What data for this category looks like
Data sources for this category include safety production supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems. The update frequency is daily. Commonly processed documents include hazard inspection lists, hazard management ledgers, rectification notices, review records, and major hazard rectification oversight orders. Data fields cover hazard level, rectification deadline, responsible entity, review result, and other items. Rectification deadlines are measured in days, the number of hazard items is measured in units of items, and single document size ranges from several pages to dozens of pages.
Constraints imposed on deployment and upgrade workflows
Reference only; verify against the target FastGPT release and deployment documentation.
Recommended configuration settings
| Configuration Item | Recommended Value | Basis |
|---|---|---|
DB_MAX_LINK | 20–30 concurrent connections | Adapts to multi-data source connection requirements for daily synchronization, prevents synchronization interruptions caused by exhausted connections |
MONGODB_URI | 192.168.1.100:27017/hazard inspection and governance database | Stores multiple types of structured documents including hazard lists and management ledgers, ensures stable data read and write operations |
REDIS_URL | redis://192.168.1.100:6379/0 | Caches synchronization logs and user sessions, speeds up retrieval responses related to hazard inspections |
STORAGE_S3_ENDPOINT | 192.168.1.100:9000 | Stores large PDF files including rectification notices and review records, adapts to local storage requirements |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Ensures access security of the file upload interface, prevents unauthorized file writes |
Three common misconfigurations
- Phenomenon: Image pull failure occurs during Docker local deployment, and the interface displays "network timeout". Cause: No internal network image source adapted to safety production scenarios is configured, or container network access permissions are not enabled, resulting in failure to pull required images.
- Phenomenon: An "offset out of range" error occurs when uploading a large PDF file to the knowledge base, with upload progress reaching 90%. Cause: The file chunk threshold is not adjusted. The default chunk size for long PDF documents related to hazard inspections is insufficient to support complete uploads, leading to upload interruption.
- Phenomenon: Local Ollama service access fails after private deployment, and model calls cannot be completed. Cause: The internal network access whitelist for
REDIS_URLis not configured, causing the Ollama service and FastGPT container to fail to communicate across networks.
How to verify successful configuration
- Perform a local image pull test to confirm that images can be pulled normally, and network policies align with local deployment requirements.
- Upload a single PDF hazard rectification notice with more than 10 pages, confirm that upload progress has no abnormal interruptions.
- After configuring the local Ollama service address, initiate a test question and answer targeting hazard inspection terminology, confirm that the service can respond normally.
- View database connection logs to confirm that the concurrent connection count of
DB_MAX_LINKcan cover daily synchronization peak loads.
The values provided are common starting points and should be evaluated against the specific samples of each deployment.
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.