What this category of data looks like
This category of data comes from design BOM, silicon steel sheet and insulating material inventory data, vacuum drying equipment logs, insulating oil test reports, and high-voltage test station records. Data is updated per single product or per batch of materials. Daily documents include winding and core assembly records, vacuum drying records, insulating oil performance reports, partial discharge and withstand voltage test reports, and product nameplate parameter tables. Data fields include rated voltage level, insulation moisture content, partial discharge amplitude, withstand voltage test value, and other items, and are associated with material batches and production process nodes.
What constraints do these characteristics impose on the "multi-turn dialogue and prompt engineering" link
The data feature of being updated per single product or per batch of materials requires that multi-turn dialogue binds chatId to associate with the exclusive ledger of the corresponding batch or single equipment, to avoid cross-batch data confusion. Structured test and process documents require prompts to clearly specify extracting core fields such as insulation moisture content, partial discharge amplitude, and withstand voltage value, and filter irrelevant redundant information. Multiple data sources require multi-turn dialogue to gradually supplement missing process records, while prompts need to add data verification logic to ensure that the summarized ledger parameters comply with production specifications, and avoid subsequent analysis deviations caused by missing fields.
How to set the configuration
This configuration is based on FastGPT v4.17.0, adapted to the production ledger scenario of this category:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique production batch number of a single product | This category of data is updated per single product or per batch, so it needs to be associated with an exclusive ledger to avoid cross-batch confusion |
messages | Limit the context to the latest 12 rounds of dialogue or 1500–2000 characters | The total length of test and process records for a single product falls within this range, and an overly long context will interfere with core parameter extraction |
variables | Preset two core variables: rated voltage level and test batch number | This category of data is associated with fixed production parameters, and variables can be used to quickly locate ledger documents for the corresponding batch |
| Maximum context length | 1500–2000 characters | Matches the conventional data volume of a single product ledger, avoiding context overflow |
| Single search limit | Top 6 matching documents | The number of core test reports for this category is limited, and too many matches will introduce irrelevant non-batch data |
Three common mistakes
- Phenomenon: Conversation history does not appear in the associated storage, and the
messagesfield is empty. Cause: ThechatIdis not bound to the corresponding production batch number, and the system does not trigger the history record storage logic for the exclusive ledger. - Phenomenon: Knowledge base search works normally, but the AI dialogue output does not reference the test report content of the current batch. Cause: The prompt does not clearly specify calling the batch-associated documents passed in by the current
variables, or the batch number parameter is not passed correctly. - Phenomenon: The output result of the program execution module cannot be connected to the AI dialogue module, and the dialogue context does not include the executed process logs. Cause: The output result of the program execution is not passed to the dialogue context through
variables, so the AI cannot obtain the latest process data.
How to confirm the configuration is correct
- Enter the batch number of a single product, check whether
chatIdis automatically bound to the corresponding ledger, and whether the dialogue context includes the test and process records of this batch. - Trigger multi-turn dialogue to supplement missing process data, verify that
messagesretains valid dialogue content as set, and does not introduce irrelevant information from other batches. - Call the prompt to generate a production ledger report, check whether the output includes the core parameters in the preset
variables, and does not omit required fields related to insulation and testing. - Run the program execution test case, verify whether the execution result can be passed to the AI dialogue module through
variables, and the context includes the latest process logs.
The values provided here are common starting points and should be measured against the reader's own samples.