What Data Looks Like for This Category
Data for this category typically comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Update frequency follows per order, daily, or per material batch. Daily documents include cutting drawings, cutting lists, edge banding and drilling programs, coating process documents, hardware bill of materials, and assembly and packaging records. Data fields include panel size, material batch identifier, processing parameters, hardware model and installation location. Common units are millimeters, pieces, and batch numbers.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering Workflows
Data for this category updates per order, per batch, or daily, and includes diverse document types. Multi-turn dialogue and prompt configurations must support dynamic data loading and multi-document field recognition. Multi-turn dialogue must carry cross-stage historical data. Context length must accommodate concatenation of multiple documents such as cutting drawings and cutting lists to avoid context overflow. Variables bound to the current order must support dynamic reading of fields including material batch and panel size, to prevent information bias from fixed templates. The number of panels is large and field details are complex. Prompts must clearly specify document extraction rules to avoid confusion of processing parameters across different batches. Cross-session dialogue association must rely on unique identifiers to ensure audit data for different orders does not interfere with one another.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the first 10–15 conversation turns + current order-related document snippets | Adapt to context requirements for multiple documents per single order, avoid overload from historical messages |
variables | Bind current order-specific fields: material batch, panel size, hardware bill of materials | Dynamically load category-specific data that updates per order |
| Maximum single-turn conversation context | 8000–12000 characters | Accommodate text content from multiple documents such as cutting drawings and cutting lists |
chatId | Bind current order number as the unique identifier | Differentiate conversation history and audit logs across different orders |
| MySQL tool call timeout | 300 seconds | Adapt to query and verification workflows for batch panel data |
| Workflow global variable loading | Trigger by current order ID | Match the data update rhythm that occurs per order |
Three Common Misconfigurations
- Symptom: After publishing a guest access link, users delete conversation content, and background logs are cleared synchronously. Cause:
chatIdis not bound as the order unique identifier, leading to invalid association between conversations and logs. - Symptom: Workflow nodes fail to load matching historical global variables when starting a new conversation. Cause: Current order-specific variables are not bound via
variables, and fixed parameters from historical sessions are incorrectly reused. - Symptom: MySQL tool calls return a 400 status code with no response body. Cause: Prompt engineering does not clearly specify category-specific field extraction rules, leading to missing or malformed SQL statement parameters.
How to Verify Proper Configuration
- Initiate a simulated conversation, input a query for the current order's panel size, check if returned results include corresponding document field information.
- Test the conversation deletion operation via the guest access link, verify if corresponding session records are retained in background logs.
- Configure a MySQL tool call, pass simulated order parameters, check if panel processing data is returned normally.
- Trigger a workflow node to load global variables, confirm that the current order's material batch parameters are loaded correctly.
The values provided are common starting points and should be measured against the reader's own samples.