What this category of data looks like
Data for lithium-ion cell manufacturing process and abnormality tracking comes from raw material batch ledgers, workshop MES systems, on-line slurry testing equipment, workshop dew point monitoring systems, formation and grading cabinet collection terminals, and laboratory testing equipment. Data updates follow this schedule: Batching, coating, winding, and liquid injection records are updated per production batch. Each individual cell generates exclusive formation and grading data. Dew point, slurry parameters and other metrics are collected in real time. Daily documents include batching records, coating and roller pressing parameters, winding alignment records, liquid injection volume ledgers, formation and grading curves, OCV and internal resistance test reports. Fields include professional parameters such as feeding weight, coating speed, roller pressing pressure, and standing duration.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link?
The high granularity and high-frequency update nature of the data requires dialogue to accurately associate documents tied to a specific production batch. This prevents mixing data across batches. There are many professional fields covering electrochemistry, mechanical processing and other specialized areas. Prompts must clearly specify the parameter types to be extracted. This stops the model from generating vague content. Data is scattered across multiple types of equipment and systems. Dialogue must support integrating associated information from different documents. For example, combine batching records and coating parameters to analyze slurry consistency issues. Lithium-ion cell manufacturing has strict requirements for ambient moisture and assembly accuracy. Abnormal report drafting must prioritize extracting key parameters such as dew point values and pole piece alignment deviations. Prompts must set parameter priorities.
How to configure settings
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind the production batch number field | Accurately locate knowledge base files corresponding to the target production batch, avoid mixing data across batches |
messages | Retain the last 12 turns of dialogue context | Adapt to the multi-turn traceability needs of process abnormality analysis, support tracing earlier process parameters from abnormal reports |
chatId | Bind the exclusive dialogue identifier | Isolate dialogue contexts across different production batches, prevent data cross-interference |
| Single search limit | Top 6 results | Cover core document types such as batching, coating, formation and grading, avoid redundant data interfering with analysis |
| Context window length | 5000–7000 token | Adapt to the parameter integration analysis needs of multiple professional documents, avoid truncating critical data |
| Maximum context refresh interval | 240 seconds | Match the real-time data update rhythm of the workshop, ensure dialogue calls the latest batch data |
Three common misconfigurations
- Phenomenon: When requesting to repeat the content of an uploaded XLSX process document, the system returns a prompt that the file cannot be read. Cause: The XLSX file was not converted to a retrievable text format via official tools, or the file was not correctly mounted to the target knowledge base.
- Phenomenon: After calling the interface to create a knowledge base, the dialogue interface cannot associate the knowledge base, and returns an invalid parameter status code. Cause: The configuration parameters for associating the knowledge base were not correctly passed in the dialogue request, or the parameter format did not meet system requirements.
- Phenomenon: The generated abnormal report omits key parameters such as dew point values and liquid injection volume. Cause: The prompt did not clearly specify the professional fields for lithium-ion cell manufacturing to be extracted, or the single search limit was set too low to cover the corresponding documents.
How to confirm configurations are correctly set
- Upload a single production batch XLSX process document, initiate a query asking to repeat the core parameters of the document, verify that the model correctly returns the field content within the document.
- Pass the
variablesparameter bound to the production batch number in the dialogue request, initiate a query asking for the coating and roller pressing parameters of the corresponding batch, verify that the model only returns content from the target batch's documents. - Initiate a multi-turn dialogue, continuously ask follow-up questions about process abnormality analysis for more than 3 rounds, verify that the context window retains the complete dialogue history.
- Check the maximum context refresh interval setting in the configuration interface, confirm that it matches the actual rhythm of workshop data updates.
The values provided here are common starting points. Adjust them based on your own specific samples and use cases.