What this category’s data looks like
Core data for this category originates from formulation and process libraries, raw material batch records, reactor DCS systems, weighing systems, LIMS systems, and warehouse management systems. Data is updated once per production batch or each key process step, and includes documents such as batch production instructions, feeding verification sheets, reaction process records, cleaning validation records, test reports, deviation and change orders. Document fields include feeding quantity, reaction temperature, batch identifier, cleaning validation results, raw material batch number, and other relevant fields, with units matching common industrial production units such as kilograms, degrees Celsius, hours, batch numbers, and similar units.
Constraints for Multi-turn Dialogue and Prompt Engineering
The multi-batch switching production mode requires that dialogue context be strictly bound to the current production batch to prevent mixing of process data from different batches and interference with inspection results. Frequent batch-level data updates require prompts to dynamically call real-time data for the current batch, ensuring Q&A is based on the latest process parameters. Fixed formats for multiple types of structured documents require prompts to explicitly specify extracted fields to avoid interference from invalid information. Security requirements mandate that dialogue context must not disclose process data from other batches, with session records for different batches strictly isolated.
Configuration Settings
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Conversation history retention limit | Previous 5 entries | Adapts to multi-batch switching scenarios, avoids mixing context from different production batches |
chatId binding rule | Associate with production batch number | Ensures equipment inspection dialogues for the same batch share consistent context, avoids cross-batch interference |
| Maximum number of documents called per single invocation | Previous 3 documents | Matches the number of core documents for a single batch, prevents context overload |
| Context truncation threshold | 800-1200 characters | Matches the average length of core documents for a single batch, prevents context overflow |
messages filtering rule | Filter historical messages by batch | Only retains dialogue records for the current batch, excludes unrelated data |
variables usage specification | Only inject real-time parameters for the current batch | Ensures Q&A is based on the latest reactor DCS and weighing system data |
Three Common Misconfigurations
- Symptom: Context isolation fails for multiple AI model calls within a workflow, with dialogue records from different batches mixed together. Cause:
chatIdis not bound to production batches, leading to sessions from different batches sharing context. - Symptom: Dialogue history exports include document content from unrelated batches. Cause: Prompts do not limit calls to only document data for the current production batch, resulting in non-target data being included in the context.
- Symptom: File upload parsing errors occur in the workflow, and local storage cleaning record paths cannot be read. Cause:
variablesare not correctly configured to bind access permissions for local documents, or target fields for document parsing are not specified.
How to Verify Correct Configuration
- Initiate two equipment inspection dialogues for the same batch, check that the context of both sessions is associated with parameters for the current batch, with no cross-batch data present.
- Export dialogue history, confirm that only relevant document fragments for the current production batch are included, with no content from other batches.
- Trigger file upload parsing, verify that documents such as cleaning records and feeding verification sheets can be properly parsed and injected into prompts.
- View
chatIdbinding logs, confirm that session IDs match production batch numbers.
The values provided are common starting points and should be evaluated against the reader's own samples.