What data for this category looks like
Process and standard data for beverage and bottled water factories includes recipe sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspections, and finished product inspection documents. Data sources cover water source and raw material testing, recipe systems, online sugar and acidity collection equipment, filling equipment, visual inspection systems, and LIMS systems. Updates follow production batches and shifts. Some online equipment data is collected continuously. Data fields include batch number, sugar content value, acidity value, filling liquid level, sealing parameters, label batch number, and other fields, with dedicated units for different testing and production links.
Constraints on citation sources and traceability
Since data is updated by production batch and shift, and core parameters must be bound to a unique batch identifier, the traceability link requires precise association of documents and testing data for the corresponding batch. Online equipment data collected continuously has significantly different formats from offline system documents. Citation rules for real-time data and static documents must be distinguished. Production errors can lead to out-of-range formula values, microbial contamination, or mislabeled product recalls. Therefore, traceability requires retaining complete field details and avoiding generalized citations. A production model that switches between multiple product categories requires clear differentiation of process data for different batches to prevent cross-batch confusion.
How to set up configurations
The following configuration items are based on the citation traceability logic of FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical paragraph length of a single process document, avoids splitting critical batch parameters |
quoteMaxToken | 1200–1500 tokens | Covers the content volume of complete inspection records for a single batch, ensures core data is not truncated |
responseData | Includes batch number, test values, document source fields | Binds unique identifiers for process data, supports precise traceability identification |
detail | Enable full citation | Retains all field details from original documents, meets compliance review requirements |
| Single Recall Count | Top 3–5 entries | Matches the number of associated documents for a single batch process issue, avoids information overload |
Three common errors
- Symptom: After setting
quoteMaxTokento 1500, some chunks in the knowledge base that exceed this length are still cited. Cause: This parameter only limits the total token count of returned results, not the length of individual chunks. Individual chunks that exceed the threshold will still be fully recalled. - Symptom: The knowledge base retrieves matching results, but the chat interface only returns a response with no citation content. Cause: The
responseDataconfiguration is not enabled, and necessary traceability identifier fields are not returned. The system cannot generate citation information. - Symptom: After passing knowledge base retrieval results to an HTTP handler in a workflow, AI conversations cannot associate corresponding citations. Cause: Traceability data is not passed in the format required by
responseData, so the AI cannot identify source information.
How to confirm configurations are set correctly
- Upload a complete process inspection document for a single batch, initiate a query about process issues for that batch, and confirm that returned citation chunks are not incorrectly split.
- Check the content length of retrieval results, confirm that no truncation occurs under the
quoteMaxTokenlimit. - Verify traceability fields in retrieval results, confirm that necessary identifiers such as batch number and document source are included.
- Trigger the complete workflow process, confirm that the traceability data received by the AI conversation conforms to the configured format.
The values provided are common starting points and should be measured against the reader's own samples.