What Data for This Category Looks Like
This category primarily covers factories producing bread, pastries, biscuits, instant noodles, and frozen dough products. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection logs, cleaning and disinfection records, and factory inspection reports. Data sources include raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, production equipment sensors, laboratory test results, and food traceability ledgers. Data updates occur per production batch, shift, or daily. Documents include structured fields such as allergen labels, feeding quantities, equipment operating parameters, and test results. Some documents are time-series temperature and time curve data.
Constraints on Knowledge Base Retrieval and Recall
High-frequency batch-level data updates require retrieval systems to support incremental synchronization and batch archiving, to avoid redundant load from full-scale retrieval. Multiple document types and structured fields require retrieval to cover both text keywords and numerical parameters. A single retrieval mode cannot meet all needs. Frequent product line changes require retrieval to quickly switch the knowledge base collection for the corresponding product, reducing cross-product data interference. Business priorities for allergens and cleaning changes require retrieval to first associate key documents such as allergen materials and cleaning and disinfection records, ensuring accuracy of defect analysis.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Used to accurately match key business fields such as allergens and feeding parameters, filtering low-correlation retrieval results |
limit | Top 8 | The number of valid documents associated with a single production batch ranges from 5 to 7. Excessive results exceed the context window and reduce AI analysis efficiency |
searchMode | mixedRecall | Covers retrieval needs for formula text keywords and numerical parameters such as baking curves and feeding quantities, adapting to multiple document structure types |
usingReRank | Enabled | Re-ranks recall results by relevance to the current defect issue, prioritizing production records directly related to texture deviations and foreign matter problems |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands the retrieval scope based on user input defect issues, associating indirectly related documents such as cleaning and disinfection records and equipment operating parameters |
collectionIds | Bind exclusive knowledge base collections per production batch | Isolates production data from different batches, avoiding cross-batch retrieval interference with quality traceability accuracy, and adapting to high-frequency data update rhythms |
Three Common Misconfigurations
- Phenomenon: Retrieval results include document data from other production batches. Cause: The
collectionIdsparameter is not used to bind the exclusive knowledge base collection for the current batch, and full knowledge base retrieval is used, leading to cross-batch data interference. - Phenomenon: Numerical parameters in baking curves and feeding lists cannot be retrieved. Cause: Only vector retrieval is enabled, and
searchModeis not configured for hybrid mode, so keyword information for structured fields cannot be matched. - Phenomenon: Retrieval response times out. Cause: A reasonable
limitvalue is not set, and too many documents are recalled in one go, exceeding system processing limits. Or incremental synchronization is not enabled, leading to full scanning of historical data.
How to Verify Proper Configuration
- Input clear allergen material keywords for a single batch, check that retrieval results only return feeding lists and cleaning and disinfection records corresponding to that batch, with no unrelated data from other batches.
- Input a specific defect issue such as "bread has a sticky texture", check that retrieval results include baking curves and production records for the corresponding batch, and that the ranking matches business relevance expectations.
- Observe response times after triggering retrieval, confirm that no timeouts occur, and that real-time requirements for the business scenario are met.
- Check the knowledge base collection binding status, confirm that the
collectionIdsassociated with the current session match the current production batch.
The values given are common starting points and should be measured against the reader's own samples.