What Data for This Category Looks Like
Data for this category comes from daily production and inspection documents of carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. 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, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection, and LIMS systems. Updates occur per batch, per shift, or via continuous collection. Document fields include core information such as specific feeding amounts, test values, batch numbers, and shelf lives.
Constraints for Knowledge Base Retrieval and Recall Workflows
Continuous data collection for this category requires timely retrieval responses to avoid recalling outdated production records. Batched production and inspection documents must link to retrieval results for the matching batch, to prevent audit errors from cross-batch data confusion. Multiple heterogeneous data sources—real-time equipment monitoring, laboratory reports, system records—create field differences. Define clear retrieval matching rules to ensure recalled content aligns precisely with the current audit scenario. The high-frequency production update rhythm also demands sufficient retrieval link response speed, to avoid delays that reduce on-site audit efficiency.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category has high precision requirements for test data, so low-match irrelevant records must be filtered to avoid incorrect audit results |
limit | Top 8–12 entries | Single-batch document volume for this category is moderate. Too many recalled entries will exceed the model context window, while too few will miss critical test records |
searchMode | mixedRecall | This category has both structured test values requiring vector matching, and keyword fields such as batch numbers. Hybrid mode balances accuracy and recall completeness |
usingReRank | Enabled | Semantic similarity of heterogeneous data requires secondary calibration to avoid mismatches between equipment monitoring data and laboratory reports |
datasetSearchUsingExtensionQuery | Disabled | Audits for this category require strict matching of original production and inspection records. Extended queries may introduce irrelevant content outside production scenarios |
collectionIds | Collection IDs divided by production workshop/document type | This category requires isolating documents from different production links to prevent recall of irrelevant data across workshops or types |
Three Common Configuration Errors
- Issue: Generated audit reports are truncated, returning only partial test values or record fragments. Cause: The
limitparameter was not adjusted, leading to recalled entries exceeding the model context window, or re-ranking was not enabled, causing redundant content to be truncated first. - Issue: After restoring a project backup, the backend knowledge base and agent do not show the corresponding production document content. Cause: The backup only includes project metadata, and associated knowledge base document collections were not synced. Export and import the relevant document collections separately.
- Issue: Retrieval results do not match test records for the current production batch, instead returning content from historical batches or unrelated workshops. Cause:
collectionIdswas not configured to filter document collections for the current production link, or thesimilaritythreshold was set too low, recalling low-match irrelevant records.
How to Verify Correct Configuration
- Upload a single batch of water treatment record documents, run an audit query for the matching batch, and confirm recalled results only include relevant records for that batch.
- Adjust the
similarityparameter to the preset range, run a query for sugar or acidity tests, and confirm recalled results have expected match quality. - Enable
usingReRank, compare the order of recalled results before and after enabling, and confirm re-ranking works to avoid incorrect sorting of heterogeneous data. - Check the knowledge base collection ID configuration, confirm only document types for the current audit link are included, to prevent cross-link data from interfering with retrieval results.
The values provided here are common starting points and should be evaluated against the reader's own samples.