What This Category's Data Looks Like
Data sources for this category include milk source testing, MES systems, homogenization and heat treatment equipment, online temperature and flow monitoring devices, CIP systems, and LIMS laboratory management systems. Data updates occur in real time per milk tank and per production cycle. Daily documents cover raw milk acceptance records, standardized batching sheets, pasteurization or UHT heat treatment records, CIP cleaning records, filling operation records, reserved sample and microbial inspection reports. Fields include raw milk antibiotic and physicochemical indicators, heat treatment time and temperature, cleaning parameters, and filling batch information. Units include mg/kg, seconds, ℃, liters, and others.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Real-time high-frequency production data updates require dialogue contexts to quickly sync the latest batch ledgers. This prevents errors caused by calling expired data. Multi-source heterogeneous data sources — directly connected device data and manually entered system documents — require clear classification mapping in prompts. This stops field confusion that leads to summarization deviations. Scenarios with large single-batch data volumes, such as 15,000-row Excel filling records, can cause context overflow. Limit the range of documents loaded per dialogue turn. Errors in raw milk acceptance, heat treatment, and other links directly relate to recall risks. Prompts must mandate inclusion of data verification logic to ensure the accuracy of ledger summarization. The perishable nature of liquid raw materials requires report generation to complete quickly per batch. Dialogue responses must match production rhythm.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 characters | Adapts to the core field range of single-batch production ledgers, avoids messages context overflow |
| Number of Documents Loaded Per Turn | First 3 batch documents | Matches the processing capacity of a single dialogue turn, prevents data overload |
| Variable Binding Rule | Bind variables by production batch | Ensures ledger data called during dialogue corresponds to the specified milk tank or production batch |
| Dialogue Timeout Setting | 600 seconds | Adapts to the parsing and summarization time required for 15,000-row Excel data |
chatId Management | Create independent sessions by production batch | Distinguishes dialogue contexts for different milk tanks or batches, avoids data confusion |
Three Common Misconfigurations
- Front-end calls to the dialogue interface return CORS cross-domain errors. The deployment environment has not configured the allowed front-end request domain whitelist, so the browser blocks the cross-domain request.
- Dialogue output response takes too long, exceeding the waiting threshold of the production link. The number of documents loaded per turn is not limited, so too many batches of production ledger data are loaded. This creates an overly large
messagescontext and increases parsing time. - When loading a 100,000-character Chinese Word document or 15,000-row Excel data, the dialogue has no response or returns an empty result. No reasonable maximum context length is set, and large documents are not split into adaptive fragments. This causes
messagescontext overflow.
How to Verify Proper Configuration
- Initiate a test dialogue targeting a single-batch production ledger. Check if the
chatIdis bound to the specified milk tank or batch, and ensure the context does not mix data from other batches. - Upload a 15,000-row Excel filling record. Test the dialogue response time, and adjust the number of documents loaded per turn and the maximum context length to a range that matches the production rhythm.
- Enable dialogue logs. Check the token consumption statistics to confirm that the ledger data bound by
variablesis included. Verify that the configuration rules are correct. - Initiate multi-turn dialogue across batches. Check if the context automatically refreshes with new batch data, and ensure that the latest production ledger information is used.
The values provided are common starting points and should be measured against the reader's own samples.