What Data for This Category Looks Like
The primary entities in this category are liquid milk, yogurt, milk powder, and dairy ingredient processing enterprises. Daily documents include raw milk acceptance records, standardized ingredient lists, pasteurization/UHT sterilization records, CIP cleaning records, filling records, and retained sample and microbial test reports. Data sources cover raw milk testing, production MES systems, homogenization and heat treatment equipment, online temperature and flow monitoring systems, CIP systems, and LIMS laboratory management systems. Updates occur in real time for individual milk tanks and production batches. Available fields include antibiotic physiochemical indicators, heat treatment time-temperature parameters, aseptic filling and cleaning verification data. Units include degrees Celsius, seconds, colony counting units, and other relevant units.
Constraints Imposed by These Characteristics on Source Citation and Traceability
Real-time batch-based data updates require traceability systems to accurately bind unique identifiers for individual milk tanks and production batches, preventing cross-batch data confusion. Multiple heterogeneous data sources must be uniformly linked to batch IDs, ensuring cited raw milk testing, equipment operation, and laboratory test data belong to the same production chain. Perishable raw materials require no delays in traceability responses, as delays can introduce compliance risks. The fully continuous production chain cannot be isolated per batch like solid food baking, so every cited source data must cover the full process from milk receipt to filling, with no data gaps.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Matches the average character length of single-batch production records, to avoid truncating core traceability fields such as batch IDs and antibiotic test values |
responseData | Return only documents matching the current batch | Avoid cross-batch data confusion, aligns with the precise binding requirements of dairy processing traceability |
detail | Enable full field display | Fully present key audit basis such as heat treatment time, online temperature, and microbial test results |
| Single search limit | Top 5–7 results | Covers multiple types of documents associated with a single batch, including raw milk acceptance, CIP cleaning, and filling records |
| Similarity threshold | 0.72–0.80 | Filter historical data not from the current batch, accurately match batch keywords in queries |
| Maximum context | 1500–2000 token | Carry associated data for the full production chain of a single batch, ensuring complete production records are available for audit |
Three Common Errors
- Phenomenon: Returns documents from other batches when the query is unrelated to the current production batch. Cause: Does not use batch unique identifiers as a mandatory retrieval condition, relying only on semantic matching to retrieve cross-batch data.
- Phenomenon: Cited equipment operation data is not linked to the corresponding production batch, making it impossible to verify heat treatment parameter compliance. Cause: Does not configure retrieval rules to associate multi-source data by batch ID, matching only equipment parameters and breaking data links.
- Phenomenon: Chinese-language queries do not retrieve English industry standard documents from the knowledge base, preventing access to English reference sources. Cause: Does not configure rules to enable retrieval of English data sources, or does not accommodate mixed Chinese and English business requirements in retrieval logic.
How to Verify Correct Configuration
- Submit a simulated query for a single production batch, and confirm all returned documents are associated with the batch’s unique identifier, with no data from unrelated batches.
- Review retrieval result field displays, and confirm all required production parameters are fully presented with no truncated fields.
- Test Chinese and English queries to confirm they retrieve matching-language business documents, ensuring data source restriction rules are active.
- Check FastGPT v4.17.0 system logs, and confirm there are no context overflow or timeout errors, ensuring configured parameters align with current business data volume.
The values provided are common starting points and should be measured against the reader's own samples.