What the data for this category looks like
Data sources for this category include raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Data update cycles cover production batches, individual cells, and real-time monitoring scenarios. Daily generated documents include batching ratio records, coating roller pressing parameter logs, winding/stacking process records, liquid injection operation ledgers, full formation and grading process data, and OCV and safety test reports. These documents contain fields such as slurry ratio parameters, coating areal density values, winding tension values, dew point monitoring values, internal resistance test results, and capacity test results.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
The real-time nature and batch granularity of this category’s data require multi-turn dialogue to bind the context of the corresponding production batch via chatId, to avoid mixing data from different batches. There are many specialized fields with close correlations, so prompts must precisely limit the range of callable ledger fields to prevent generating content outside the business scope. The high update frequency requires triggering real-time updates of variables during the dialogue process, to match device data synchronization nodes. Electrochemical systems have high requirements for parameter accuracy, so prompts must include parameter verification logic to avoid misleading caused by incorrect parameter combinations. The large data volume per single batch requires limiting the effective length of the dialogue context, to ensure accurate responses.
Configuration Settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
chatId | Bind the unique number of the production batch | Ensure multi-turn dialogue context only associates with ledger data of the current batch, avoiding cross-batch confusion |
messages | Retain the last 10 dialogue histories | Control the total context length, adapt to the scenario of large ledger data volume per batch, and prevent response timeouts |
variables | Synchronize the latest ledger data every 300 seconds | Match the conventional interval of device data uploads, balancing real-time performance and system resource usage |
| Maximum context length | First 8000 characters | Cover the key fields of the core ledger for a single batch, ensuring the large language model can access complete business information |
| Question optimization | Calibrate based on actual testing | Optimize question decomposition logic for professional terminology in lithium-ion cell manufacturing, improving response accuracy |
This document was written based on FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: No data source reference related fields are returned after calling the dialogue interface. Cause: The reference association switch for ledger data was not enabled in the configuration, causing the content generated by the large language model to not bind the corresponding data source identifier.
- Phenomenon: The decomposed question from the previous round cannot be retained as a keyword for subsequent replies in multi-turn dialogue. Cause: The dialogue context was not bound via
chatId, causing the historical records of each round of dialogue to not be properly stored and called. - Phenomenon: After configuring thinking process output, the thinking content is not displayed in the dialogue history panel. Cause: The thinking process synchronization switch for dialogue records was not enabled, or the log output path of the agent configuration does not match the recording path of FastGPT.
How to Confirm Configuration is Correct
- Initiate a dialogue for the corresponding production batch, check whether the unique identifier of the batch is bound in the historical records, and confirm that the
chatIdconfiguration takes effect. - Initiate a dialogue with multiple rounds of questions, verify whether subsequent replies are associated with the question decomposition content from the previous round, and confirm that the context storage configuration takes effect.
- Call the dialogue interface, check whether the returned result includes data source association fields, and confirm that the reference configuration takes effect.
- After enabling thinking process output, check whether the corresponding content is displayed in the dialogue history panel, and confirm that the synchronization switch configuration takes effect.
The values provided here are common starting points and should be measured against the reader's own samples.