What Data Looks Like for This Category
Primary data sources for this category include GitHub or Gitee Issues, code repository version logs, user-submitted environment information, and code snippets. Maintained documents include unified Issue templates, repository tag rules, historical response records, and problem reproduction guides. Data updates occur daily or weekly. Core fields include Issue numbers, problem descriptions, tags, reproduction steps, and associated code modules. The most time-consuming steps in the processing workflow are problem classification, completion of reproduction information, and marking of responsible code modules.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Since data comes from GitHub Issues with inconsistent formatting, multi-turn dialogue must first standardize incoming problem descriptions to ensure context alignment. Data updates happen daily or weekly. Prompts and configuration parameters must sync with updated tag rules and Issue templates on a regular basis. Daily documents include clear triage rules. Multi-turn dialogue prompts must incorporate these rules to maintain consistent triage. For the most time-consuming classification and completion steps, the system must automatically guide users to supply missing reproduction information. It must also automatically match the corresponding responsible code modules, to reduce repetitive manual work. Errors may lead to problem backlogs. Timeout and retry mechanisms must be configured to ensure smooth workflow.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Unique number associated with the GitHub Issue | Ensures each dialogue session is bound to the corresponding problem, avoiding context confusion |
messages | Retain the most recent 20 interaction records, with each entry under 1500 characters | Adapts to the reply length limits of GitHub Issues, controlling context load |
variables | Inject the current repository’s tag rules and Issue template content | Aligns with the category’s daily document requirements, unifying triage standards |
| Session timeout duration | 600 seconds | Matches the average time taken to process a single Issue in community workflows |
| Maximum context token count | 8000–12000 token | Calibrated via real-world testing, avoiding exceeding model processing limits |
Three Common Misconfigurations
- No subsequent output after the workflow reaches the AI dialogue stage, which occurs for specific problems. The cause is failure to inject Issue context into the dialogue configuration via
variables, leaving the AI without access to problem background. - If conversation records appear missing in a local deployment, compare conversation identifiers with stored data, query scope, access permissions, backups, and operational change records. This does not establish a default cleanup cycle. A record missing from the interface is not proof of deletion.
- No interactive feedback triggered on the dialogue interface. The cause is failure to configure custom interaction callbacks in FastGPT v4.17.0, requiring custom implementation of the corresponding logic.
How to Verify Correct Configuration
- Submit a test Issue, confirm the dialogue tool automatically associates the problem number matching
chatId - Input a test question into the configured dialogue tool, confirm the repository’s tag rules and Issue template content are called
- Trigger the configured workflow process, confirm no interruptions occur between problem optimization and the AI dialogue stage
- Check that dialogue logs are retained as configured, with no automatic cleanup occurring
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.