What the data for this category looks like
This category of data primarily comes from GitHub or Gitee Issues, release notes, user environment information, and code repositories. Daily maintained documents include Issue templates, label rules, response records, and problem reproduction instructions. The data update frequency is daily or weekly. Core fields include Issue number, labels, priority, reproduction steps, associated code modules, user environment information, and more. Typical users are open source maintainers, community operators, and core contributors. Data revolves around the reporting, classification, and response workflow of online faults.
What constraints these characteristics impose on the deployment and upgrade link
Decentralized data sources include unstructured Issue descriptions and attachments. This requires the deployment process to support multi-data-source synchronization and unstructured data parsing. The daily/weekly incremental update rhythm requires elastic adjustment capabilities for scheduled synchronization tasks, to avoid excessive resource usage from full synchronization. Core fields include classification information such as responsible modules and priority. This requires support for hot updates of custom label rules and classification logic during deployment. Data workflows involve interactions between community users and maintainers. This requires the deployed permission verification to adapt to multi-role access scenarios, ensuring the security of sensitive configurations and user data.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:securepass@mongodb.internal:27017/fastgpt_issue?authSource=admin | Stores Issue data, label rules, and response records, ensuring persistent storage of classification and response data |
REDIS_URL | redis://:redispass@redis.internal:6379/0 | Caches frequently accessed Issue classification rules and user environment information, reducing database query pressure |
DB_MAX_LINK | 80–150 | Adapts to daily/weekly incremental Issue data synchronization, preventing synchronization failures caused by exhausted database connections |
STORAGE_S3_ENDPOINT | Determined based on actual testing | Stores attachment data such as screenshots and log files for problem reproduction, supporting image understanding and log parsing functions |
ROOT_KEY | Custom complex string of 32+ characters | Configures the platform administrator permission key, ensuring access security for core configurations such as label rules and synchronization tasks |
| General API key validity period | 7 days | Adapts to the response timeliness of open source community Issues, balancing interface call security and convenience |
Three common mistakes
- The symptom is no image understanding model option when creating a general knowledge base. The cause is that
STORAGE_S3_ENDPOINTand storage attachment read/write permissions are not correctly configured, causing the storage link relied on by image parsing to fail to access normally. - The symptom is failure to generate application-specific keys. The cause is that
ROOT_KEYis not set or the key format does not meet security requirements, causing the platform permission verification process to fail. - The symptom is GitHub Issue data synchronization timeout. The cause is that
DB_MAX_LINKis set too low, causing the database connection pool to be exhausted and unable to handle concurrent requests for incremental synchronization.
How to confirm the configuration is complete
- Run the database connection test command to verify that the database configured via
MONGODB_URIcan read and write Issue synchronization data normally. - Log in to the platform backend to check the cache status, confirming that the cache service configured via
REDIS_URLhas properly loaded classification rules and user environment information. - Submit a test Issue to verify that the system's automatic classification and label matching functions can trigger normally and return results.
- Check the platform running logs to confirm that the FastGPT v4.17.0 deployment version matches the currently running version, with no configuration errors.
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.