What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The data structure of this category is complex and updated daily. Multi-turn dialogue must retain key information such as case numbers and correction status from historical conversations. Context length must be strictly controlled to avoid redundant information interfering with model judgment. Policy-based Q&A must strictly match statutory reporting rules. Prompts must explicitly limit use to only specified infectious disease-related documents, and associate case information from historical conversations to ensure verification accuracy and prevent underreporting or delayed reporting. Multi-turn dialogue must also distinguish reporting records of different cases to avoid mixing up information across cases.
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Only retain the messages from the first 5 turns of dialogue | Daily updated data has high context redundancy. Limiting turns ensures the model focuses on dialogue information for the current case |
variables | Bind statutory reporting rule variables | Policy Q&A must strictly match statutory requirements. variables must specify document types such as infectious disease reporting cards and epidemic situation analysis reports |
chatId | Bind the chatId of the current case | Ensures multi-turn dialogue associates context for the same case, avoiding mixing up records of different cases |
| Maximum context | 6000–8000 characters | This category requires retaining key case information from at least 3 turns of dialogue. Overly long context increases model inference load |
| Single search limit | Top 3 recall results | Data for this category requires precise matching of case information. Too many recall results will interfere with the model’s judgment of statutory rules |
| Query optimization | Calibrate based on actual testing | Optimize queries using fields such as case numbers and correction times from user questions to ensure accurate recall |
Three common mistakes
- Symptom: Custom model and workspace model produce inconsistent responses | Cause:
chatIdandvariablesparameters are not unified, leading to inconsistent context calling rules across different applications - Symptom: Model output includes policy interpretations outside the knowledge base or content from non-specified documents | Cause: The
variablesfield does not explicitly limit use to only infectious disease-related documents, and case information from historical conversations is not associated - Symptom: Voice files cannot be uploaded in the chat interface, or there is no response after uploading | Cause: Related modules for speech-to-text are not configured, or corresponding function switches are not enabled
How to confirm the configuration is complete
- Initiate a multi-turn dialogue involving case corrections. Check that the model’s reply only references specified infectious disease-related documents and associates case information from historical conversations
- View the
messagesparameter call log to confirm that only context from the first 5 turns of dialogue is retained - Test multi-turn dialogue for different cases. Check that the model does not mix up reporting information across cases
- Test voice file upload functionality. Confirm that uploads can be properly converted to text and trigger Q&A
The values provided are common starting points and should be measured 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.