What the data for this category looks like
Typical users for this category include open source maintainers, community operators, and core contributors. Commonly used documents include Issue templates, label rules, response records, and problem reproduction instructions. Data sources include GitHub or Gitee Issues, version logs, user environment information, and code repositories. Update frequency is daily or weekly. Data fields include Issue title, content, associated code paths, labels, priority, reproduction steps, and more. Each entry corresponds to a single user-submitted question or feedback. The overall structure follows the full lifecycle flow of issues. Unlike Pull Request reviews, this category prioritizes problem descriptions and priorities, focusing on initial issue submission and classification.
What constraints these characteristics impose on multi-turn dialogue and prompts
Dispersed data sources linked to multiple systems require multi-turn dialogue to bind independent context via chatId to avoid mixing interaction data from different Issues. With an update frequency of daily or weekly, prompts must regularly match the latest label rules and Issue templates. Fixed fields in daily documents require prompts to include built-in validation logic to guide users to supplement missing reproduction information. The most time-consuming steps of issue classification and reproduction completion require multi-turn dialogue to use step-by-step guidance, avoiding overloading users with too much information in a single interaction.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind GitHub/Gitee Issue number | Ensure each Issue corresponds to an independent conversation context, avoiding cross-Issue interaction data mixing |
messages | Retain the first 20 historical interaction records | Cover the multi-round communication rounds of daily Issues, while controlling system resource usage from context |
variables | Inject Issue template fields and repository label rules | Allow prompts to directly call category-specific business data to complete classification and template validation |
| Timeout duration | 600 seconds | Match the reasonable waiting response time for open source community users, avoiding short timeouts interrupting complex reproduction information completion processes |
| Multi-turn guidance count | No more than 5 times | Align with the operational habits of open source maintainers, avoiding excessive rounds increasing communication costs |
| Context filtering threshold | Calibrated via actual testing, retain the first 1000 characters related to the current Issue | Filter irrelevant historical messages, focusing on valid context for the current issue |
Three common configuration errors
- Empty conversation log fields for local deployments. This occurs because the local deployment log storage path configuration is not injected via
variables, preventing interaction records from being saved normally. - Workflow interruptions when reaching the AI conversation step. This occurs because no historical message retention limit is set for
messages, causing context volume to exceed system limits and trigger termination. - Interaction response timeouts. This occurs when the timeout duration is set below 300 seconds, failing to cover complex reproduction information completion processes in open source Issue scenarios.
How to confirm successful configuration
- Submit a test Issue, verify that the conversation interface automatically associates the Issue number as
chatId. - Inject preset repository label rule variables, verify that the prompt can automatically classify and label the test Issue.
- Initiate more than 3 rounds of interaction, verify that historical messages are displayed normally according to the configured retention count.
- Simulate a complex reproduction information completion request that requires long processing time, verify that no abnormal interruption is triggered when the response duration exceeds 600 seconds.
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.