What the Data for This Category Looks Like
The daily documentation for this category includes batching records, coating and calendering parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports, and similar materials. Data originates from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory test data. Data is synchronized in real time for each production batch and each individual cell. Each document includes fields such as material batch number, process parameter values, test results, and test time. Some parameters require associated units, for example, coating areal density uses g/㎡, and compacted thickness uses mm.
Constraints Imposed on Citation Sources and Traceability
Real-time, high-frequency update requirements mean the traceability system must quickly locate the latest batch documents to avoid citing expired data. Heterogeneous data from multiple devices must be uniformly linked to the corresponding cell batch to prevent data misalignment during traceability. Each single document contains multi-dimensional process parameters, which must be accurately matched to the relevant stage document for the identified issue. Highly sensitive electrochemical parameters require traceability links to directly point to the original storage location, preventing data tampering in intermediate steps. Exclusive data for each individual cell must support precise retrieval by batch and cell ID, ensuring traceability accuracy during cross-review.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Matches the average length of a single cell document, ensuring complete citation of key process parameters and test results |
responseData | Return only original document links and core fields | Avoids returning redundant content, meets traceability requirements, and adapts to quick reference scenarios in workshops |
detail | Enabled | Retains the hierarchical structure of original documents, making it easier to verify associations between multi-stage documents during cross-review |
| Single retrieval limit | Top 5 entries | Limits the number of retrieval results, focuses on the most relevant batch documents, and prevents information overload |
| Retrieval matching threshold | Calibrated via actual testing | Adapts to the professional terminology and parameter matching logic of cell manufacturing documents, avoiding missed detection of critical batch data |
This document is written based on the configuration logic of FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: The AI response does not display the original document link, only returning summary content. Cause: The
responseDataparameter is not configured correctly, and the original link return logic is not enabled. - Phenomenon: The number of retrieval results exceeds expectations, with a large number of irrelevant historical batch documents appearing. Cause: A reasonable single retrieval limit is not set, and the time range or batch range for retrieval is not restricted.
- Phenomenon: The cited document does not match the cell batch currently under review, with incorrect cross-batch associations. Cause: The cell ID or batch number is not bound in the retrieval parameters, resulting in an overly broad retrieval scope.
How to Verify Correct Configuration
- Submit a review request for a known cell batch, and check whether the returned results include the original document link corresponding to that batch.
- Review the number of retrieval results to confirm whether they meet the preset single retrieval limit requirement.
- Verify the length of the returned citation content to confirm whether it falls within the range set by
quoteMaxToken. - Test cross-batch retrieval results to confirm that only document data within the specified range is returned.
The values provided are common starting points and should be evaluated against relevant sample datasets.