What data for this category looks like
Lithium-ion cell manufacturing equipment inspection and maintenance data comes from the electrode, assembly, formation, and quality departments of power, energy storage, and consumer battery companies. 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. Fields cover slurry solid content, calendering gap, winding tension, liquid injection volume, formation and grading curves, internal resistance, and more. Units include percentage, millimeter, newton, milliliter. Data sources include raw material batch ledgers, MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory test data. Update frequency is real-time updates per batch and per cell.
What constraints these characteristics impose on knowledge base retrieval and recall
This category of data has high real-time requirements. Detection data for each batch and each cell is updated in real time. The retrieval system must support binding to high-frequency refresh datasets. Documents contain a large number of technical terms and numerical parameters. This places high demands on retrieval matching precision. Low-relevance non-lithium-ion cell manufacturing data must be excluded from results. Additionally, data covers multi-source equipment and test records, with a large content volume. The number of single-recall results must be controlled to fit the large model context window. Some inspection scenarios rely on multimodal content such as equipment operation images and defect photos. The system must support mixed text and image retrieval and recall.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Professional parameter matching precision requirements for lithium-ion cell manufacturing are high. This setting prevents low-relevance inspection records from appearing in results |
limit | Top 6–10 results | Single-batch detection data volume is large. Too many results will exceed the large model context window. Too few results will miss critical maintenance parameters |
searchMode | mixedRecall | Documents contain technical terms and numerical parameters. Hybrid mode balances semantic matching and exact keyword matching |
usingReRank | Enabled | Multi-source data is mixed. The re-ranking step filters low-relevance content and improves the ranking priority of key inspection and maintenance content |
datasetSearchUsingExtensionQuery | Enabled | Equipment inspection questions often contain implicit search terms. For example, inputting "calendering abnormality" automatically supplements "areal density deviation" to improve recall coverage |
collectionIds | Bind dedicated datasets by department | Inspection scopes are clear for different departments. Binding corresponding datasets avoids interference from cross-department irrelevant data |
Three common mistakes
- Symptom: Unable to select a specified number of retrieval results to send to the large model. Only the default single result or fixed number of results can be used. Cause: The
limitparameter is not configured correctly, or the multi-result context splicing function is not enabled. - Symptom: Uploaded coating defect images and equipment operation images are not recalled. Retrieval results only include text records. Cause: The multimodal vectorization switch is not enabled, or image datasets are not included in the retrieval scope.
- Symptom: When a user asks "abnormal calendering areal density", recall results are mixed with calendering parameters from other electronic assembly categories. Cause: Dedicated lithium-ion cell manufacturing datasets are not bound via
collectionIds, leading to interference from cross-category data.
How to confirm the configuration is correct
- Navigate to the knowledge base management page of FastGPT v4.17.0. Check if the datasets bound via
collectionIdsinclude dedicated documents for batching, coating and calendering, formation and grading, and other relevant areas. - Submit a test query: "inspection standards for insufficient liquid injection volume". Check if the
similarityscore of returned results falls within the set 0.75–0.85 range. - After enabling
usingReRank, submit a query containing an equipment model. Confirm that returned results are sorted to prioritize inspection and maintenance-related content. - Upload a defect image from coating and calendering. Initiate a mixed text-image query. Confirm that corresponding test records and maintenance plans are recalled.
The values provided are common starting points. They should be evaluated against the reader’s own samples.