Data Characteristics for This Category
Data for sheet metal blanking and bending scenarios comes from CAD/CAM software, order BOM systems, sheet material inventory ledgers, laser cutter and bending machine device logs, and shop floor MES systems. Updates occur per order, daily, and each time a production run is changed.
Daily documents include unfolded drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece records, and process transfer cards. Unfolded drawings note bending compensation values and hole position coordinates. CNC programs include G-code parameters. Nesting diagrams note sheet material utilization rates and leftover material dimensions. Most field units are millimeters, meters, and kilograms.
Constraints for Multi-turn Dialogue and Prompting Workflows
Disparate, multi-source data requires syncing the latest parameters during dialogue initialization to avoid using outdated bending coefficients or nesting plans. Frequent update cycles require limiting dialogue context length, as overly long contexts can introduce old data and cause audit errors.
The high-mix, low-volume order characteristic means prompts must adapt to unique drawings and process parameters for each order, so generic templates cannot be used. Drawing-driven business logic requires multi-turn dialogues to identify specific fields in drawings, such as hole position deviations and bending angles. Cross-role interactions for supplier audits also require explicit binding of sessions to orders, to prevent mixing of dialogue data across different orders.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique business identifier of the current order | Distinguish independent sessions for different supplier audits to prevent data cross-talk |
messages | Retain the most recent 10 interaction records | Limit context length to match the total character count of sheet metal parameters and drawing text |
variables | Pass in the current order's sheet thickness (2–5 mm), bending coefficient (0.8–1.2x material thickness), and nesting leftover data | Provide order-specific process and inventory parameters for multi-turn dialogues to ensure accurate audit basis |
| Maximum context length | 8000–12000 token | Match the total length of unfolded drawing text, CNC program snippets, and interaction records |
| Model temperature | 0.2–0.7 | Control the rigor of audit results to avoid overly divergent deviations in parameter calculations |
| Global variable update rule | Sync process parameters in variables after each interaction | Ensure subsequent dialogues use the latest bending compensation and nesting plans |
Three Common Misconfigurations
- Issue: Global variable values passed during initial dialogue cannot be modified by the model in subsequent turns. Cause: Static variables are only passed during session initialization, and no real-time update rule for
variablesis configured. - Issue: When retrieving a list of dialogue records, AI responses cannot be correctly matched to user questions. Cause: The
messagesarray is not stored in interaction order, orchatIdis not bound to the session's business identifier. - Issue: Individual user question content cannot be retrieved for each turn. Cause: All interaction context is directly concatenated, and no configuration to split single-turn interactions via
messagesis enabled.
How to Verify Proper Configuration
- Start a new supplier audit dialogue, enter a specified order number and sheet material parameters, and check if
variablesloads the BOM and inventory data for that order. - Conduct three consecutive rounds of questioning, and verify that the
messagesarray saves each round's user input and AI response in order. - Attempt to modify a bending compensation value during the dialogue, and confirm that the parameter is updated in
variablesin subsequent interactions. - Call the dialogue record list retrieval API, and verify that the
chatIdof each record matches the current session's business identifier.
The values provided are common starting points and should be measured against the reader's own samples.