What the data for this category looks like
Dairy processing inspection and process record data originates from raw milk testing, MES systems, homogenization and heat treatment equipment, online temperature and flow collection modules, CIP systems, and LIMS laboratory management systems. Data updates are triggered by milk tanks and production batches, with some online parameters synchronized in real time. Daily documents include raw milk acceptance records, standardized batching sheets, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, retained samples and microbial test reports. Fields cover physical and chemical indicators, equipment operating parameters, and test results. Units vary by indicator type: ℃ for temperature, seconds for time, and CFU/mL for colony count.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The high-frequency update of data requires dialogues to call the latest batch of business records in real time, to avoid using expired data. Structured multi-field documents require prompt engineering to clearly specify extraction rules, ensuring accurate positioning of core audit items such as raw milk antibiotic indicators and heat treatment time and temperature. The update logic based on milk tanks and batches requires sessions to bind unique identifiers via chatId to distinguish the context of different production batches and avoid cross-batch data confusion. The properties of perishable raw materials require timely audit responses, and multi-turn dialogue must support quick backtracking of historical interaction content to complete verification of consecutive process links.
Configuration Settings
This page’s configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique code of the current production batch | Distinguish the session context of different milk tanks or batches to avoid cross-batch data confusion |
messages | Archive historical interaction content per single batch | Retain the complete dialogue chain for single-batch audits, supporting multi-turn backtracking to verify each process |
variables | Bind the current batch's raw milk acceptance and heat treatment record IDs | Automatically pull business data for the corresponding batch, reducing manual input operations |
| Single recall result count | Top 3 | Dairy product audits require quick positioning of core indicators; too many results will interfere with judgment |
| Context window length | 8000–12000 token | Cover the key fields of the entire process record for a single batch, meeting the information needs of multi-turn audits |
| Timeout threshold | 60 seconds | Match the real-time response requirements of dairy production, avoiding delays that disrupt production schedules |
| File storage limit | 1000 MB | Adapt to the storage scale of single-batch production records |
Three Common Mistakes
- Symptom: The knowledge base appears empty during dialogue, but all content is visible on the knowledge base page. Cause: The current batch's business documents are not bound to the session's
variablesparameter, causing the system to fail to locate the corresponding batch's audit data. - Symptom: Xlsx-format production records cannot be recognized by AI dialogue. Cause: The prompt engineering does not specify the field extraction rules for raw milk acceptance and heat treatment links in Xlsx files, or the uploaded files are not split by batch.
- Symptom: The dialogue interface cannot pass image URLs, even if publicly accessible external addresses are used. Cause: Internal storage addresses supported by the platform are not used, and direct use of external URLs triggers access restrictions.
How to Confirm Configurations Are Set Correctly
- Initiate an inspection and audit dialogue for the current production batch, check whether the automatically retrieved raw milk acceptance and heat treatment record fields match the business data of the corresponding batch.
- Switch sessions for different batches, confirm that after the batch code bound by
chatIdis updated, the dialogue context and called business data are synchronized. - Upload Xlsx production records for a single batch, initiate a dialogue, and check whether core audit fields such as antibiotic indicators and heat treatment time are correctly extracted.
- Test passing image URLs through the dialogue interface, confirm that normal calls are possible after using storage addresses supported by the platform.
The values provided here are common starting points and should be measured against the reader's own samples.