What the data for this category looks like
Typical entities include bread, pastry, cookie, instant noodle, and frozen pastry factories. Daily documents include formulas and ingredient lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, ovens or frying lines, laboratory testing equipment, and food traceability ledgers. Update frequency is per batch, per shift, or daily. Core fields include allergen labels, baking/frying temperature and duration, packaging date, shelf life, batch number, and others. Some documents use structured table formats, while others use unstructured text. Field structures vary across production stages.
What constraints these characteristics impose on the source citation and traceability workflow
The mixed-format, high-frequency update, and detailed field characteristics of this category impose multiple constraints on the source citation and traceability link. First, mixed-format daily documents require chunking rules adapted to both structured tables and unstructured text, to avoid splitting ingredient rows of formula ingredient lists or time-temperature sequences of baking curves. Second, high-frequency updates per batch or shift require the knowledge base to support incremental synchronization, to avoid overloading the context from repeatedly loading old batch data. Third, the need for accurate extraction of core fields such as allergens and batch numbers requires retrieval results to prioritize matching high-weight fields, while retaining the original data source identifier to ensure traceability to specific production stages. The direct-edible nature of products also requires no errors in traceability information, requiring strict matching of the correspondence between batches and test results.
How to set up the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the field density of single documents in this category, to avoid splitting ingredient groups of formula ingredient lists or complete sequences of baking curves |
quoteMaxToken | 1500–2000 token | Adapts to complete citation of long texts such as metal detection reports and production records, ensuring that all parameter details can be viewed during audits |
| Single Search Limit | Top 6 results | Covers retrieval needs for multi-source data, while avoiding context overload that affects audit efficiency |
responseData | Return only core fields | Only extracts key information such as allergens, batch numbers, and test results, reducing information filtering costs during traceability |
detail | Enabled | Retains the original source identifier of cited data, making it easier to trace back to specific production batches or test reports |
| Incremental Update Switch | Calibrated based on actual testing | Adapts to the high-frequency update rhythm per batch or shift, avoiding repeated loading of old data |
Three Common Errors
- When batch uploading 100 5kb documents, half of the files show training errors in the interface, and the log displays a "format mismatch" error. Cause: Unified chunking rules were not set for the mixed-format documents of this category, causing some structured table documents to be incorrectly split.
- After switching to variable reference mode, the temperature setting button disappears, and the generation parameters cannot be adjusted. Cause: Parameters in variable reference mode need to be passed through the context fields bound by
responseData, and the associated items were not configured correctly. - The {{id}} field in the citation content template returns a null value, making it impossible to locate specific traceability data. Cause: The
detailconfiguration was not enabled, and the system did not retain the unique batch identifier of the cited data, causing the template variable to fail to extract valid information.
How to Confirm the Configuration Is Set Properly
- Upload a complete bread formula document, check whether the citation results include all core fields such as ingredients and allergen labels, and adjust the chunk length to match the document content.
- Batch upload multiple test reports from the same batch, confirm that all knowledge base training statuses are normal, and that duplicate batch data is automatically deduplicated during citation.
- Call the citation template, verify that the {{id}} field returns the unique batch identifier of the corresponding document, and confirm that the
detailconfiguration is active. - Adjust the
quoteMaxTokenparameter, verify that long text citations are not truncated, ensuring that all data from the baking curve is fully displayed.
The values provided here are common starting points, and should be evaluated against the reader's own samples.