What the data for this category looks like
The core data for this category comes from the "Double Random, One Public" regulatory platform, enterprise credit information public disclosure system, industry regulatory systems, and law enforcement officer directory. Daily documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result disclosures. Fields include the unified social credit code of inspected entities, law enforcement officer IDs, inspection item entries, rectification deadlines, and more. The update frequency is once per month.
What constraints do these characteristics impose during deployment and upgrade
This category's data characteristics impose three core constraints on deployment and upgrade. First, multi-source data access requirement: support connecting multiple heterogeneous data sources, and configure parameters compatible with various databases. Second, monthly update frequency: vector database synchronization tasks do not need high-frequency triggering; set a fixed cycle to perform incremental synchronization. Third, diverse document structures: including lists, records, disclosures and other types, support custom field mapping to adapt to field format differences across different documents and prevent data import errors.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://user:pass@host:5432/fastgpt?sslmode=disable | Compatible with PostgreSQL commonly used by regulatory departments for storing structured law enforcement data; 5432 is the industry-standard database port |
REDIS_URL | redis://:pass@host:6379/0 | Used to cache frequently accessed law enforcement officer directory and inspection item data; database 0 is the default configuration for basic cache databases |
DB_MAX_LINK | 20–30 connections | Adapts to concurrent requirements of monthly batch data synchronization; prevents excessive connection counts from occupying server resources |
STORAGE_S3_ENDPOINT | http://minio.example.com:9000 | Used to store attachments such as on-site inspection records and rectification notices; 9000 is the default port for MinIO, adapted for private deployment requirements |
ROOT_KEY | 32-bit random string generated through actual testing | Used for system administrator permission verification, ensuring background access security for private deployments |
Three common configuration mistakes
- Phenomenon: Some fields are missing when importing inspection target lists in bulk. Cause: Custom field mapping rules are not configured, causing the system to fail to recognize special field formats in law enforcement data.
- Phenomenon: After upgrading to FastGPT v4.17.0, the setting to hide thought processes remains ineffective, and tags still appear in reply content. Cause: The inference service was not restarted to apply the configuration, or the configuration was not correctly synchronized to all inference nodes.
- Phenomenon: The large model inference speed of private deployments is significantly lower than that of cloud platform versions. Cause: Cache and connection configurations for
DB_MAX_LINKandREDIS_URLwere not adjusted, causing frequent data queries to occupy excessive inference resources.
How to confirm successful configuration
- Log in to the system backend, run a database connection test, and confirm that the connection status for
PG_URLandREDIS_URLreturns successful. - Trigger a monthly data synchronization task, check the import logs for inspection target lists and inspection item lists, and confirm that there are no missing fields or failed import entries.
- Submit a policy basis query, confirm that no unconfigured thought process tags appear in the model's reply, and that response speed meets expectations.
- Upload an on-site inspection record attachment, confirm that the file can be stored normally and previewed in the system.
The configuration values listed are common starting points. They should be evaluated against the specific use cases and samples associated with the 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.