What Data Looks Like for This Category
Core data for this category typically comes from clothing OEM/ODM factories, brand supply chains, and cutting and sewing workshops. Daily documents include process sheets, patterns, layout plans, cutting orders, first sample seals, process quality tables, and packaging size allocation slips. Data sources cover order and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector results, and customer sealed sample pieces. Data updates are performed per order, per style, and per batch. Process requirements and fabric parameters vary significantly across different orders. Document structures primarily combine structured fields and process description text.
Constraints Imposed on Workflow Orchestration
Since data is updated dynamically per order, style, and batch, workflows must support dynamic binding to the exclusive knowledge base for the corresponding order. This prevents confusion across order process standards. This category’s documents include multiple types of process files with inconsistent formats. Workflow nodes must adapt to field extraction rules for different documents to accurately capture key information such as sizes, fabric batches, and process requirements. Cut pieces are irreversible and manual processes are labor-intensive. This requires high accuracy in Q&A results. The recall and generation stages of the workflow must strictly match the current order’s process parameters to avoid batch deviations or quality defects. The process splitting stage takes significant time. Workflows must reserve sufficient synchronization time to ensure data is complete before executing the Q&A process.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Global variable variables | Bind the exclusive knowledge base for the current order's process sheet and layout plan | Category data is updated per order, requiring dynamic association with the current order's process standard documents |
| Text extraction node | Match cutting order size and fabric batch parameters based on document fields | The category's core data includes size charts and fabric and accessory inspection information, requiring extraction of key business fields |
Code execution node nodeId | Bind the ID of the process splitting result calculation node | The process splitting result from code execution must be passed to the AI model to enable process verification |
| Single search limit | Top 3 results | Category process documents are mostly short texts; excessive recall will interfere with the model's understanding of precise process requirements |
| Context window length | 800–1200 characters | Category process procedure texts are of moderate length; overly long texts will cause the model to process redundant information |
| Timeout threshold | 600 seconds | Waiting for CAD pattern and hanging system data synchronization is required to avoid early termination of the workflow |
Three Common Misconfigurations
- Symptom: The AI model returns the error
chat:ai_input_is_e. Cause: The result of the code execution node'svariableswas not correctly passed to the user question field of the AI model, resulting in empty input parameters. - Symptom: The global variable "Select Knowledge Base" is not dynamically assigned. The returned process standards do not match the current order. Cause: The association between the order ID and the corresponding knowledge base was not bound in the workflow startup node.
- Symptom: The text extraction node returns empty fields. Cause: No document field matching rules were configured, making it impossible to extract key business information such as sizes and fabric batches from layout plans and cutting orders.
How to Verify Correct Configuration
- Launch the test workflow in FastGPT v4.17.0, pass a simulated order ID, and check if the global variable automatically binds to the exclusive process knowledge base for the current order.
- Upload test cutting orders and process sheets, run the text extraction node, and confirm that preset fields such as sizes and fabric batches can be extracted.
- Run the code node to generate process splitting results, check if the AI model's input includes this result data, and confirm there are no missing parameter errors.
- Simulate input of abnormal parameters such as incorrect size matching, and check if the Q&A result returns the corresponding process verification rules and rectification suggestions.
Note: The configuration values listed above are common starting points. Always validate against your own specific samples and use cases.