What Data for This Category Looks Like
Data for this category comes from raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily documents include batching records, coating and rolling parameters, winding/stacking records, liquid injection records, formation and grading data, OCV and safety test reports. Data is updated in real time per batch and per individual cell. Fields include specific process parameters, test values, timestamps and batch numbers. Units cover standard electrochemical and industrial measurement units such as voltage, current, weight and dew point values.
Constraints on Source Citation and Traceability
The high-frequency real-time updates and per-batch/per-cell data granularity require the citation and traceability system to accurately match the associated batch and process node of individual data entries, to avoid cross-batch data confusion. The multi-dimensional process and test document structure require the system to recall corresponding data based on the specific process step referenced in a query, without broadly matching all content. The strict requirements of electrochemical systems for moisture and cleanliness mean traceability data must cover environmental and process details across the entire production flow. Missing any link will affect audit accuracy. Long-cycle formation and grading data also require the system to support cross-time-span associated queries, to ensure the complete chain of process records is traceable.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category’s documents contain long sections of process parameters and test data. This length can fully cover the core information of a single process record and avoid segmentation breaks |
| Overlap Length | 100–200 characters | Single-batch data has continuous associated parameters. The overlap length ensures contextual coherence of cross-chunk data and avoids breaks in the process chain |
quoteMaxToken | 1500–2000 token | This category requires recalling associated data from multiple process links. This range can accommodate complete source citation information and ensure the integrity of audit logic |
| Single Search Limit | Top 8 entries | This category has multiple data dimensions; excessive recall will introduce irrelevant data. The top 8 entries can cover associated records for core process links |
responseData | Enable full field return | Inspection and audit require verifying complete fields of original data; only extracting summary information cannot meet traceability accuracy requirements |
detail | Enable detailed citation mode | Audits for this category require clear citation of source equipment, test time, and batch number of data. Detailed mode can fully display cited metadata |
Three Common Configuration Errors
- Embedded pages do not display the citation content area. The cause is failing to enable the citation display switch for knowledge base question answering, or failing to correctly bind the rendering node for citation data in front-end code.
- After configuring dynamic knowledge base variables, the AI response does not associate with the corresponding documents. The cause is failing to correctly pass dynamic parameters to the context matching link of knowledge base search, resulting in a recall range that does not match the query.
- Long process documents are incorrectly segmented, leading to loss of key parameter blocks. The cause is using heading symbols with excessive depth as separators, without matching the process paragraph structure of documents in this category.
How to Verify Proper Configuration
- Upload a single complete batching record document, initiate a corresponding process audit query, and check whether the returned results include the citation metadata of this document.
- Adjust the chunk length and overlap length configurations, upload segmented test documents, and use FastGPT v4.17.0 to verify that the split chunks cover the complete process parameter content.
- Call the API interface and check whether the returned results include the cited detail information corresponding to the
responseDataanddetailfields. - Simulate a dynamic parameter call, pass parameters for a specified batch, and verify that the recall results only associate with document data for that batch.
The values provided are common starting points and should be measured against the reader's own samples.