What the data for this category looks like
Dairy processing data covers raw milk acceptance, full production processes, and sample retention and testing links. Data sources include milk source testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection modules, CIP cleaning systems, and LIMS laboratory management systems. Data is updated in real time for each milk tank and each batch. Daily documents include raw milk acceptance records, standardized batching sheets, pasteurization or UHT heat treatment records, CIP cleaning records, filling operation records, and sample retention and microbial testing reports. Fields include antibiotic residue values, heat treatment temperature and duration, CIP cleaning parameters, colony testing results, etc., with clear physical units.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering workflow
The real-time nature and strong batch association of dairy processing data require that multi-turn dialogue be bound to chatId to lock single-batch production data and avoid cross-batch confusion. Data is updated frequently and fields include units, so prompts must require the AI to strictly match document fields and units, and prohibit vague expressions. The perishable nature of liquid raw materials requires that dialogue responses not be too long, and the context length must be limited to avoid retrieval bias caused by redundant historical data. Multi-source data is scattered across different systems, so prompts must clearly specify that the retrieval scope is full-process documents for the corresponding batch, to prevent mixing of cross-link data.
How to set up the configuration
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current production batch | Ensure that multi-turn dialogue only retrieves raw milk, production, and testing data for this batch, avoiding cross-batch confusion |
messages | Retain the first 10 dialogue contexts | Dairy processing data is updated quickly; overly long contexts can introduce irrelevant batch information. 10 entries balances context completeness and retrieval accuracy |
| Single search limit | Top 8 matching results | The number of production documents per batch is limited; too many results will increase the AI's processing load |
| Query optimization | Calibrate based on actual testing | Need to adapt to professional terminology for different links such as raw milk, heat treatment, and CIP, to avoid retrieval bias |
| Maximum context | 1200–1500 characters | Liquid dairy production processes are short, and the total character count of documents per batch is controllable. This range covers core retrieval data while avoiding redundancy |
variables | Pass the production time range of the current batch | Assist knowledge base retrieval to accurately locate documents for the corresponding batch, improving recall accuracy |
Three common misconfigurations
- Phenomenon: When calling the dialogue interface, after retrieving a knowledge base image address, the system returns "No answer found". Cause: The prompt does not explicitly specify that only text documents should be retrieved, or non-text retrieval results are not filtered in the configuration.
- Phenomenon: Send two dialogue requests in quick succession, and the later request does not receive a timely response. Cause: No dialogue interruption mechanism is configured, and the session from the previous request is not released, causing new requests to queue up and wait.
- Phenomenon: An attempt to modify or delete a dialogue record returns "No permission to operate this dialogue record". Cause: The current session's owning user or batch identifier is not passed in
variables, causing permission verification to fail.
How to verify that the configuration is correct
- Initiate a multi-turn dialogue about antibiotic testing for a single batch of raw milk, and verify that the returned results only include raw milk acceptance and microbial testing document data for that batch.
- Send two query requests for different batches in quick succession, and verify that the later request receives a response quickly with no queuing.
- Attempt to operate a dialogue record that does not belong to the current session, and verify that a permission error is returned to confirm that the permission verification logic is working.
- Retrieve a professional question about "pasteurization temperature", and verify that the returned results match the heat treatment record parameters and units for the corresponding batch.
The values provided here are common starting points and should be measured against the reader's own samples.