What the data for this category looks like
Typically, data for this category comes from the 12345 citizen hotline platform, contractor business systems, internal knowledge bases, follow-up review data, and local hotline management regulations. The update frequency is daily. Daily processed documents include request registration forms, dispatch records, handling responses, follow-up records, extension applications, return explanations, and knowledge base entries. Data uses ticket ID as the core associated field, covering core information such as public requests, responsible units, handling progress, and review results.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Daily updated multi-source data requires that dialogue context be limited to recent ticket workflows to avoid mixing data across tickets. Structured business documents require prompts to accurately extract fields such as request type and responsible unit, ensuring responses align with business rules. The need to integrate multi-source data requires multi-turn dialogue to gradually piece together information from different systems to avoid missing key details. Constraints on error consequences require the dialogue link to strictly verify information accuracy, preventing ticket overdue or return due to response deviations.
How to set up configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
messages | Retain 8000–12000 tokens of conversation history | Covers the full workflow dialogue content from ticket dispatch to response, avoiding truncation of key information |
variables | Bind ticket ID, request type, and responsible unit fields | Injects business-specific information into prompts to ensure responses match the corresponding ticket |
chatId | Bind unique ticket ID as the identifier | Distinguishes dialogue contexts for different tickets, avoiding mixing data across tickets |
| Single search limit | Top 3 recall results | Controls the number of reference contents, avoiding excessive redundant information interfering with response accuracy |
| Question optimization | Enabled | Converts colloquial public requests into standardized query terms, improving knowledge base recall precision |
| Timeout threshold | 600 seconds | Adapts to the time consumption requirements of multi-source data queries, avoiding interruptions to ticket processing due to request timeouts |
Three common misconfigurations
- Phenomenon: A connection error is reported when calling the model, and recovery fails after enabling the proxy. Cause: The proxy parameter is not bound in the dialogue configuration, or the timeout threshold is set below 600 seconds, causing the request to fail to complete normally.
- Phenomenon: After the knowledge base returns an image link, the large model response does not include displayable image content. Cause: The prompt does not explicitly require embedding image links into the response format, and the image field is not included in the
variablesparameter transfer. - Phenomenon: Keywords and variables such as departure date and city cannot be accurately extracted from dialogue content. Cause: The prompt does not specify the business fields to be extracted, and the structured extraction configuration for dialogue content is not enabled, resulting in missing variable recognition.
How to confirm the configuration is complete
- Initiate a test ticket dialogue, check if
chatIdmatches the test ticket ID, to verify that the context isolation configuration takes effect. - Input test content containing the request type, check if the returned
variablesparameter includes the corresponding business field, to verify that parameter injection is working properly. - Trigger a model call, check if a connection error is reported, to verify that the proxy and interface configuration is correct.
- Input test data containing image links, check if the response includes recognizable image display formats, to verify that the prompt configuration is correct.
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.