What This Category of Data Looks Like
Data for this category originates from MES systems, online inspection equipment, laboratory systems, process parameter libraries, customer quality standards, and historical defect libraries. Data update cadence follows per production batch, per shift rotation, or continuous collection of real-time inspection data. Daily documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product review forms, release forms, and quality monthly reports. Fields include inspection batch number, nonconforming item code, release threshold, defect type, inspection time, and more. No unified fixed units exist; specific field units vary by document type.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered, and real-time requirements are high. Multi-turn dialogue must associate homologous data across different systems. Prompt engineering must clearly distinguish inspection logic for work-in-progress and finished products. Differences in update frequencies require dialogue contexts to dynamically adapt to documents with varying time granularities. Batch-level data must link to single-batch inspection records. Continuously collected inspection data must be pulled in real time. Document structures vary. Prompt engineering must adapt to field extraction rules for different documents, avoiding confusion between release forms and nonconforming review forms’ fields. Associations with historical defect libraries require multi-turn dialogue to retain context to track defect relationships, preventing irrelevant data from being called repeatedly.
Configuration Reference for FastGPT v4.17.0
Configuration items for FastGPT v4.17.0 are referenced below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 characters | Adapts to the total content length of multiple inspection reports, review forms, and quality monthly reports, avoiding context overflow |
| Dialogue History Retention Period | 30–90 days | Matches the traceability cycle of production batches, meeting internal quality audit and bid document traceability requirements |
| Single Dialogue Recall Limit | Top 5 entries | Limits the number of recalled historical defect library and inspection records, avoiding information overload that disrupts prompt engineering logic |
variables Binding Rules | Match by document type | Automatically associates exclusive fields of control plans or release forms, improving the accuracy of field extraction in prompt engineering |
chatId Configuration | Bind to production batch number | Ensures dialogue contexts for the same batch are associated, avoiding confusion across batch data |
messages Context Truncation Threshold | Retain the most recent 10 dialogue turns | Focuses on the latest needs of current bid document preparation, filtering expired historical dialogue content |
Three Common Misconfigurations
- Phenomenon: Dialogue response time exceeds 10 seconds, and the interface displays a loading timeout status. Cause: The number of recalled documents is not limited, causing the system to pull excessive historical inspection data and defect records, leading to prolonged model processing time.
- Phenomenon: Unable to query dialogue logs for a specified production batch, or dialogue logs are lost prematurely. Cause: The binding relationship between
chatIdand production batch number is not configured correctly, or the dialogue history retention period is set shorter than the production traceability cycle. - Phenomenon: Fields extracted by prompt engineering do not match actual documents. For example, the qualified threshold of a release form is incorrectly extracted as the number of nonconforming items in a patrol inspection record. Cause: The matching rules for
variablesare not configured by document type, leading to confusion between fields of different documents.
How to Verify Successful Configuration
- Initiate a simulated dialogue, enter inspection requirements for a specific production batch, and verify that the returned context only includes documents related to that batch.
- View the dialogue history list, confirm that the session corresponding to
chatIdcan be queried normally, and that the retention period meets production traceability requirements. - Adjust the single dialogue recall limit parameter, and verify that the number of returned historical data entries matches the preset limit.
- Test input of different types of documents, confirm that
variablescan automatically match exclusive fields of the corresponding documents.
The values provided are common starting points and should be measured against the reader's own samples.