What the data for this category looks like
Core data for this category comes from business outputs of tooling departments and dedicated tooling suppliers at automotive, aerospace, and mechanical assembly enterprises. Daily documents include tooling requirement documents, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources cover product digital models and GD&T, process routes, coordinate measuring machine inspection data, on-site trial feedback, and metrology systems. Data updates are carried out per individual project, triggered by product changes. Gauges and tools that undergo regular calibration require synchronized updates to their calibration records. Data fields include datum parameters, dimensional tolerances, calibration validity periods, change version numbers, and more. Some drawing documents contain unstructured annotation content.
Constraints on the "Workflow Orchestration" Link
The multi-type document structure of category data requires workflows to adapt to multiple input formats including structured text, drawing annotations, and metrology data. Parsing nodes for multiple file types must be set during orchestration. The update rhythm tied to projects and change triggers requires workflows to bind project metadata and change events. This ensures the process only starts when valid data is updated. The relevance of professional metrology data such as GD&T and coordinate measuring machine data requires workflows to link each step through parameter passing between nodes, preventing data disconnection. Regular calibration records require a validity check step to be added to the process. This stops expired gauge data from entering the audit link. The most time-consuming steps in the category—datum conversion and interference verification—require automatic verification nodes to be added to the workflow. This reduces repetitive manual operations.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Input document classification | Divide by category-specific document types such as tooling requirement documents, fixture drawings, and calibration records | Daily category documents include multiple types of structured and unstructured files. Classifying them improves parsing accuracy |
variables | Bind project numbers, GD&T datum parameters, and calibration validity periods | Data is updated per project. Variables are used to pass core business parameters across nodes, ensuring consistent data across all steps |
nodeId | Link coordinate measuring machine data nodes, acceptance report nodes, and change history nodes | Metrology data and audit results must be connected. Node IDs ensure correct process links and prevent step disconnection |
| Trigger timing | After project submission, after product changes, and after calibration completion | Matches the update frequency of category data, which follows projects, changes, and regular calibrations |
| Text extraction character threshold | 800–1200 characters | Annotation text length on fixture drawings falls within this range, and it fits the parameter range of the FastGPT v4.17.0 text parsing component |
| Recall result limit | Top 10 entries | Fits the keyword distribution of category documents, avoiding redundant data occupying context |
Three Common Misconfigurations
- The text extraction component in the workflow returns empty content, and the issue is resolved after re-adding the component with the same configuration. The cause is that the category-specific document classification tag was not bound during initial configuration. This prevents the component from recognizing the text structure of tooling and fixture documents.
- Some large language models fail to extract text from positioning datum schemes. The cause is that term enhancement configuration was not enabled for GD&T-specific terminology. General large language models cannot recognize professional fields, and some small parameter models have insufficient professional corpus coverage.
- The workflow fails to pass the project number parameter when calling
variables. The cause is that category project metadata was not bound to the process initialization node. This leads to failed cross-node parameter passing, and the corresponding project's acceptance and calibration data cannot be linked.
How to Confirm the Configuration Is Correct
- Upload a typical fixture drawing document, check if the text extraction component returns fields such as positioning datum and clamping requirements.
- Trigger a workflow configured for project submission, check if
variablesincludes core business variables such as project numbers and GD&T parameters. - Verify that the
nodeIdlinks between nodes are correct, ensuring that coordinate measuring machine data is automatically synchronized to the acceptance report node. - Simulate a process trigger after a product change, confirm that FastGPT v4.17.0 automatically starts the workflow and loads the latest change history data.
The values provided are common starting points and should be measured against the reader's own samples.