What Data for This Category Looks Like
Typical entities in this category include bread and pastry, cookie, instant noodle, and frozen pasta factories. Daily documents include formulas and ingredient lists, production records, baking or steaming curves, metal detection logs, cleaning and disinfection logs, and factory inspection reports. Data sources cover raw material compliance certificates, ERP/MES systems, weighing systems, production device terminals, laboratory testing systems, and food traceability ledgers. Data updates occur per batch, per shift, or daily. Formulas and ingredient lists are updated when product lines switch. Production records are generated per shift. Baking curves and metal detection logs are archived once a batch production finishes. Document fields include raw material name, ingredient dosage, allergen labels, production batch numbers, temperature parameters, test results, and more. Some device-exported data are structured numerical sequences.
Constraints on Vector Models and Indexing
This category’s data characteristics impose multiple constraints on the vector model and indexing workflow: First, mixed data from multiple sources (structured device data, semi-structured paper transcription records, bulk ERP data) requires chunking rules adapted to different field types. This avoids splitting key associated information. Second, frequently updated batch data requires indexes to support incremental synchronization. This reduces resource consumption from full indexing operations. Third, the sensitivity of formula and allergen information and frequent product line switching require indexes to support field filtering by batch number and product type. Fourth, bid documents need to associate multi-dimensional compliance data. Recall results must cover multiple document types including production records and test reports. Single text matching cannot meet the compliance verification needs of bid preparation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-page text length of formula sheets and production records, avoids splitting key fields such as allergen labels and batch numbers |
| Overlap Length | 100–150 characters | Retains batch number and production date fields across chunks, prevents key associated information from being lost during indexing |
maxToken | 4096 token | Matches the input limit of mainstream vector models, adapts to long text sequences such as baking curves and lengthy production records |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant documents, focuses on compliance-related production and test data required for bids |
| Single Search Limit | Top 10 results | Controls the number of recall results, adapts to the need for quick verification of multiple documents during bid document preparation |
defaultToken | Calibrated via actual testing | Adapts to token consumption fluctuations across different batch data, prevents interruptions during indexing |
Three Common Misconfigurations
- Issue: After upgrading FastGPT v4.17.0, chunking errors occur for CSV-format production record files. The interface displays "incompatible field format". Cause: The automatic recognition rule for numeric fields in CSV files was updated after the upgrade, and does not adapt to vectorization preprocessing of floating-point values such as baking temperatures and ingredient dosages.
- Issue: Knowledge base indexing tasks get stuck. The interface shows "running" for more than 2 hours, and no index files are generated. Cause: No timeout threshold was configured for incremental indexing. Processing batch production records and test reports in bulk exhausts system resources.
- Issue: After calling the file upload API, the interface returns a 200 status code, but it cannot confirm whether file chunking and indexing are complete. Cause: The callback status return configuration for indexing was not enabled. By default, only the file upload success status is returned, and indexing progress is not synchronized.
How to Verify Correct Configuration
- Upload a standard formula sheet CSV file, check that the chunked text contains complete allergen labels and ingredient dosage fields.
- Initiate a simulated search, enter keywords related to the bid, and verify that the number and similarity of recall results fall within the preset configuration range.
- After calling the file upload API, check the indexing completion marker in the system log, confirm that the returned status field contains indexing progress information.
- After switching vector models, verify that the sorting logic of search results does not have abnormal deviations, and meets configuration requirements.
Final Note
The values provided here are common starting points, and should be evaluated against the reader’s own samples.