What the Data for This Category Looks Like
This category covers end-to-end documents from bread and pastry, cookie, instant noodle, and frozen pastry factories, including formulas and feeding lists, production records, baking/steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data sources include raw and auxiliary material qualification certificates, ERP/MES systems, weighing equipment, production lines, laboratories, and food traceability ledgers. Update frequency follows production batches, shifts, or daily updates. Document fields include allergen labels, feeding parameters, production temperature and humidity curves, foreign matter detection results, packaging dates, shelf lives, and more. Some documents require linkage to multiple batches of production information.
What Constraints Do These Characteristics Impose on the Vector Models and Indexing Link
Production line changes are frequent for this category, data update frequency is high, and sources are scattered. These factors create multiple constraints for vector models and indexing. Document structures vary across different batches, so indexing must support dynamic adaptation to vector mapping for multiple field types. Highly sensitive allergen and foreign matter detection data requires precise retrieval to avoid missing critical information. Data from multiple sources must be uniformly linked to meta fields such as batch numbers and production line numbers; otherwise, batch-based quality traceability cannot be achieved. High-frequency updates require indexing to support incremental synchronization, avoiding performance losses from full reconstruction, while also matching production rhythms to control refresh cycles.
How to Configure the Settings
The following configurations are based on the interface settings of FastGPT v4.17.0, and involve core parameters such as defaultToken and maxToken.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the length of mixed documents such as formulas and production records, balancing context completeness and retrieval accuracy |
| Overlap Length | 100–200 characters | Match the temporal continuity of production records, avoiding breaks in critical information across chunks |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Adapt to retrieval of sensitive data such as allergens and foreign matter detection, balancing recall rate and precision |
| defaultToken | 4096 | Adapt to basic parsing requirements for single documents, ensuring basic context input |
| maxToken | 8192 | Support full context loading for multi-batch linked documents, meeting long-text requirements for traceability analysis |
| Single Search Limit | Top 10 entries | Cover the retrieval scope of multi-source linked data, avoiding redundant results |
Three Common Misconfigurations
- Allergen fields appear empty in retrieval results. Cause: Vector mapping for allergen fields was not enabled in the index configuration, and vectors were only generated for general text fields, resulting in sensitive information that cannot be retrieved.
- Incremental index update delay exceeds the production shift cycle. Cause: No incremental sync trigger rules were configured, and full index updates were still used, resulting in new batch production data that cannot be retrieved in time, failing to match daily or per-batch update rhythms.
- The number of single search return results exceeds the preset limit. Cause: The single search limit parameter was not set correctly, resulting in too many retrieval results that make it impossible to quickly locate target batch quality data.
How to Verify the Configuration Is Properly Set
- Upload a test document containing a formula and metal detection record, check whether the vector generation status is normal.
- Simulate a retrieval for quality issues related to allergens, verify whether the returned results contain complete information of the target fields.
- Submit new production batch data, check whether the index completes updates within the preset cycle.
- Adjust the similarity threshold parameter, verify whether the precision of allergen-related retrieval meets expectations.
The values provided here are common starting points and should be measured against the reader's own samples.