What Data for This Category Looks Like
Daily documents for this category include tooling requirement documents, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change logs. Data sources include product 3D models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Data updates are carried out per individual project, triggered by each product change, and executed during regular calibration cycles. Documents include fields such as positioning and clamping parameters, detection tolerance values, and calibration validity periods. Single-project datasets are dynamically adjusted along with project phases.
What Constraints Do These Characteristics Impose on Workflow Orchestration
This category’s data characteristics impose three core constraints on workflow orchestration. First, data is updated per individual project, product changes, and regular calibrations. Workflows must support dynamic calling of corresponding datasets per project to avoid cross-project data confusion. Second, daily documents include multiple types of content such as engineering drawing metadata, metrology data, and structured parameters. Workflows must adapt to parsing and association of multiple input formats. Third, bid documents and quotations must be strictly bound to project-specific tooling and gauge parameters. Workflows must implement automatic parameter verification and association to prevent mismatches between datums and drawings.
How to Configure Settings
The following configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Knowledge Base Selection for Knowledge Base Search Node | Bind the knowledge base corresponding to the project ID passed in variables | Data for this category is updated per project. Referencing via variables enables dynamic matching of the current project’s exclusive dataset |
| Prompt for Text Content Extraction Node | Specify extraction of positioning datum, tolerance value, and calibration validity period fields | Core fields for documents in this category are engineering parameters. Precise prompts improve extraction accuracy |
| Workflow Timeout Setting | 300–600 seconds | Single-project bid document preparation includes multiple types of document parsing and data association. This duration covers standard workflows |
| Trigger Condition for Database Query Node | Trigger based on the completion signal of the preceding text extraction node via nodeId | Data for this category must be dynamically queried based on document parsing results. Triggering via nodeId ensures data relevance |
| Maximum Single Search Results | Top 8 entries | Project data volume for this category fluctuates with project complexity. This quantity covers core parameters while avoiding context overload |
| Variable Passing Verification | Enable parameter format verification | Data for this category includes professional formats such as GD&T tolerances and coordinate values. Verification prevents invalid parameters from entering the workflow |
Three Common Mistakes
- Phenomenon: When configuring the knowledge base search node, after binding the knowledge base referenced via
variables, calling the workflow returns "No valid knowledge base matched". Cause: The project-associated knowledge base identifier was not passed viavariablesduring invocation, causing the dynamic matching logic to fail. - Phenomenon: The text content extraction node returns a "Prompt parsing failed" error after running. Cause: The prompt did not explicitly specify the core engineering fields for this category, causing the model to fail to accurately identify target extraction content.
- Phenomenon: The database query node returns no results. Cause: The completion signal of the preceding document parsing node was not bound via
nodeId, causing the query to trigger before data generation.
How to Confirm Proper Configuration
- Pass the
variablesparameter when invoking the workflow, and confirm that the knowledge base search node successfully loads the document set corresponding to the current project. - Trigger the text content extraction node, and verify that the extracted fields such as positioning datum, tolerance values, and calibration validity periods match the source documents.
- View the workflow run logs, and confirm that the database query node triggers based on the completion signal of the preceding node bound via
nodeId. - Simulate a project parameter change, and verify whether the workflow can dynamically switch to the corresponding knowledge base and dataset.
The values provided are common starting points and should be measured against the reader's own samples.