What Data for This Category Looks Like
Data for this category originates from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Data is updated per production batch, for single cell testing, and in real time for operating conditions. Available documents include batching records, coating and rolling parameter ledgers, winding/laminating process records, liquid injection operation logs, full-cycle formation and grading data, and OCV and safety performance test reports. Data fields include batch numbers, measured process parameter values, equipment operating status codes, test result values, and other relevant items. Some parameters have clear physical units.
Constraints on Multi-turn Dialogue and Prompt Engineering Workflows
The real-time nature, inter-process correlation, and strong physical attributes of this category’s data create clear constraints for multi-turn dialogue and prompt configuration. First, single batch data volume is large and updates frequently. Multi-turn dialogue must limit the context window size to avoid response delays caused by loading redundant historical data. Second, data is layered by processes such as batching, coating, assembly, and formation. Prompts must guide dialogue in process order to ensure each round of queries focuses on parameters and records for the current process. Additionally, data includes clear physical units. Prompts must require output results to match the standard units of the corresponding parameters to avoid confusion between values and their meanings. Finally, data is tied to batch and cell numbers. The current conversation context must be bound via chatId to prevent batch mix-ups across rounds of dialogue.
How to Configure the Settings
These configurations follow the functional rules of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding rule | Bind by production batch, use independent chatId for each production batch | Lithium-ion cell data is associated by production batch, preventing cross-batch context confusion and ensuring each round of dialogue focuses on the current production link |
messages context length limit | Retain the most recent 15 conversation turns or within 7000 characters | Single batch process data has a large volume; overly long context will cause response delays. This range balances context completeness and computational efficiency |
variables binding fields | Bind batch number, cell model, current process name | Accurately match process documents, test records, and parameter ledgers for the corresponding process, improving retrieval accuracy |
| Maximum context window | 10000 token, calibrated based on actual measurements | Adapts to the scale of multi-process data for lithium-ion cells, avoiding loading redundant historical data |
| Question optimization switch | Enabled | Converts colloquial process queries from on-site operators into standardized retrieval keywords, adapting to the retrieval logic of structured process documents |
Three Common Mistakes
- Phenomenon: API responses differ from test conversation results within the platform, and some process parameters are not correctly recalled. Cause: The current conversation’s batch number is not bound via
variables, so the corresponding batch’s process documents are not loaded during API calls, and only general retrieval results are used. - Phenomenon: After embedding a floating dialog box on a web page, the conversation state cannot be retained across pages, and conversation history is lost after each refresh. Cause: Persistent storage of
chatIdis not properly configured, andchatIdis not bound to the user session. - Phenomenon: In multi-turn cross-process conversations, returned parameter units are inconsistent, with non-standard physical units appearing. Cause: The prompt does not clearly require matching the standard units of the corresponding process, and the unit rules of the current process are not passed via
variables.
How to Confirm the Configuration Is Correct
- Initiate a single-batch process query, verify that returned results only include process parameters and test records for the currently bound batch, with no data from unrelated batches.
- Initiate consecutive multi-turn cross-process queries, verify that the conversation context retains previous batch and process information, with no context loss or confusion.
- Submit a colloquial process question, verify that returned results are converted into standardized parameter expressions that match the corresponding process’s unit rules.
- Call the API interface to initiate a query, verify that returned results are consistent with test conversation results within the platform, with no significant differences.
The values provided here are common starting points and should be evaluated against the reader’s own samples.