What the data for this use case looks like
This data primarily comes from GitHub or Gitee Issues, code repository version logs, user-submitted environment information, and problem reproduction instructions. Daily maintenance documents include Issue templates, tag rules, historical response records, and problem reproduction instructions. Updates are triggered by new community-submitted Issues, code changes, and user feedback, with a typical update frequency of daily or weekly. Data fields include Issue titles, descriptions, tags, linked code snippets, user environment information, response status, and more.
What constraints do these characteristics impose on context and token management
Data for this use case is scattered, and token usage fluctuates widely. It includes code snippets and user environment information, making context token usage difficult to estimate. Data updates occur daily or weekly, so context caches must be refreshed regularly to match the latest tag rules and code changes. Community Issue volumes are high; recalling too much context will exceed token limits. Strict controls are required for the number of recalled context entries and the token quota per piece of data. Additionally, the length of code snippets linked to Issues varies significantly. Context token allocations must be adjusted based on the code volume of linked modules to avoid reducing the accuracy of problem classification and responsible module tagging.
How to Configure Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
max context token quota | 8000–12000 token | Matches average token usage of linked code and descriptions for single Issue in this use case, and aligns with model context limits for FastGPT v4.17.0 |
per-request recalled context count | top 3–5 | Controls total token usage and prevents redundant information from interfering with Issue classification and responsible module tagging |
context refresh interval | once daily | Matches the daily or weekly data update frequency, ensuring recalled tag rules and code changes are up to date |
max token per single context entry | 1500–2000 token | Adapts to length fluctuations of code snippets linked to Issues, preventing single entries from using excessive quota |
recalled content filtering rules | only code and historical replies linked to the current Issue | Narrows the context scope to improve the accuracy of problem classification and reproduction information completion |
Three Common Configuration Mistakes
- Symptom: After adjusting the number of context entries recalled, relevant historical replies for the target Issue are still not retrieved, and unrelated domain knowledge content appears. Cause: The context filtering rule for only linked current Issue content is not enabled, causing recall scope to cover the entire knowledge base and reducing the accuracy of Issue triage.
- Symptom: Task execution fails when submitting an Issue linked to long code, with a prompt indicating token limit exceeded. Cause: The max token quota per single context entry is not restricted, causing total usage to exceed the model's supported range.
- Symptom: Newly added tag rules are not recalled for Issue classification after configuring the refresh interval. Cause: The set refresh interval does not match the data update frequency, so latest tag and code change information is not synchronized in time.
How to Verify Successful Configuration
- Submit a test Issue, confirm recalled context only includes code snippets and historical replies linked to that Issue, to verify the filtering rule is active.
- After adjusting configuration items, observe token usage logs during task execution to confirm total usage stays within the model's supported range.
- Manually modify tag rules, wait for the configured refresh interval to elapse, then confirm recalled context includes the latest tag information.
- Submit multiple Issues of varying lengths, confirm the max token quota per single context entry adapts to code snippets of different lengths.
The values given 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.