What Data for This Category Looks Like
This category covers carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data comes from water source and raw material testing, formula systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection systems, and LIMS systems. Updates occur per batch, per shift, or via continuous collection. Fields include feeding ratio, water treatment parameters, filling liquid level, sealing strength, label batch number, and more. Units include percentage, ppm, millimeters, and more. Frequent product switches mean single-batch data links to multi-stage documents.
What Constraints These Characteristics Impose on Source Citation and Traceability
Multi-source, multi-type document data requires precise batch number and timestamp matching during traceability, to link cross-system records. Frequent product switches mean single-batch data covers multiple stages of documents. Retrieval scope must be limited to avoid interference from irrelevant cross-batch content. High-timeliness online monitoring data needs real-time retrieval, and cannot rely solely on static knowledge base archives. Errors carry recall risks, so cited data accuracy must be ensured to avoid traceability mistakes. Structured LIMS data and time-series equipment monitoring data have large format differences. Unified retrieval standards must be adopted to ensure matching efficiency.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | This category links multi-stage documents per batch, with high total token volume. Must cover key stage data including formulas, water treatment, filling, etc., to adapt to the context processing limit of FastGPT v4.17.0 |
| Single recall citation count | `Top 6–8 entries | This category links 3–5 core document types per batch. Too many citations dilute context relevance, too few fail to cover full-stage information required for anomaly troubleshooting |
detail | Enabled | Display specific fields of cited documents such as label batch number, filling liquid level, to meet detailed traceability requirements during inspection and audit |
| Cited data filtering threshold | Calibrated based on actual testing | Online monitoring data for this category has narrow fluctuation ranges. Must match the precision interval of equipment collection, to filter invalid interfering data |
responseData | Return original document ID and batch number | Traceability for this category requires linking batch records from the LIMS system, to quickly locate original archives via document ID |
Three Common Configuration Mistakes
- Issue: The number of citation results returned during invocation does not match the configured number. Cause: No filtering logic configured for the batch association rules of multi-stage documents for this category, leading to irrelevant cross-batch documents being recalled.
- Issue: An error triggers when referencing a knowledge base ID via application variables. Cause: Did not match the document naming rules for this category, directly used a fixed ID without binding batch number parameters, so cannot locate documents for the corresponding production batch.
- Issue: Retrieval results cannot link production batch information from historical conversations. Cause: Context association configuration is not enabled, so traceability cannot reuse previous batch query records.
How to Verify Correct Configuration
- Import three core documents for a single batch: formula, water treatment, filling. Initiate an inspection and audit query, verify that all returned cited documents belong to this batch.
- Adjust the
quoteMaxTokenparameter, check that returned cited content fully covers key fields without truncation. - Simulate feeding anomalies or filling and sealing anomalies, initiate a query, confirm that returned cited data includes anomaly records for the corresponding stage.
- Verify the logic for binding batch numbers in citation variables, confirm that the call correctly associates knowledge base documents for the corresponding batch.
The values provided are common starting points and should be evaluated against the reader's own samples.