Quality Issue Traceability and Defect Analysis for Lithium-ion Cell Manufacturing: Knowledge Base Retrieval and Recall

What the Data for This Category Looks Like

This category’s data covers end-to-end records for electrode, assembly, formation, and quality departments of power, energy storage, and consumer battery enterprises. Daily documents include batching records, coating and calendering parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports. Data sources cover raw material batches, MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory data. Update frequency is per batch and per cell, with some parameters synchronized in real time. Electrochemical systems have extremely high requirements for moisture, cleanliness, and batch consistency; long-term operating curve data must be retained for the post-stage formation and grading links.

Constraints Imposed by These Characteristics on the "Knowledge Base Retrieval and Recall" Link

The data characteristics of this category impose multiple constraints on the retrieval and recall link: The attribute of real-time updates per batch and per cell requires the retrieval system to quickly match the latest batch process parameters and historical defect records. Multiple types of professional documents include high-precision fields such as slurry ratio, coating and calendering parameters, and dew point values, requiring the model to accurately identify professional terms and unit associations. The large volume of long-cycle formation and grading curve data requires limiting the content length and number of single recall results to avoid exceeding the context window. The strong correlation requirements of electrochemical systems for moisture, cleanliness, and batch consistency require retrieval to prioritize matching environmental and process data from the same batch.

Configuration Settings

Three Common Misconfigurations

• Using a universal authentication key when calling the knowledge base API causes the retrieval scope to exceed the allowed data for the current production batch, resulting in cross-batch irrelevant results. This occurs because the universal key is not bound to the specified collectionIds, and the retrieval dataset scope is not limited.
• Training errors occur when importing knowledge base data, with the system prompting format incompatibility. This is caused by failing to adjust the field structure according to the FastGPT v4.17.0 CSV template, or mixing non-standard document content not belonging to this category.
• The number of knowledge base retrieval return results is far lower than the set value, with only a small number of results returned. This is caused by incorrect configuration of the limit parameter, or failure to adjust the reference upper limit of the retrieval module during local deployment, leading to early truncation of retrieval results.

How to Confirm Proper Configuration

• Verify that the collectionIds parameter is bound to the dataset ID corresponding to the current production batch, ensuring the retrieval scope only covers process and testing data of the target batch.
• Confirm that the value range of the similarity parameter meets the matching precision requirements for professional parameters of this category. The relevance of recall results can be verified by retrieving process data from known defective batches.
• Test retrieval of long-cycle formation and grading curve data, confirming that the content length and number of single recall results do not exceed the context window limit to avoid content truncation.
• Check that usingReRank and datasetSearchUsingExtensionQuery are enabled. The coverage of full-link traceability requirements can be confirmed by retrieving associated raw material batches and formation data.

The values provided are common starting points, and should be validated against the reader’s own samples.