What the Data for This Category Looks Like
Lithium-ion cell manufacturing production ledger data originates from raw material batch ledgers, MES systems, slurry and coating testing equipment, dew point monitoring terminals, formation and grading cabinets, and laboratory testing systems. Daily generated documents include batching records, coating and rolling parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports, and additional items. Fields cover batch numbers, process parameters, test values, test results, and more. Data updates occur in real time during single-batch cell production. Exclusive records are created after each individual cell completes its corresponding process. There is no fixed batch update cycle.
Constraints Imposed on Knowledge Base Retrieval and Recall
Data is generated in real time for each batch and individual cell. The retrieval workflow must support high-frequency incremental dataset updates. This prevents index lag that returns outdated production records. Multi-type process documents include both structured numerical parameters and unstructured test reports. Retrieval units must be split by process module to ensure accurate matching when cross-process ledgers are linked. Electrochemical systems have strict requirements for moisture and batch consistency. Retrieval must prioritize recalling records of raw materials and environmental parameters that match the current production batch. This avoids quality risks from parameter deviations. Multi-source data has varying field formats. Standardized processing must be applied before retrieval to ensure recalled fields can be used directly for ledger summary and report generation.
How to Configure the Settings
The following configurations are based on the knowledge base retrieval feature of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Lithium-ion cell production parameters have high precision requirements. Low-match irrelevant ledger records must be filtered to ensure recalled results meet parameter matching standards for the current process |
limit | Top 8–12 entries | The number of associated parameters for a single batch production ledger is limited. Too many recalled entries add context redundancy, while too few may miss key process records |
searchMode | embedding | Production ledgers contain large volumes of structured numerical parameters and process records. Vector retrieval accurately matches content linked to semantics and numerical values, outperforming keyword fuzzy matching |
usingReRank | Enabled | Semantic similarity across multi-source ledgers can be disrupted by field formats. The re-ranking step further filters records most relevant to the current production scenario |
datasetSearchUsingExtensionQuery | Enabled | Retrieval needs in production scenarios often imply implicit process association logic. Extended queries can supplement upstream and downstream associated parameters for the current process, improving the completeness of ledger summaries |
collectionIds | List of dataset IDs grouped by process | Splitting datasets by processes such as coating, winding, and formation accurately limits the retrieval scope and avoids interference from cross-process irrelevant records |
Three Common Misconfigurations
- Symptom: Retrieval requests take too long to respond, and the console returns a
504 Gateway Timeoutstatus code. Cause: Incremental synchronization was not configured for high-frequency real-time updated production ledgers. Full index reconstruction takes too long, causing retrieval timeouts. - Symptom:
chunk IDentries in the knowledge base cannot be selected or copied. Cause: Thechunk IDelements in the FastGPT interface do not have selectable text properties, and the interaction is not optimized for batch operations on ledger-style knowledge bases. - Symptom: Ledger report parameters generated by retrieval do not match actual production records. Cause:
usingReRankwas not configured, or thesimilaritythreshold was set too low. Low-match non-current batch ledger records were recalled.
How to Verify the Configuration Is Correct
- Check the dataset update log to confirm that real-time production ledgers from the last 24 hours have completed incremental synchronization with no failed records.
- Submit a retrieval request for the current production process, and verify that the number of recalled entries matches the configured
limitvalue. - Manually copy any
chunk IDto confirm that it can be properly selected and pasted into an external text editor. - Adjust the
similaritythreshold to 0.7, then confirm that low-match irrelevant records have been filtered after retrieval.
The values provided here are common starting points and should be measured against the reader's own samples.