What Data for This Category Looks Like
This category covers full production workflows for bread, pastry, cookie, instant noodle, and frozen pastry factories. Daily documents include formula 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 line sensors, laboratory ledgers, and food traceability systems. Updates trigger per production batch, shift, or daily. Core fields include allergen material lists, process window parameters, foreign object detection results, and shelf life labels.
Constraints Imposed on Model Integration and Configuration
Multi-source heterogeneous data, high-frequency updates, and sensitive field attributes for this category create clear constraints for model integration and configuration. Dispersed ERP, production line sensor, and laboratory data have large format differences. This requires configuring model integration logic adapted to multi-source data parsing. High-frequency updates per production batch and shift demand configuration supporting real-time or near-real-time data synchronization rules. High recognition accuracy requirements for core fields such as allergen materials and process window parameters need adjustment of model parameters to ensure result consistency. Long-sequence baking/steaming curve documents need configuration of sufficient context windows to fully parse all time-series data.
How to Configure Settings
This document is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context parsing requirements for long documents such as single-batch complete production records and baking curves, covers multi-segment process data and field verification |
temperature | 0.1–0.3 | Reduces random output probability, ensures result consistency for scenarios such as allergen recognition and process parameter verification |
maxTokens | 2000–3000 token | Limits single-round output length, avoids parsing exceptions caused by overlong audit results, and adapts to structured output requirements for inspection reports |
quoteMaxToken | 1500–2000 token | Limits the maximum length of referenced source data, ensures accurate matching of corresponding fields in formulas and feeding lists during audits |
AIPROXY_API_ENDPOINT | Fill in a dedicated endpoint according to the deployment environment | Adapts to access requirements for domestic or overseas deployments, ensures stability of data transmission |
AIPROXY_API_TOKEN | Bind a dedicated access key | Restricts interface access permissions, avoids data leakage risks caused by unauthorized calls |
Three Common Misconfigurations
- The symptom is a prompt of "receiving message address verification failed" during DingTalk integration. The cause is failure to configure a publicly accessible endpoint, incorrect filling of the public network address corresponding to
AIPROXY_API_ENDPOINT, or failure to enable SSL certificate verification as prompted, leading to matching failure. - The symptom is that the allergen recognition result output by the model omits some materials. The cause is that the
temperatureparameter is set too high, which increases randomness of model output and prevents accurate matching of sensitive fields in formulas. - The symptom is that parsing results for long-sequence baking curves lack data from intermediate time periods. The cause is that the
maxContextparameter is set too small, which cannot cover complete time-series document content.
How to Verify Successful Configuration
- Upload a single-batch complete production record document, verify that the allergen list output by the model matches the material fields in the original document.
- Call the dedicated interface to test the DingTalk integration function, confirm that the receiving address can normally respond to verification requests without error prompts.
- Adjust the
temperatureparameter to 0.2, run the same formula audit task three consecutive times, verify consistency of output results. - Upload a long-sequence baking curve document, confirm that the process window parameters output by the model cover the complete time range.
The values listed here are common starting points, and should be evaluated against local deployment samples.