What the data for this category looks like
The core data for this category is generated around individual power battery packs, energy storage cabinets, or supporting BMS systems. Daily documents include grouping rules, BOM lists, welding and torque operation records, BMS flashing logs, insulation and voltage withstand reports, EOL test reports, and system configuration parameter tables. Data sources cover cell capacity grading equipment, production MES systems, welding and torque tools, BMS software version repositories, and EOL test benches. The system generates a dedicated record each time a pack or energy storage cabinet is produced, and updates corresponding configuration documents synchronously whenever a BMS software version changes.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Data for this category is tied to the characteristics of individual production units. This requires the knowledge base retrieval system to accurately filter the recall scope using metadata such as batch numbers and SN codes, to avoid interference from irrelevant results. Mixed storage of multiple document types requires retrieval strategies to adapt to content in different formats, including structured parameter tables, time-series operation records, and compliance reports. Frequently updated single-item data requires the retrieval link to support incremental synchronization and version verification, to prevent calling expired BMS configurations or test data. Additionally, the concentrated volume of associated data for a single device can lead to truncated responses from too many recall results, so the number of items recalled in a single round must be limited.
How to configure the settings
This configuration is based on FastGPT v4.17.0. Specific parameter settings are as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Adapts to the matching accuracy of structured parameters and time-series records for this category, filters low-correlation cross-batch data |
limit | Top 8–12 items | Controls the number of recall results per round, prevents response truncation caused by too many associated data items for a single device |
searchMode | embedding | Prioritizes recalling core business documents such as grouping rules and BMS configurations via semantic matching |
usingReRank | Enabled | Rearranges recall results by business relevance, prioritizes returning welding and test records directly related to defect analysis |
datasetSearchUsingExtensionQuery | Enabled | Automatically supplements industry terminology such as cell capacity grading parameters and torque thresholds, improves recall accuracy for professional content |
collectionIds | Configured with knowledge base collection IDs bound to production batches | Restricts the retrieval scope to only dedicated documents for the corresponding batch, avoids interference from irrelevant cross-batch data |
Three common misconfigurations
- Phenomenon: Single-round retrieval returns results exceeding the threshold, and the large model response is truncated mid-way with no complete defect analysis conclusion. Cause: The
limitparameter is not configured, or its value is set too high. Too many welding and test records associated with a single device cause context overflow. - Phenomenon: Retrieved content does not carry metadata such as batch numbers and SN codes, making it impossible to link to complete records for the corresponding production link. Cause: The retrieval scope is not limited via
collectionIds, or metadata filtering rules are not configured, leading to recall of irrelevant cross-batch documents. - Phenomenon: Uploaded structured Markdown documents such as grouping rules and BOM lists are directly split into segments, and the logical relationship between titles and subordinate content is lost. Cause: Markdown structure recognition chunking is not enabled, and splitting is done only by fixed length, destroying hierarchical relationships.
How to verify proper configuration
- Submit a quality problem query for a single batch, check that all recalled documents belong to the corresponding production batch, to verify that metadata filtering and
collectionIdsconfigurations are effective. - Adjust the
similarityparameter range, observe changes in the relevance of recall results, to confirm that matching accuracy meets business requirements. - Trigger a query involving multiple associated documents, check that the response is complete and not truncated, to verify the effects of the
limitandusingReRankconfigurations. - Upload a structured Markdown document, retrieve content under the corresponding title, confirm that hierarchical logic is correctly preserved, to verify the chunking rule configuration.
The values provided here are common starting points, and should be evaluated against the reader's own samples.