What Data for This Category Looks Like
Data for this category typically originates from CAD/CAM software, order BOM systems, sheet metal inventory ledgers, laser cutter and bending machine equipment logs, and workshop MES systems. Daily generated documents include 3D part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process transfer cards. Fields include bending compensation coefficients, sheet metal thickness, cutting dimensions, bending angles, process transfer order numbers, and additional relevant metrics. Common units are millimeters, degrees, and pieces. Data updates align with order creation, daily inventory checks, and each production changeover.
What Constraints Do These Characteristics Impose on Workflow Orchestration?
Data sources for this category are scattered, and update schedules are flexible. Workflow orchestration must support real-time synchronization triggers across multiple data sources. Different types of process documents have distinct structural requirements. Unfolding drawings require extraction of bending compensation coefficients. Nesting diagrams require identification of leftover material areas. Workflows must be configured with differentiated parsing nodes. The multi-variety, small-batch production model requires workflows to dynamically adapt sheet metal parameters and process requirements for individual orders via variables. Process parameter errors directly lead to sheet metal waste or assembly failure. Workflows must include parameter validation links to prevent invalid transfers.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Question Optimization | Enabled | Process questions require standardized technical terms to avoid ambiguity, and this leverages the built-in technical term optimization capability of FastGPT v4.17.0 |
| Single Search Limit | Top 3 entries | Core sheet metal process parameters are concentrated in the top 3 matching results, reducing redundant computing overhead |
| Maximum Context | 800–1200 characters | Core process specification parameters have moderate length. Excessively long contexts introduce irrelevant information, while excessively short contexts lose necessary process details |
variables Binding | Bind sheet thickness and bending radius fields from order BOM | Sheet metal bending compensation coefficients are strongly correlated with sheet parameters. Dynamic variables improve the accuracy of Q&A responses |
nodeId Trigger Rule | Bind current process node ID | Process Q&A must match the current production process (cutting or bending) to avoid invalid cross-process Q&A |
| Node Timeout | 600 seconds | Parsing sheet metal process documents requires reading CAD/CAM files, which takes a long time. 600 seconds covers most scenarios |
Three Common Configuration Mistakes
- Phenomenon: Bound knowledge base and agent configurations are lost after restoring a project backup. Cause: FastGPT project backups only save workflow process structures, and do not synchronize associated knowledge base files and agent parameters. Manual rebinding is required.
- Phenomenon: Results from multiple AI chat modules in the workflow are all output, resulting in redundant information. Cause: No output filtering rules are configured for nodes, and no rule is set to retain only the results from the last AI module.
- Phenomenon: Workflow execution times out, with a prompt that the node is unresponsive. Cause: No reasonable single search limit or node timeout is set, causing excessive document matching to consume excessive time.
How to Verify Correct Configuration
- Manually trigger the workflow, input process questions for the corresponding production process, and verify that returned results only include process parameters for the current process.
- Check the fields bound to
variables, confirm that sheet thickness, bending radius and other parameters from the order BOM have been associated. - View workflow node logs, confirm that only the results from the last AI chat module are output, with no redundant information.
- Simulate a backup restoration operation, confirm that bound knowledge base and agent configurations can be restored normally.
The values provided here are common starting points, and should be measured against the reader's own samples.