What data for this category looks like
Data for this category primarily originates from source code repositories, project dependency lists, Static Application Security Testing (SAST) tools, and public vulnerability databases. Update cadence aligns with project version iterations or the completion of individual audit tasks. Daily deliverables include code audit reports, vulnerability proof materials, remediation recommendation documents, and retest records. Common data fields include vulnerability ID, risk level, code path, impact scope, and remediation plan, among others. There are no uniform mandatory units; adjustments are made based on project audit requirements.
What constraints these characteristics impose on database and operations workflows
Incremental updates to source code repositories and dependency lists require databases to support frequent batch writes and version rollback queries. Operations teams must reserve sufficient disk space to handle audit data storage across multiple projects. Audit document structures vary significantly between different projects, so databases must support flexible field expansion to avoid hardcoded table structures limiting data entry. Real-time synchronization requirements for vulnerability databases mandate that operations configurations include regular data pull and validation mechanisms to prevent delayed vulnerability information from impacting audit results. Workflows combining manual review and automated scanning require databases to associate code paths, remediation records, and retest statuses to ensure consistency across data pipelines.
How to set up configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://username:password@host:5432/fastgpt_audit?sslmode=disable | Compatible with docker-deployed PostgreSQL database connections, matches FastGPT’s database driver format |
MONGODB_URI | mongodb://username:password@host:27017/?authSource=admin&replicaSet=rs0 | Supports multi-node MongoDB deployments, adapts to unstructured storage needs for audit data |
DB_MAX_LINK | 20–30 | Matches memory usage of an 8-core 16GB virtual machine, prevents service interruptions caused by exhausted database connections |
REDIS_URL | redis://:password@host:6379/0 | Used to cache intermediate states and temporary data for audit tasks, reduces database query load |
| Database field expansion | Reserve 10–15 custom fields based on project requirements | Adapts to flexible structures of different project audit documents, avoids hardcoding limitations on data entry |
Three common misconfigurations
- Symptom: Returns
Invalid array lengtherror when executing knowledge base question splitting operations, occurs consistently. Cause: TheDB_MAX_LINKdatabase connection pool configuration value is too low, causing the array parameter length to exceed limits during batch writes. - Symptom: When deploying PostgreSQL via docker, knowledge base indexes cannot be created, even though file sizes are small and host resources are sufficient. Cause: The
PG_URLconnection parameters are not configured correctly, missing required database permissions or connection timeout settings. - Symptom: After updating database or service passwords, online services fail to start normally or synchronize configurations. Cause: Services were not restarted to load updated database connection configurations, leaving old cached passwords intact.
How to confirm configurations are properly set up
- Run a basic database connection test to verify that the
PG_URLorMONGODB_URIconfigurations can establish a normal connection, with no permission or address errors. - Submit an audit task for a small test file to confirm that data can be written to the database normally, with no field incompatibility errors.
- Check database connection count monitoring to confirm that current connection counts do not exceed the
DB_MAX_LINKthreshold, and adjust the threshold as needed. - Check cache service status to confirm that the
REDIS_URLconfiguration can properly store and retrieve temporary audit task data.
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.