What data for this use case looks like
Response template recommendation data is sourced from archived historical government hotline tickets, officially published unified response template documents, and post-completion audit records for completed tickets. The dataset synchronizes once per week. Emergency templates update immediately alongside policy adjustments. Each data entry includes ticket classification codes, original user questions, standard response texts, associated business scenarios, and audit status fields. Each entry maps to one specific user consultation scenario.
Constraints for multi-turn dialogue and prompt engineering
Since data comes from historical tickets and official templates, multi-turn dialogue must retain the ticket classification and user follow-up context for the current session. This prevents recommending mismatched responses that deviate from the business context. Weekly synchronized bulk data and immediately updated emergency templates require prompt configurations to support dynamic pulling of the latest template library. This ensures sessions always call the most recent version of response content. The presence of ticket classification code fields requires that pre-prompt text for multi-turn dialogue first completes classification matching for user questions. It then calls the template dataset under the corresponding classification for recommendations. This improves response accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 1200–1800 characters | Matches the average length of a single hotline conversation, avoids truncating critical business information |
| Question Optimization Toggle | Enabled | Optimizes colloquial hotline questions into standardized query statements for classification matching |
| Single-Round Template Recall Limit | Top 3 entries | Controls the number of templates returned per round of dialogue, prevents information overload that disrupts agent judgment |
| Context Refresh Interval | 600 seconds | Aligns with the weekly template update rhythm, regularly refreshes the available template dataset within the session |
| Classification Matching Threshold | Calibrated via real-world testing | Adapts to ticket classification systems across different industries, ensures classification matching accuracy meets business requirements |
Three Common Configuration Mistakes
- Issue: After upgrading to FastGPT v4.17.0, an error
fail to create post presigned urlappears when adding attachments during a conversation. Cause: The signature validity period parameter for S3 storage is not configured correctly. This prevents valid signatures from being generated for attachment upload requests within the session. - Issue: AI-generated responses do not return in the specified JSON format. Cause: The prompt text does not explicitly specify output constraints for JSON format, and no format validation rules are configured in
variables. - Issue: Like or dislike actions during conversations do not trigger subsequent optimization adjustments for templates. Cause: Session feedback synchronization configuration is not enabled. This prevents user feedback from being synchronized to the training dataset for template recommendations.
How to Verify Proper Configuration
- Submit a simulated hotline user question, check if the returned response templates match the ticket classification for the current session.
- View the context refresh interval parameter on the configuration page, confirm it aligns with the template library update rhythm.
- Trigger an attachment upload operation, check that upload links are generated normally without errors.
- View session history records associated with
messagesandchatId, confirm that multi-turn dialogue context is retained correctly.
The values listed above are common starting points. Adjustments should be validated against specific samples and use cases for the deployment environment.
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.