What Data for This Category Looks Like
This category of data is generated by a change management team consisting of R&D, process, quality, production, and procurement teams at manufacturing enterprises. Data sources include PLM, ERP, MES systems, supplier materials, validation test records, and customer special requirement documents. Daily output documents include ECN/ECR forms, change assessment reports, drawings and BOM version files, process validation reports, production switching plans, inventory disposal documents, and customer approval documents. Data updates are triggered irregularly with each change. Document fields include change number, version identifier, effective node, associated material code, inspection result, and other items. There is no unified fixed unit, as values vary by document type.
Constraints Imposed on Model Access and Configuration
Dispersed data sources involving multiple systems require configuring pull rules adapted to different system export formats to ensure consistency of cross-source data. Diverse document types with significant structural differences require configuring chunking strategies adapted to different document lengths and field structures to avoid damaging the integrity of change-related association information during chunking. Irregular data update cycles require configuring on-demand trigger synchronization mechanisms to ensure the latest change-related documents are used when calling the model. Documents include sensitive content such as customer approval files, requiring configuring permission control rules to limit access scope for sensitive data. Strong association between material and process information linked to changes requires configuring context association rules to ensure accurate matching of all relevant document content during Q&A.
Configuration Settings
This configuration is based on FastGPT v4.17.0. Specific configuration items and recommendations are as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirement for splicing multiple documents such as ECN/ECR and BOM versions, avoiding truncation of key change-related association information |
maxTokens | 2000–3000 token | Matches the single-response length requirement for equipment inspection and maintenance Q&A, ensuring detailed descriptions related to changes can be fully output |
quoteMaxToken | 1500–2000 token | Limits the single-paragraph length of referenced documents, avoiding context overflow caused by overly long reference content |
temperature | 0.3–0.5 | Reduces random output probability to ensure accuracy of information such as change processes and document versions |
AIPROXY_API_ENDPOINT | Enterprise internal proxy address | Adapts to the requirement for pulling data from multiple dispersed systems, enabling secure forwarding of cross-source data |
AIPROXY_API_TOKEN | Enterprise-specific authentication token | Ensures only authorized change management teams can call model services, protecting sensitive data such as customer approval documents |
Three Common Configuration Mistakes
- Symptom: The model returns a
do_request_failederror. Cause:AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, preventing cross-system data pulling or model calls from being completed. - Symptom: Mixed old and new version content appears in Q&A results. Cause:
maxContextis not configured to adapt to multi-document context, leading to truncation of key version switching node information during model calls. - Symptom: Content related to customer approval documents is not referenced in returned results. Cause: Permission control rules are not configured, causing sensitive documents to be excluded from the recall scope.
How to Verify Successful Configuration
- Check the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, initiate a test request, and confirm there are no connection errors. - Input a test question involving change version associations, and verify whether the returned results include the correct ECN number and BOM version information.
- Import a test document of customer approval files, and confirm that the content of this document can be correctly referenced during Q&A.
- Adjust the
temperatureparameter, verify the consistency of output results, and confirm the parameter takes effect.
The values given are common starting points and should be measured against the reader's own samples.