This page is configured for FastGPT v4.17.0.
What the data for this category looks like
Data for this category comes from customer product models, mold flow or forming simulation files, CAD/CAM output files, machining and measurement data, and trial mold part inspection records. Data updates proceed per individual mold project, and are synchronized after each trial mold or mold modification. Commonly involved documents include mold design task sheets, DFM reports, part drawings, machining process cards, trial mold reports, mold modification notices, and mold history records. Each document contains fields such as mold base dimensions, number of cavities, material grades, trial mold defect descriptions, and more. Units include millimeters, tons, hours, and others.
What constraints these characteristics impose on the workflow orchestration link
Multi-source heterogeneous data sources require workflows to support connection to input nodes of different formats, to avoid data format conversion losses. Updates follow project and trial mold node timelines, so process trigger conditions must be tied to specific project nodes or document upload actions. Diverse document structures and multi-field attributes require splitting key information from different documents using variable storage links, to prevent context confusion. Errors directly impact mass production nodes, so workflows must include clear failure branches and manual review nodes.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Variable storage configuration | Map to variables by document field, with each variable bound to an independent field of the corresponding document | The category documents contain multiple types of structured fields, which need to be stored separately to avoid context mixing |
| Node trigger conditions | Triggered by trial mold report upload or mold modification notice submission | Data updates are synchronized after each trial mold or mold modification, matching project progression nodes |
| Parallel node threshold | Execute the first 2 AI conversation nodes in parallel | Multi-part machining and assembly accuracy reviews require parallel processing to improve process efficiency |
| Tool call timeout | 300 seconds | Mold design data processing takes a long time, to avoid process interruption due to timeout |
| Error branch jump | Point to the manual review node | Errors will affect mass production nodes, requiring rapid intervention for correction |
Three common mistakes
- A "ModuleNotFoundError" occurs during runtime of the code execution node, because the imported library is not installed in the global Python environment of the FastGPT container.
- The tool call node returns no results, because the tool's call parameters are not configured, or the trigger condition does not match the current process node.
- Multiple AI conversation nodes cannot run in parallel, because the
nodeIdassociated nodes are not set correctly, or the parallel switch is not enabled.
How to confirm the configuration is complete
- Trigger a trial mold report upload process, and check whether
variablescorrectly extracts the defect fields and material grades from the report. - Run the code execution node, confirm that the imported library loads normally, with no module missing errors.
- Enable two AI conversation nodes, and check whether they start simultaneously and return their respective review results.
- Trigger the tool call node, and confirm that the tool runs normally according to the configured parameters and returns results.
The values provided are common starting points and should be measured against the reader's own samples.