What this category's data looks like
This category's data primarily comes from source code repositories, dependency lists, static application security testing (SAST) tools, and public vulnerability databases. Daily produced documents include code audit reports, vulnerability proof documents, repair suggestion lists, and retest records. Data updates trigger when project versions iterate or individual audit tasks complete, with no fixed schedule. Document fields include vulnerability risk level, dangerous function location path, associated dependency package information, repair status tags, and retest results. No uniform mandatory units apply to each field; adjustments are made based on specific audit scenarios.
What constraints these characteristics impose on database and operations workflows
Multi-source data access requires database configurations to support multiple protocol adaptations to match data formats from different source code repositories and SAST tools. Unfixed update schedules create traffic fluctuations, so database connection pool parameters need sufficient elasticity to avoid connection exhaustion during peak periods. Dynamically changing document field structures require flexible field mapping rules in operations links. Hard-coded field verification logic should not be used. Writes of high-risk vulnerability associated data require strict verification to prevent erroneous data from entering audit summary reports and affecting subsequent decision-making.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://audit_user:secure_pass@db-host:27017/audit_data?retryWrites=true | Adapts to unstructured code audit report and vulnerability proof document storage, enables retry mechanism to ensure write stability |
PG_URL | postgresql://audit_admin:db_pass@pg-host:5432/audit_core?sslmode=verify-full | Stores structured repair suggestions, retest records and other relational data, enforces SSL verification to protect sensitive data security |
DB_MAX_LINK | 15–25 | Adapts to concurrent data read/write requirements for single-project audits, prevents connection pool resource exhaustion |
REDIS_URL | redis://:redis_pass@cache-host:6379/1 | Caches frequently queried vulnerability library matching results and temporary task status, reduces load on core databases |
Three Common Configuration Mistakes
- Symptom: Startup fails when passing environment variable placeholders while configuring
MONGODB_URI. Normal connection works when manually replacing the placeholder with the actual connection string. Cause: Environment variable loading order is not parsed correctly, so the placeholder is not replaced with the actual value. - Symptom: Database query tool calls in workflows return empty results, but executing the corresponding SQL statement manually retrieves data. Cause: The database connection configuration does not grant permissions to the workflow execution account, or the database name specified in the connection string does not match the actual database storing audit data.
- Symptom: Connection to the FastGPT database via a MongoDB connection tool fails, with an authentication error prompt. Cause: The
authSourceparameter is not correctly specified in the connection string, or the authentication username is not granted read/write permissions for the corresponding database.
How to Verify Configurations Are Correct
- In FastGPT v4.17.0, run the built-in database connectivity test tool to confirm that databases corresponding to
MONGODB_URIandPG_URLcan establish normal connections and perform read/write operations. - Import a small batch of code audit test data, check whether the data can be correctly written to the corresponding database tables or collections, and whether field mapping meets expectations.
- View system runtime logs to confirm there are no error messages about database connection failures, timeouts, or permission issues.
- Trigger a complete audit task workflow, check whether the final generated release notes and operational reports include correct audit data.
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.