What This Category of Data Looks Like
Data for this category comes from four channels: content review backend, user reports, regulations and policies, and sample libraries. Four types of documents are maintained daily: review rules, disposal guidelines, appeal records, and quality inspection reports. Data updates occur via daily synchronization or are triggered by policy changes. Documents include fields such as effective time, rule version, appeal status, and quality inspection score. Some unstructured samples require additional audit labels. No mandatory unified format applies to fields.
Constraints Imposed by These Characteristics on Database and Operations
Multiple data sources require the database to support multi-table join queries, joining operation logs from the review backend, raw user report data, and updated entries from regulations and policies. High-frequency, unformatted documents need flexible field expansion to adapt to audit sample storage for different scenarios. Daily or policy-triggered update frequencies require configuring incremental synchronization mechanisms to avoid excessive system resource usage from full updates. Features with severe failure consequences need regular backup and disaster recovery configurations to prevent data loss from leading to spread of non-compliant content or regulatory rectification. Additionally, time-consuming rule explanation and appeal review links need optimized database query performance to reduce response latency.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://fastgpt:pass@localhost:5432/fastgpt?sslmode=disable | Adapts to PostgreSQL databases deployed via Docker, disables SSL for network environments without GPU virtual machines, explicitly specifies database username and password |
MONGODB_URI | mongodb://fastgpt:pass@localhost:27017/fastgpt?authSource=admin | Stores unstructured audit sample libraries and disposal guideline documents, supports flexible expansion of audit label fields |
DB_MAX_LINK | 20–30 | Matches resource limits for 8c16G host machines, prevents connection pool exhaustion from causing index creation failures or query timeouts |
REDIS_URL | redis://:pass@localhost:6379/0 | Caches frequently accessed audit rule versions and real-time appeal status, reduces repeated query pressure on the database |
Three Common Configuration Errors
- Phenomenon: When deploying PostgreSQL via Docker, knowledge base index creation fails, and the interface displays a "database connection failed" error. Cause: Incorrect configuration of
PG_URLconnection parameters, unopened container-mapped port 5432, or unescaped special characters in the password. - Phenomenon: After enabling problem optimization and result rearrangement, audit result return delay exceeds 30 seconds, and logs show query timeouts. Cause:
DB_MAX_LINKis set too small, exhausting the database connection pool and failing to support concurrent rule queries and sample retrieval. - Phenomenon: Audit rule updates do not take effect, and historical audit tasks do not match new rules. Cause: Incremental sync is not configured according to the daily update frequency, and full updates overwrite the latest rule version data.
How to Confirm Proper Configuration
- Run the database connection test tool built into FastGPT v4.17.0 to verify the connectivity of
PG_URLandMONGODB_URI, confirm there are no permission or address errors. - Check the database connection pool monitoring panel to confirm that the current active connection count does not exceed the
DB_MAX_LINKsetting, with no connection exhaustion. - Submit a test audit rule, wait 10 minutes, then retrieve the rule to confirm it can be normally matched and return matching results.
- Enable problem optimization and result rearrangement functions, initiate batch audit queries, confirm that return delay meets business expectations.
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.