What This Category of Data Looks Like
The data for this category primarily comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint tickets. The update frequency is daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Most data fields include case numbers, responsible entities, violation types, on-site location information, rectification execution status, and similar items. Some fields are associated with geographic coordinates and timestamps, with no unified fixed units.
What Constraints These Characteristics Impose on the Deployment and Upgrade Phase
High-frequency case data updated daily requires configuring storage and database parameters adapted for batch import during deployment, to avoid exceeding load limits with single-batch loading. Multiple types of unstructured documents (on-site photos, various law enforcement documents) require configuration items adapted for hybrid storage. Storage paths and access permissions for unstructured data must be planned in advance. Precise fields such as geographic locations and responsible entities associated with cases require prioritizing specified indexes during retrieval. The scope of database indexes must be clearly defined during deployment. For intranet or offline deployment scenarios, a local mirror source must be configured in advance to prevent failures when pulling official images.
How to Set the Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://username:password@host:5432/fastgpt?sslmode=disable | Adapts to the structured storage requirements of urban management law enforcement case data, supports vector retrieval for processing text and location data associated with cases |
STORAGE_S3_ENDPOINT | Intranet object storage address:9000 | This category contains large volumes of unstructured data such as on-site photos and law enforcement documents. Intranet storage ensures data access efficiency and security |
REDIS_URL | redis://:password@host:6379/0 | Daily high-frequency updated case data requires caching retrieval results to reduce database load |
DB_MAX_LINK | 20–30 | This category has a high data update frequency. The number of concurrent connections must adapt to the concurrent requirements of batch import and retrieval, and is calibrated based on actual testing |
FILE_TOKEN_KEY | Custom 32-bit random string | Used to sign access permissions for unstructured files, ensuring access security for photos and documents related to law enforcement cases |
MONGODB_URI | mongodb://username:password@host:27017/fastgpt?authSource=admin | Stores unstructured semi-structured data such as inspection records and complaint tickets, adapting to flexible field expansion needs |
Three Common Misconfigurations
- Vector matching accuracy issues occur during deployment or retrieval. This is caused by not using the pgvector 0.7.4 version adapted for PostgreSQL 15, which leads to reduced vector retrieval effectiveness for case-associated data.
- Image pull failures occur when deploying with Docker Compose. This is caused by not configuring a local mirror source for intranet deployment, making it impossible to access external image repositories.
- API calls triggering workflows return empty values. This is caused by not adapting to the knowledge base nesting logic introduced in FastGPT v4.8.10 and later, leading to context transfer exceptions.
How to Confirm the Configuration Is Complete
- Execute a test script that batch imports 100 simulated case data entries, check database connection status and storage write logs, and confirm that the
DB_MAX_LINKconfiguration adapts to concurrent requirements. - Upload an on-site photo to the configured unstructured storage path, use
FILE_TOKEN_KEYto generate an access link, and verify that the file can be accessed normally. - Call the case retrieval API, pass a preset case number, check whether the returned results include core case fields, and confirm that the database index configuration is correct.
- View Redis cache logs, confirm that high-frequency retrieved case data has been cached, and verify that the
REDIS_URLconfiguration is effective.
The values provided are common starting points and should be evaluated against relevant sample data.
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.