What the data for this category looks like
Business data for pharmaceutical distribution comes from internal inventory and sales management systems, GSP compliance files, employee office approval systems, and attendance ledgers. Update cadence follows three patterns: inventory and sales data is synced daily, compliance files are archived by business batch, and office documents are uploaded to the knowledge base as needed. Most individual documents are structured tables and process approval slips. Fields include generic drug name, batch number, inventory quantity, and approval order number. Units include box, case, batch, and calendar day.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
This category has dense structured fields and highly varied update cadences. This creates three constraints for multi-turn dialogue and prompt configuration. First, critical business fields such as drug codes and batch numbers must be passed accurately in messages. This prevents query errors caused by confused context during multi-turn dialogue. Second, inventory and sales data is updated daily. Prompts must bind real-time data retrieval rules. This ensures each dialogue returns the latest inventory information. Third, office approval documents have uniform formats. Prompts must preset rules for extracting approval order numbers. This reduces the frequency of multi-turn follow-up questions.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
messages | Retain the last 10 dialogue entries | Multi-turn dialogue for pharmaceutical distribution focuses on drug inventory and approval processes. 10 context entries cover complete business workflows while controlling context length |
variables | Bind drug codes and batch numbers as fixed variables | Business fields in this category have high standardization. Preconfiguring variables simplifies prompt writing and improves multi-turn dialogue consistency |
| Multi-turn dialogue timeout | 600 seconds | Internal office workflows often involve cross-department collaboration. 600 seconds covers the full dialogue cycle for consultation and approval responses |
| Prompt trigger rules | Trigger based on business keywords such as drug and approval | This accurately matches core business scenarios for pharmaceutical distribution and avoids invalid calls |
| Context-sensitive data filtering | Filter GSP data based on employee account permissions | Pharmaceutical distribution compliance data involves sensitive enterprise information. Permission isolation must be implemented during multi-turn dialogue |
Three common mistakes
- Phenomenon: After deleting a single dialogue record, the system synchronously deletes associated operation logs. Cause: No independent storage logic for dialogue records and logs is configured. The system defaults to binding both to the same data entry.
- Phenomenon: Prompts cannot correctly reference drug batch information from multi-turn dialogue. Cause: Structured business fields are not included in
messages. Only natural language dialogue content is retained. This prevents the model from accurately extracting key parameters. - Phenomenon: Performing a bulk delete operation on dialogue records triggers a system error. Cause:
chatIdis not specified as the deletion condition. This leads to accidental deletion of dialogue data from other accounts.
How to confirm the configuration is complete
- Initiate a multi-turn consultation dialogue that includes a drug code. Check if the returned results match the preset business logic. Confirm that
variablesandmessagesconfigurations are active. - Perform a single dialogue record deletion operation. Check if system logs are retained. Confirm that storage rule configurations are correct.
- Enter preset business keywords. Check if prompts trigger the corresponding call logic. Confirm that trigger rule configurations are accurate.
- Switch between different employee accounts to initiate dialogue. Check if only business data within the corresponding permission range is visible. Confirm that sensitive data filtering configurations are active.
The values provided are common starting points. They should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.