What the data for this category looks like
Typical entities for this category are bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include formulas and feeding 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, oven or frying line equipment, laboratory test data, and food traceability ledgers. Update frequency is per batch, per shift, or daily. Document fields include allergen material identifiers, proofing/baking/frying window parameters, packaging dates, shelf life, and other fields. Fields and units must be adapted to the original records of individual documents.
Constraints on model access and configuration from these characteristics
Multi-source heterogeneous data sources require model access to support different formats of device logs, production records, and laboratory data. Frequently updated business data requires context configuration to adapt to rapidly refreshed document content, preventing recall of outdated information. The business characteristics of allergen control and frequent line changes require the model to accurately retrieve associated data for specific fields. It also requires strict rigor for maintenance Q&A, avoiding irrelevant generated content. In addition, the directly edible product attribute requires that inspection and maintenance suggestions from the model comply with food production hygiene specifications, and must not introduce potential contamination risks.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the context carrying requirements of long documents such as single-batch production records and complete baking curves for this category |
temperature | 0.2–0.4 | Ensures the answers to equipment inspection and maintenance Q&A are rigorous and accurate, avoiding generation of irrelevant or divergent content |
AIPROXY_API_ENDPOINT | Fill in according to the deployed large model service address | Adapts to local or privately deployed large model invocation scenarios, meeting the data privacy requirements of food factories |
quoteMaxToken | 1500–2000 tokens | Recalled document fragments must match the core content length of a single formula or cleaning and disinfection record |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools for scenarios such as inspection parameter queries and equipment status judgment |
Three common configuration errors
- A parameter mismatch error occurs when calling a local large model. The root cause is failing to adjust the
maxTokensvalue based on the actual context window of the local model. Directly using default parameters from an online API causes the request to exceed the model's carrying limit. - A version incompatibility prompt appears when connecting the mineru tool. The root cause is installing the latest official mineru version directly, without using the version compatible with FastGPT v4.17.0. This leads to mismatched interface invocation logic.
- Production curve images fail to parse after configuring the
visionparameter. The root cause is either not uploading baking or steaming curve images that meet specifications, or not enabling the food production document-specific adaptation rules for the vision tool in the configuration.
How to verify a successful configuration
- Submit an equipment inspection-related query, and confirm the returned result includes production records or baking curve fragments for the corresponding batch.
- Test a large model interface call, and confirm the returned response format and fields match the preset requirements for equipment maintenance Q&A.
- Check whether the configured
AIPROXY_API_TOKENmatches the deployed large model service key, to avoid invocation interruptions caused by authentication failures. - Trigger an allergen control-related query, and confirm the model retrieves allergen material control data from cleaning and disinfection records.
The values provided are common starting points and should be measured against the reader's own samples.