What the data for this category looks like
The business data for game planning and numerical design mainly comes from player behavior data, public competitive product materials, battle logs, and version feedback. Daily produced documents include gameplay design plans, numerical tables, version requirement lists, and balance test records. Data update frequency is weekly or per version cycle. Data fields usually include rule descriptions, numerical thresholds, version identifiers, feedback sources, etc. There are no unified fixed units; some numerical fields need to match the unit parameters within the game.
Constraints imposed on conversation logs and auditing by these characteristics
The data sources for this category are scattered and the update rhythm is fast. Conversation logs must cover the full link from debugging preview to official release, ensuring that every conversation about rule discussions and numerical adjustments can be traced. Daily document structures are complex, so chatId and responseChatItemId must be used to associate the corresponding planner’s conversation and specific replies, to avoid log confusion across different projects. Due to the high frequency of data updates, the auditing link must support archiving logs by version cycle while retaining the original format of fields, ensuring that the precision of numerical content is not tampered with. This page is written based on the configuration logic of FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind by project dimension, assign a unique chatId to each planner’s conversation | Matches the work pattern of game planners grouping by project, avoiding log confusion across projects |
responseChatItemId | Retain unique identifiers for each reply, do not automatically clean | Supports tracing the complete conversation link for a single numerical adjustment or rule discussion |
detail | Store full conversation content, including numerical table snippets and version requirement list references | Covers the core content of documents for this category, meeting the need for complete information during retrospectives |
| Log retention period | 7–30 days, calibrated based on actual testing | Matches the weekly or per-version update rhythm, balancing storage costs and auditing requirements |
| Conversation permission control | Divide visible scope by planner role | Ensures planners for different projects can only view conversation logs they initiated |
Three Common Misconfigurations
- Symptom: Historical conversation logs for a specified project are accidentally deleted, making version adjustment records impossible to retroactively review. Cause: No filtering rules configured based on
chatId, and full log cleanup operations are executed directly. - Symptom: Debug records for stream responses only show the final result, without displaying intermediate steps. Cause: Full storage configuration for the
detailparameter is not enabled, only concise reply summaries are retained. - Symptom: Cross-role access to conversation logs from other projects occurs, creating data leakage risks. Cause: Conversation visible scope is not divided by role, and permission verification based on
chatIdis not configured.
How to Confirm the Configuration Is Correct
- Initiate a debugging conversation for numerical adjustment, check whether a unique
chatIdis generated for the conversation, and confirm it is associated with the current project identifier. - Call the query interface associated with
responseChatItemId, verify that each reply’s identifier can be queried individually, with no duplicates or missing entries. - Attempt to log in with different role accounts, confirm that only conversation logs initiated by the user themselves can be viewed, and records from other projects cannot be accessed.
- Check the log storage period, confirm that conversations exceeding the set period have been automatically archived or cleaned up, and no additional storage resources are occupied.
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.