What This Category of Data Looks Like
This data is generated and maintained by the change management team composed of research and development, process, quality, production, and procurement personnel at manufacturing enterprises. Daily documents include ECN/ECR forms, change assessment reports, drawings and BOM version files, process verification reports, production transition plans, inventory disposal documents, and customer approval documents. Data sources cover PLM, ERP, and MES systems, supplier materials, verification test records, and customer special requirements. Data updates occur without a fixed cycle, and are triggered each time a process or engineering change takes place. Core fields include version number, change scope, verification status, and associated approval nodes.
Constraints on Source Citation and Traceability
Because data sources cover multiple heterogeneous systems such as PLM, ERP, and MES, the citation and traceability link must interface with the formats and permissions of multi-source data to ensure consistency across systems. Because data updates have no fixed cycle and are triggered by each change, a real-time synchronization mechanism must be configured to avoid referencing invalid data from old versions. Because changes require identifying the cascading impacts on design, materials, processes, and inspections, the citation and traceability link must cover all associated design, material, and process data. Because core documents include multiple versions of drawings, BOMs, and process files, version numbers and change nodes must be accurately matched to prevent mixing of old and new versions. Because documents include approval nodes and customer approval documents, a complete traceability chain must be retained to ensure that every citation can be traced back to the original approval and verification records.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 2000–3000 token | Documents for process and engineering changes typically include multi-page forms, version comparisons, and verification records. Sufficient citation content must be retained to support complete traceability |
responseData | Includes version number, change scope, verification status, and approval nodes | Core fields must be included in the citation scope to ensure the traceability chain covers key information across the entire change process |
detail | Enabled | Complete citation source details must be displayed, including document numbers, version numbers, and associated approver information |
| Single Search Limit | Top 6 entries | The number of associated documents related to changes is moderate. Excessive results will increase context redundancy and reduce output accuracy |
| Maximum Context | 8000–12000 characters | Control the total content length after multi-source integration to avoid exceeding the processing limits of large language models |
Three Common Configuration Mistakes
- Phenomenon: Garbled citation marks appear at the end of each paragraph in AI conversation outputs, which revert to standard quotation marks after output. Cause: The format parameters of
responseDataare not configured correctly, leading to abnormal escape handling of citation markers. - Phenomenon: When using a tool call module in a workflow to connect to a knowledge base, expected citation content is not output. Cause: The
detailconfiguration item is not enabled, or the citation fields to be returned are not specified in the tool call parameters. - Phenomenon: When the knowledge base retrieves a question that does not exist in the knowledge base, citations from knowledge base files are still output. Cause: Reasonable matching filtering rules are not configured, causing irrelevant results with low matching scores to be incorrectly included in the citation scope.
How to Verify Proper Configuration
- Initiate a query targeting process changes or engineering changes, and check whether the output content includes clear citation source fields, such as document numbers, version numbers, or approval nodes.
- Review the citation mark format in the AI output, confirm that there are no garbled characters or abnormal symbols, and that the citation content matches the original documents.
- Simulate inputting a question that does not exist in the knowledge base, and confirm that irrelevant knowledge base citation content is not output.
- Trigger the tool call module in the workflow, and check whether the output results include the configured citation field information.
The values provided here are common starting points and should be evaluated against the reader’s own samples.