Data Overview for This Category
This category’s data originates from multiple business documents across carbonated beverage, juice, tea, functional beverage, and packaged drinking water factories. These documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports.
Data is collected from water source and raw material testing systems, formula management systems, online sugar and acidity monitoring equipment, filling production lines, visual inspection devices, and LIMS systems. Updates occur per production batch, per shift patrol, and via continuous real-time collection. Document fields include raw material indicator parameters, production process records, test results, product batch numbers, and other relevant metrics.
Constraints Imposed on Vector Models and Indexes
The multi-source, heterogeneous data characteristics of this category require indexes to support mixed input of structured and unstructured data. Continuously real-time collected production data creates demand for high-frequency updates. Incremental index logic must be used to avoid excessive time spent on full index reconstruction.
Document lengths vary widely, from single online monitoring data entries to complete formula sheets. Flexible chunking rules must be supported to adapt to different content lengths. Professional production fields such as sugar content, acidity, and aseptic parameters require vector models to adapt to food and beverage industry terminology, ensuring accurate semantic recall matching.
Audit scenarios require precise recall of abnormal data. The index’s recall accuracy must meet the strict requirements of process quality review.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Values | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content length of most production records and inspection reports, avoiding excessive irrelevant information in a single chunk |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, preventing cross-chunk semantic breaks in long documents |
| Similarity Threshold | Calibrated based on actual testing | Must be adjusted based on the recall priority of abnormal data to meet precise matching requirements for quality audits |
| Single Search Limit | Top 10 entries | Balances recall efficiency and information completeness for audit reviews, avoiding excessive redundant results |
maxToken | 4096 | Adapts to the input length limit of mainstream vector models, ensuring chunked content can be fully embedded |
| Reranking | Enabled | Improves sorting priority for highly relevant results, supporting the requirement to display key information first for quality audits |
Three Common Configuration Errors
- Phenomenon: After upgrading FastGPT, original CSV-format production record files fail to chunk normally, and the interface displays a chunking failure prompt. Cause: The new version adjusts CSV field parsing rules, and the parsing logic for multi-unit mixed fields unique to the beverage category (such as sugar content and total bacterial count in the same table) has not been updated.
- Phenomenon: The knowledge base indexing task remains in a running state for a long time, and uncompleted production data records show as empty. Cause: Continuously collected real-time data is not batched and organized, and the single batch data volume exceeds the system’s default processing limit, blocking the indexing process.
- Phenomenon: Results returned when calling the knowledge base reference do not match the expected vector model. Cause: Vector model binding parameters have not been updated in the system configuration, and the old model’s index vector format is still in use.
How to Verify Correct Configuration
- Upload a single typical document, such as a complete formula sheet, and confirm that chunking results cover all core content without obvious semantic breaks.
- Submit an abnormal data query, such as a filling liquid level over-limit record, and verify that the sorting and matching degree of recalled results meet audit requirements.
- View indexing task logs to confirm that incremental updates only process newly added production batch data, with no abnormal full reconstruction triggers.
- After switching vector models, verify that recall results for the same query align with the new model’s semantic matching logic.
The values provided here are common starting points and should be measured against the reader's own samples.