What This Category of Data Looks Like
Data for this category covers all stages of printed circuit board factories including engineering, production, quality control and laboratory operations. Daily documents include engineering production instructions, Gerber analysis records, drilling and lamination parameters, bath solution test records, AOI reports, electrical test reports, and slice analysis reports. Data sources include Gerber files, drill tapes, and BOM lists provided by customers, as well as CAM system data, production equipment parameters, bath solution test results, data collected by AOI and electrical test equipment, and laboratory test records. Data updates are triggered per production batch and shift frequency; some equipment data is collected continuously.
What Constraints These Characteristics Impose on Workflow Orchestration
The data sources for this category cover customer-delivered files, internal systems and equipment collection results, with significant differences in format and structure. This requires workflows to support series connection of multiple types of input nodes. Data updates are triggered per production batch, shift or continuous collection, which requires workflows to be configurable to start flexibly based on trigger conditions to avoid unnecessary execution. Documents include multiple types such as parameter-based, record-based, and test report-based content, which requires workflows to distinguish processing logic for different data types. Errors may lead to full batch scrap, which requires workflows to include built-in verification and failure rollback links.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Configure grouped by document types associated with production batches | Data for this category is classified by production batch; grouping allows precise matching of the review logic for corresponding documents |
| Trigger Condition | Trigger after a production batch completion node, with a 600-second delay | Bath solution and AOI data collected by equipment take approximately 600 seconds to complete uploading, to avoid reading incomplete files |
| Node Flow Rule | Proceed to the next link when the output of the previous node is non-empty; otherwise trigger the failure branch | Missing data will prevent cross-review from being completed, requiring early interception of abnormal processes |
nodeId | Unique identifier for marked abnormal nodes; each abnormal log stores no more than 200 KB | Locate the specific erroneous link in the workflow to facilitate quick troubleshooting |
| Output Node Configuration | Only summarize the review results into text of no more than 800 characters before outputting; hide intermediate AI conversation content | Business scenarios only require the final review conclusion, without displaying intermediate generation processes |
| Knowledge Base Retrieval Format | Pass in the customer's Gerber number and production batch parameters; return the top 5 matching documents per call | Core review for this category relies on customer Gerber and production batch data; limiting the number of returned entries improves processing efficiency |
Three Common Misconfigurations
- Phenomenon: After filling all input items in the workflow and running it, the interface displays the error "Cannot convert undefined or null to object". Cause: The grouping association logic for
variablesis not configured, causing document data for some production batches to not be read correctly, and passing null values triggers a type conversion error. - Phenomenon: The generated content of the AI conversation node is directly displayed in the front-end dialog box, and cannot be output after secondary processing via the text splicing component. Cause: The automatic output switch is not turned off in the output node configuration, causing intermediate node content to be pushed directly to the front end.
- Phenomenon: After passing the knowledge base retrieval results to the AI conversation node, the reference sources cannot be correctly associated. Cause: The customer's Gerber number and production batch parameters are not passed in, causing the retrieval results to fail to match the correct review context.
How to Verify Correct Configuration
- Trigger a simulated production batch workflow and check whether the trigger condition starts after the preset delay.
- View the workflow logs to confirm that the document data associated with the
variablesgrouping is correctly read, with no null values passed. - Verify the abnormal branch trigger logic: manually interrupt the previous node's data input to confirm that the workflow jumps to the failure processing link.
- Check the output node to confirm that only the summarized review results are displayed in the front-end dialog box, with no intermediate AI-generated content shown directly.
The values provided here are common starting points and should be measured against the reader's own samples.