cause: Permission to install dependencies is not enabled in the container permission configuration, or tool dependency packages are not mounted correctly.
How to Confirm Proper Configuration
- Create a dialogue for a test order, pass the
variablesparameters for the corresponding style, and check if the dialogue context only associates the process requirements of the current order. - Trigger the workflow, view the node flow logs, and confirm that the HTTP request node executes before the AI dialogue node and returns valid data.
- Test multi-turn dialogue, input quality inspection data for different batches, and check if the system can correctly distinguish parameters for different batches.
- Check the container permission configuration, confirm that the installation path of dependent tools is correct and can be called.
The values provided are common starting points and should be measured against the reader's own samples.