What the data for this category looks like
Data for this category originates from ticketing systems, chat logs, product logs, and customer contract SLAs. Its update cadence is daily or real-time. Daily generated documents include customer tickets, issue triage tables, processing records, and service reports. Core fields include ticket unique identifiers, customer-associated information, original problem descriptions, triage tags, response time limit requirements, and processing progress. This data centers on technical feedback submitted by customers, covering all process nodes from problem submission to closure.
Constraints on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources and real-time updates require multi-turn dialogue to bind unique identifiers to associate with the corresponding ticket context, preventing information confusion across tickets. Daily documents follow a fixed field structure, so prompts must explicitly specify extraction of core fields such as ticket IDs and triage tags to reduce interference from invalid information. Judging problem reproduction and impact scope takes considerable time, so multi-turn dialogue must guide supplementary details across rounds, while limiting context length to ensure response efficiency.
Configuration Guidelines
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Pass the unique identifier of the current ticket | Ensure strong association between multi-turn dialogue context and the corresponding ticket, preventing cross-ticket information interference |
messages | Concatenate historical dialogue and ticket content by session rounds, retain the first 10 valid interactions | Match the update cadence of ticket data, control context length to guarantee response speed |
variables | Pass preset ticket triage rules and SLA time limit parameters | Adapt to the fixed document field structure of the category, allowing prompts to directly call preset rules |
| Maximum context length | 800–1200 characters | Balance context completeness and response latency, define a reasonable range based on actual testing |
| Language trigger rule | Adjust based on the language identifier of the ticket's customer | Adapt to different customers' communication habits, support multi-language scenarios |
Three Common Misconfiguration Issues
- Phenomenon: Historical ticket information cannot be reused when calling the session interface, and ticket content must be re-entered for each request. Cause: The
chatIdparameter is not passed correctly, or the passedchatIdis not bound to the ticket data of the current session. - Phenomenon: The prompt does not take effect when calling the
/api/v1/chat/completionsinterface, and the output result does not meet the ticket triage requirements. Cause: The preset triage rules are not correctly passed invariables, or historical interaction content is not concatenated inmessages. - Phenomenon: The conversation opening line in the same workflow cannot switch between Chinese and English, and the output format is chaotic. Cause: No trigger condition for language switching is configured in
variables, and the opening line is not adjusted based on the customer's language identifier of the ticket.
How to Verify Correct Configuration
- Initiate a test ticket request, verify that the returned session identifier matches the ticket identifier, confirming that context binding is correct.
- Pass the preset triage rules and ticket data, verify whether the output result matches the fields of the category's issue triage table, confirming that the prompt call is operating normally.
- Check the session response duration, adjust the context length to match business requirements, confirming that no timeout occurs.
- Test ticket requests in different languages, verify that the opening line can automatically switch according to preset rules, meeting configuration requirements.
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 2026-10-19.