This document is written for FastGPT v4.17.0.
What data looks like for this category
Daily documents for this category include fixture requirement documents, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources are product 3D models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Data updates are executed per project milestones, each product change, and regular calibrations. Document fields include GD&T tolerance values, millimeter-unit parameters from coordinate measuring machine measurements, and calibration cycle identifiers. The character count per single document varies widely based on project complexity.
What constraints these characteristics impose on workflow orchestration
Data sources are scattered but closely linked. Workflows must integrate multi-source heterogeneous documents and set cross-node data pull rules. Update frequency is triggered by projects and changes. On-demand node rules must be configured to adapt to project milestones and change events. Documents contain high-precision tolerance parameters. The review link must retain original field associations, without losing key information such as millimeter units and calibration cycles. Single documents have large character count spans. Workflows must adapt to different document lengths to avoid truncating critical tolerance descriptions. Field formats of multi-source data are inconsistent. A field standardization conversion step must be configured before the workflow starts.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind three core fields: fixture ID, project number, and calibration date | Match the fixed associated fields of category documents to enable cross-node data linkage |
nodeId | Bind the unique identifier of each review node | Facilitate tracking execution logs and result associations for multi-document reviews |
| Code execution module dependency installation path | /app/custom_lib directory inside the FastGPT container | Adapt to the standard custom library path for containerized deployments to avoid missing dependencies |
| Tool call trigger condition | Trigger when GD&T tolerance fields are detected in the document | Precisely match the core verification scenarios for fixtures and gauges |
| Number of parallel AI chat nodes | First 3 nodes | Adapt to the need for parallel review of multiple documents under a single project, avoid resource overload |
| Timeout threshold | 600 seconds | Cover the review and processing duration for complex fixture drawings, calibrated based on actual measurements |
| Maximum character count per single document processing | 8000-12000 characters | Adapt to the average character span of category documents, avoid truncating critical tolerance content |
Three common mistakes
- Code execution module runs with an error, and the console displays "ModuleNotFoundError: No module named 'xxx'". The cause is that the dependency library was not installed to the
/app/custom_libdirectory inside the FastGPT container, so custom dependencies cannot be loaded during runtime. - Tool call module returns no results, and the node status shows "Not triggered". The cause is that the trigger condition for detecting GD&T tolerance fields was not configured, so the fixture review scenario was not recognized.
- The fixture ID and project number bound to
variablesare empty, and subsequent nodes cannot obtain core project information. The cause is that the mapping configuration between variables and document fields was not completed at the workflow start node.
How to confirm the configuration is correct
- Upload a test fixture requirement document with preset fixture ID and project number, check whether the fields bound to
variablesare correctly passed to subsequent workflow nodes. - Run the code execution module that calls the custom library, confirm there are no dependency errors in the console and the module executes normally.
- Upload a gauge operation manual containing GD&T tolerance fields, confirm the tool call module triggers normally and returns review results.
- Start 3 configured AI chat nodes simultaneously, confirm there are no resource conflicts and all nodes complete execution.
The values listed here are common starting points and should be evaluated against the reader's own samples.