What This Category’s Data Looks Like
This category covers manufacturers of liquid milk, yogurt, milk powder and dairy ingredients. These businesses store routine documents including raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling operation records, sample retention reports and microbial test reports. Data comes from raw milk testing systems, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection modules, CIP systems, and LIMS laboratory management systems. Updates occur in real time for each milk tank and each production batch. Each document includes structured data for the corresponding batch: physical and chemical indicators, operating parameters, and test results.
Constraints for Multi-turn Dialogue and Prompt Engineering
This category’s data updates in real time, draws from dispersed sources, and is strictly tied to production batches. Multi-turn dialogue must use chatId to bind the unique identifier of a single milk tank or single batch, and track exclusive context to prevent cross-batch data confusion. Daily documents have strong correlations. For example, heat treatment parameters and CIP cleaning records have linked logic. Multi-turn prompts must guide association and matching of structured fields for the corresponding batch, to avoid interference from redundant data. Additionally, data updates frequently during a single production batch. Dialogue context must be limited to valid data for the current batch, to prevent reference to outdated test or operation records.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding rule | Bind the current production batch’s tank number + production timestamp | Matches the batch-based production logic of dairy processing, prevents cross-batch context confusion |
messages context limit | Retain the first 15 messages of the current batch, total length no more than 7000 tokens | Controls computational load per dialogue round, adapts to real-time production response requirements |
variables preset parameters | Preset raw milk antibiotic threshold ≤0.01 IU/mL, pasteurization standard temperature 72–85℃, CIP cleaning valid duration 30–60 minutes | Quickly access category-specific process standard data, reduces repeated queries |
| Historical dialogue recall limit | Only recall production-related documents for the current batch | Filters redundant data from non-current batches, improves question and answer accuracy |
| Tool call trigger condition | Trigger corresponding data source query by current batch number | Accurately matches single-batch production data, avoids cross-batch calls |
Three Common Misconfigurations
- When configuring database tool calls, a 400 status code is returned with no response body. The cause is failing to pass the current production batch number as a query parameter to the tool, so the tool cannot locate the corresponding batch’s production data.
- Non-current batch historical document data is included in multi-turn dialogue, leading to deviations in process parameter judgments. The cause is failing to filter cross-batch redundant data via context restriction rules.
- Category-specific process standard variables are not preset. Repeated queries of fixed parameters during multi-turn dialogue lead to dialogue response timeouts.
How to Verify Proper Configuration
- Initiate a simulated dialogue for a single production batch, verify that the batch number bound to
chatIddisplays correctly in the dialogue context panel. - Trigger a database tool call, check that only documents for the current batch such as raw milk acceptance records and heat treatment records are returned.
- Adjust the dialogue context length, verify that the length limit set in the
messagesconfiguration takes effect, to avoid overflow errors. - Test multi-turn interaction, confirm that preset process standard parameters in
variablesare automatically called, without manual input.
The values provided here are common starting points. They should be evaluated against the user’s own samples.