What Data Looks Like for This Use Case
Data sources for this use case include purchase orders, drawings and specifications, supplier quality certificates, inspection equipment logs, historical quality ledgers, and supplier portal data. Data updates are triggered per incoming material batch. Full supplier overall evaluations are completed monthly or quarterly, with special audits conducted irregularly. Daily associated documents include inspection specifications, sampling plans, incoming material inspection reports, supplier audit reports, exception contact sheets, 8D corrective action reports, and concession approval forms. Data fields include incoming material batch number, sampled quantity, inspection item name, and non-conformance type identifier. No additional custom units are used.
Constraints for Multi-turn Dialogue and Prompt Engineering
Multi-turn dialogue for incoming material inspection and supplier quality must adapt to high-frequency batch-level data updates. Dialogue must real-time associate the current incoming material batch’s corresponding inspection specifications and historical quality data. The irregular triggering of supplier audits requires that dialogue context can flexibly switch audit dimensions without being bound to fixed processes. The multi-type structure of daily associated documents requires prompt engineering to adapt to field extraction logic for different documents, such as extracting root cause items from 8D corrective action reports or confirming release conditions from concession approval forms. Additionally, the decentralized nature of data sources requires the dialogue module to support cross-data source calls, avoiding information discrepancies or duplicate data entry.
Configuration Settings
This configuration applies to FastGPT v4.17.0
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window upper limit | First 20 dialogue history entries | Incoming material inspection dialogues are mostly batch-related short processes; excessive history will interfere with current batch judgment |
| Session ID management | Automatically reset chatId after each batch dialogue is completed | Avoid cross-batch context interference, aligns with batch-level business logic for incoming material inspection |
| Dynamic variable binding | Configure variables to bind the current incoming material batch ID | Matches batch fields in business data sources, ensures dialogue associates with correct inspection specifications and historical data |
| Message array management | Only retain messages content for the current batch | Avoid mixing cross-batch dialogue content, ensures session independence |
| Prompt template switching | Automatically switch templates by inspection type | Adapts to document structures for different business scenarios such as incoming material inspection and supplier audit |
| Maximum single reply length | 800–1200 characters | Adapts to standard output lengths for documents such as inspection reports and corrective action descriptions |
Three Common Misconfigurations
- Incorrect process assignment: Some dialogues are assigned to the wrong business process operation. The cause is failure to bind batch information corresponding to
variablesin the dialogue, causing the problem classification module to fail to match the process branch of the corresponding inspection specification. - Dialogue function abnormality: The interface displays that the dialogue entry cannot load normally. The cause is failure to correctly configure the
chatIdgeneration rule, causing the session to fail to initialize properly. - Redundant historical records: After using API calls for dialogue, the returned results include an extra historical dialogue entry. The cause is failure to reset the
messagesarray after each API request, or repeated submission of redundant historical content.
How to Verify Proper Configuration
- Initiate a simulated incoming material inspection dialogue, enter a batch number, and verify that the dialogue automatically associates fields from the corresponding inspection specification.
- After completing a single batch dialogue, initiate a new batch dialogue, and confirm that the context does not carry the previous batch’s information.
- Use API calls for testing, and verify that the returned results contain no redundant historical dialogue entries.
- Trigger a process classification operation, and confirm that the input inspection type corresponds to the correct business branch.
The values provided are common starting points and should be measured against the reader's own samples.