What this category's data looks like
Data for this category comes from multiple systems and electronic or paper documents of bread and pastry, biscuit, instant noodle, and frozen pastry factories. It includes formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, factory inspection reports, and more. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing equipment, ovens or frying lines, laboratory testing, and food traceability ledgers. Update frequency shifts with production batches, shifts, or daily cycles. Document fields include allergen labels, feeding weight, baking temperature window, shelf life, cleaning and disinfection frequency, and more. Some documents require association with multiple batches of production data.
What constraints these characteristics impose on model access and configuration
Multi-batch, high-frequency updated documents for this category require model access to adapt to incremental synchronization logic, to avoid excessive resource usage from full refreshes. Rich segmented fields such as allergen labels, feeding weight, and baking temperature window require precise field matching during retrieval, to avoid generalized recall of irrelevant content. Ready-to-eat product attributes raise accuracy requirements for Q&A related to allergens and foreign matter control, so redundant data called by the model must be limited. Frequent line changes require configuration to support quick switching of knowledge base document sets for different product lines, to avoid cross-category data interfering with the accuracy of production process Q&A.
How to set the configuration
This configuration guide is written for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long documents of process specifications and production records for this category, avoiding truncation of key parameter content |
maxTokens | 2000–3000 token | Matches content requirements for Q&A about process parameter explanations, balancing response speed and information completeness |
quoteMaxToken | 1500–2000 token | Limits the length of a single quoted document, avoiding overly long quotes that confuse key information such as allergens and baking windows |
temperature | 0.1–0.3 | Reduces answer randomness, ensuring accurate and consistent responses for deterministic content such as process parameters and allergen labels |
| Workflow/API tool selection | auto | Calls retrieval tools as needed, adapting to real-time data call requirements for different process Q&A |
AIPROXY_API_ENDPOINT | Fill in the address provided by the selected model service provider | Adapts to network call rules for model access, ensuring normal data transmission |
Three Common Mistakes
- Phenomenon: Semantic retrieval scores 0.77, but the model returns that no relevant information is found. Cause: Retrieved documents do not intercept key fields, or quoted token amounts exceed configuration limits, causing the model to fail to extract valid information.
- Phenomenon: Response timeout occurs when knowledge base query traffic is high. Cause: Values for
maxContextorquoteMaxTokenare not reasonably limited, causing too many tokens to be transferred per call, exceeding the traffic bearing range of the model interface. - Phenomenon: The model confuses allergen labels of different product lines. Cause: Knowledge base document sets are not split by product line, leading to retrieval and recall of cross-category redundant data that interferes with model judgment.
How to confirm proper configuration
- Submit a process Q&A related to allergens, and check whether documents referenced by the model come from relevant records of the current production batch.
- Adjust the
temperatureparameter, then submit multiple identical process parameter Q&As, and confirm consistency of response content. - Simulate calls under high-traffic scenarios, and check whether interface response time meets threshold requirements expected by the business.
- Switch knowledge base configuration for different product lines, and confirm that Q&A content automatically matches process documents of the corresponding product line.
The values provided are common starting points and should be measured against the reader's own samples.