This configuration is based on FastGPT v4.17.0.
What this category’s data looks like
Data for this category originates from customer drawings and special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Daily documentation includes control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data is collected continuously on a per-batch or per-shift schedule. Fields include special characteristic identification numbers, process parameter thresholds, batch numbers, inspection values, and conformity judgment results. Units use standard engineering units such as millimeters, newton-meters, and seconds. No additional custom fields are required.
Constraints on Workflow Orchestration
The data for this category is dispersed across multiple sources, updated frequently, and covers diverse document types. Workflows must support parallel pulling of ERP/MES data, measuring tool data, and knowledge base documents across multiple nodes. Workflows must fully retain operation logs and parameter change records for audit nodes, per quality system requirements from vehicle manufacturer customers. Workflows must also adapt to mandatory verification rules for customer-specific special characteristics. High-volume production batch attributes require workflow nodes to be bound to batch number variables, ensuring audit results are accurately linked to corresponding production batches to avoid traceability confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Prompt Optimization | Calibrated based on actual measurements | Aligns with professional terminology for automotive component audits, and matches the document context of control plans and PPAP materials |
| Single Search Limit | Top 8 entries | Covers core audit documents for multiple processes in high-volume manufacturing scenarios, avoiding interference from redundant data |
| Maximum Context Window | 12000 token | Accommodates the full content of multiple 8D reports and SPC reports, preventing truncation of critical audit information |
| Chunk Length | 800–1200 characters | Matches the typical paragraph length of PPAP materials and 8D reports, ensuring retrieval accuracy |
variables | Bound to batch numbers, process parameter thresholds | Links production batches to audit results, ensuring data accurately corresponds to production processes |
| Timeout Period | 600 seconds | Accommodates data pull delays from measuring tools and ERP systems, preventing abnormal workflow interruptions |
Three Common Configuration Mistakes
- Phenomenon: AI responses output by the workflow include unmerged intermediate conversation content, and audit records are incomplete. Cause: Node settings to hide intermediate conversation flows are not configured, causing process conversations to be exposed directly in audit results.
- Phenomenon: After knowledge base retrieval results are passed to an HTTP request node, the AI conversation node cannot correctly reference the retrieved content. Cause: Structured retrieval result data is not passed as required, preventing the AI from identifying associated document sources and content.
- Phenomenon: Batch number association errors occur after workflow execution, and audit results do not match production batches. Cause: The batch number parameter in
variablesis not correctly bound, causing the data association logic to fail, which may lead to traceability confusion for defective batch production.
How to Confirm Successful Configuration
- Trigger a simulated production batch completion event, and verify that workflow nodes start as expected.
- Review workflow logs to confirm that batch numbers and process parameter thresholds in
variablesare correctly passed to all nodes. - Initiate a full audit process, and check that the final output only includes consolidated audit conclusions, with no intermediate conversation content.
- Verify that the HTTP request node corresponding to the specified
nodeIdcorrectly receives knowledge base retrieval results in the required format.
The values provided are common starting points and should be measured against the reader’s own samples.