What Data for This Category Looks Like
The business data for batchwise fine chemical synthesis comes from the formula and process library, raw material batch ledger, reactor DCS system, weighing system, LIMS laboratory management system, and warehouse management system. Data is updated once per production batch and each key process step. Daily produced documents include batch production instructions, feeding verification sheets, reaction process records, cleaning records, inspection reports, deviation and change orders. Core fields include batch number, feeding amount, reaction temperature, residence time, and cleaning verification results. The character length of a single core document ranges between 800 and 1200, with significant parameter differences across batches.
What Constraints These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
The batched, multi-system data characteristics of this category require that multi-turn dialogue be bound to a unique batch identifier to avoid cross-batch data confusion. The multi-step process flow determines that dialogue requires interactive sessions split by process stages, gradually obtaining parameters for feeding, reaction, cleaning and other stages. A one-time request for full data cannot adapt to the step-by-step verification requirements of multi-step processes. Multi-source system data needs to be dynamically passed into the prompt via the variables parameter to ensure that the called parameters match the current production batch in real time. At the same time, strict compliance and safety requirements require the prompt to automatically filter sensitive formula fields and only return publicly available process verification content.
How to Set Up the Configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current production batch number | Match independent processes and data for each batch to avoid cross-batch data confusion |
messages context round limit | 10 rounds | Adapt to multi-stage interaction for single-batch process steps, prevent context overload |
variables | Pass real-time fields such as feeding amount and reactor temperature | Connect to real-time data from systems such as LIMS and DCS to ensure accurate prompt invocation |
| Maximum context character count | 800–1200 characters | Match the core field length of single-batch process documents, avoid interference from redundant information |
| HTTP request timeout | 600 seconds | Adapt to the collection interval of reactor DCS data, avoid request interruption |
| Prompt triggering rule | Calibrated based on actual testing | Adapt to the step-by-step verification requirements of multi-stage batch synthesis, avoid false triggers |
Three Common Mistakes
- Phenomenon: After configuring an HTTP networking node in the workflow, the dialogue directly calls the large model without triggering the request. Cause: The output of the HTTP request node is not bound to the
variablesparameter, causing the prompt to fail to call process data obtained via networking. - Phenomenon: The front-end calling the dialogue interface returns a cross-origin error. Cause: The front-end domain name was not added to the cross-origin whitelist in the FastGPT service configuration, causing the browser to block the request.
- Phenomenon: No token consumption statistics data is available for dialogue records after private deployment. Cause: The session token tracking configuration item was not enabled, and consumption records associated with batch data were not linked.
How to Confirm the Configuration is Correct
- Initiate a feeding verification dialogue, check whether the output includes the feeding parameters of the current batch, and confirm that the
variablespassing configuration is effective. - After triggering the dialogue, view the workflow log to confirm that the HTTP request node was called normally, and confirm that the request link is correct.
- Check the cross-domain configuration of the FastGPT service, use the front-end domain name to initiate a test request, and confirm that no errors occur.
- View the session record to confirm that the token consumption field exists, and confirm that the statistics configuration is effective.
The values provided here are common starting points and should be measured against the reader's own samples.