What Data Looks Like for This Category
Data for this category comes from registered process documents, raw material batch ledgers, DCS control systems, weighing record systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Daily generated documents include batch production records, material balance reports, reaction and purification step records, cleaning validation records, impurity profile reports, and deviation and change documents. Data updates once per production batch and once per process step. Single documents contain fields such as process parameters, batch numbers, impurity content, solvent residue levels, and yield data. Field units include mole percent, milliequivalents, hours, kilograms, and other standard units.
Constraints on Multi-turn Dialogue and Prompt Engineering
This category has strong correlations between process and quality data. Multi-turn dialogue must associate batch data across multiple steps, requiring stable context management. Data updates frequently, and single-batch data volume is fixed. Prompts must precisely limit retrieval scope to avoid introducing irrelevant historical batch data. Impurity profile and deviation documents have strict compliance requirements. Each output of the multi-turn dialogue must match the field specifications of registered processes, with no tolerance for format deviations. Data formats vary across different sources. Prompts must unify data mapping rules to ensure accurate integration of cross-system data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId parameter configuration | Bind to the current production batch ID | Ensures context is isolated per batch, avoiding cross-batch confusion |
| Maximum number of retained context entries | First 10–15 entries | Single-batch data volume is fixed for this category; excessive context will introduce irrelevant historical batch information, reducing retrieval accuracy |
| Context clearing trigger rule | Triggered when a batch number change is detected | Each batch’s data is independent, preventing cross-batch context interference and meeting compliance requirements |
messages parameter configuration | Only retain conversation-related records for the current batch | Limits context scope to avoid irrelevant data interfering with output accuracy |
| Prompt variable binding | Bind batch numbers and impurity detection thresholds | Must precisely match single-batch process parameters and quality standards to ensure compliant dialogue outputs |
variables parameter configuration | Associate with real-time detection data from LIMS systems | Ensures dialogue outputs match laboratory test results in real time |
| Dialogue timeout threshold | 120 seconds | Process data requires retrieval across multiple systems; a timeout will cause task interruption and prevent report drafting |
Three Common Misconfigurations
- Phenomenon: After three or more consecutive questions in a multi-turn dialogue, the output process parameters do not match the current batch. Cause: No context clearing rule triggered by batch number changes is configured, so irrelevant context data from previous rounds is retained.
- Phenomenon: After a conversation link is shared, unauthorized visitors can view sensitive process and quality data. Cause: No dialogue authentication configuration is enabled, and a public link is used directly for sharing.
- Phenomenon: After consecutive calls to multi-turn dialogue, the system returns a timeout error. Cause: No reasonable dialogue timeout threshold is set, so cross-system data retrieval takes longer than the preset time.
How to Verify Correct Configuration
This verification process uses FastGPT v4.17.0.
- Manually switch the batch number, confirm that the context is automatically cleared and only retains conversation records for the current batch.
- Submit a query that includes a batch number and impurity content, confirm that the output report format matches the field specifications of registered processes.
- Simulate 10 consecutive conversations, confirm that the system completes responses within the 120-second threshold without timeout errors.
- Generate a conversation sharing link, use an unauthorized account to access it, confirm that the conversation content cannot be viewed.
The values provided here are common starting points, and should be evaluated against your own samples.