What Data Belongs to This Category
Data for this category comes from work safety supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems, with a daily update frequency. Commonly processed documents include hazard inspection lists, hazard management ledgers, rectification notices, review records, and major hazard suspension and supervision orders. Fields include hazard identifiers, rectification responsible parties, rectification deadlines, review conclusions, and more. Multi-source data is incrementally imported daily, and document volume grows dynamically based on inspection frequency. Unified parsing and archiving of multi-format files must be supported.
What Constraints Do These Characteristics Impose on the Deployment and Upgrade Phase
Daily high-frequency multi-source data updates require sufficient database connection pool and cache resources to be configured during deployment, to avoid exhausting concurrent connections. The need to process multiple types of documents requires the upgrade phase to adapt parsing rules for different file formats. The consequences of errors in hazard inspection are directly linked to work safety responsibilities, so data persistence and disaster recovery mechanisms must be configured during deployment to prevent data loss from restarts or failures. Additionally, the rectification closed-loop process requires accurate mapping of field formats across systems to prevent information misalignment that affects review efficiency.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 connections | Adapt to concurrent synchronization needs of daily multi-source data, avoid exhausting database connection pools |
PG_URL | postgresql://fastgpt:securepass@localhost:5432/fastgpt?schema=public | Store hazard ledgers and case review data, configure local persistent storage volumes to prevent data loss from container restarts |
REDIS_URL | redis://:redispass@localhost:6379/0 | Cache real-time inspection tasks and session states, enable RDB persistence to prevent cached data from being cleared on restart |
STORAGE_S3_ENDPOINT | http://localhost:9000 | Store attached files such as rectification notices and review records, bind local storage volumes or object storage services |
FILE_TOKEN_KEY | Calibrated based on actual testing | Used to generate attachment access tokens, must match the key configuration of the storage service |
ROOT_KEY | Custom complex string of 32+ characters | Configure system administrator access key, store securely to prevent unauthorized access |
Three Common Configuration Mistakes
- Phenomenon: All hazard ledger data and system configuration information is lost after a Docker container restarts. Cause: No local persistent storage volume was configured for the PostgreSQL instance corresponding to
PG_URL, and data was only stored in the container's internal temporary directory. - Phenomenon: Some attached files fail to parse normally when batch importing law enforcement inspection records, returning a 400 status code. Cause: Access permissions for
STORAGE_S3_ENDPOINTwere not configured correctly, making it impossible to read stored attached files. - Phenomenon: Database connection timeout errors occur frequently during daily incremental data synchronization. Cause: The
DB_MAX_LINKsetting value is too low, unable to support concurrent connection requests from multi-source data.
How to Verify the Configuration Is Correct
- Run the
docker pscommand to confirm that PostgreSQL, Redis, and FastGPT core containers are all running normally. - Log in to the FastGPT backend management page, enter the data source configuration module, test the connection validity of
PG_URLandREDIS_URL, and confirm no errors are returned. - Upload a rectification notice document to verify that the file upload, parsing, and storage processes can complete normally.
- Manually trigger an incremental data synchronization task, check that the synchronization log has no errors related to connection pool exhaustion or permissions.
The values provided are common starting points and should be validated against local deployment samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.