What the data for this category looks like
This category’s data supports internal work order circulation and customer demand pool management during operations on duty. Data sources include work order systems, customer interview records, CRM systems, and product roadmaps. Daily maintained documents include demand pool lists, demand assessment forms, customer feedback records, and version planning descriptions. Fields cover demand source, priority, associated customers, expected scheduling, implementation status, and more. Data updates happen weekly or alongside product version iterations. Data formats vary across sources, so unified integration into the demand pool management process is required.
What constraints these characteristics impose on deployment and upgrades
This category’s data comes from scattered sources and has a high update frequency. During deployment, configure multiple system docking parameters to achieve unified data synchronization and avoid fragmented demand information. Since daily documents include multi-dimensional demand fields, define clear field mapping rules during deployment to ensure external system data can be correctly imported into the demand pool. During upgrades, maintain compatibility with older version demand assessment form formats to prevent historical data from becoming unreadable after iteration. Demand merging and scheduling communication rely on high-frequency data queries, so configure database connection count parameters properly to avoid performance bottlenecks after deployment.
How to set configurations
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 connections | Supports high-frequency demand pool queries and merging operations, avoiding database connection exhaustion |
MONGODB_URI | Fill in according to the actual deployed MongoDB instance address | Stores unstructured data such as demand pool lists and customer feedback records |
REDIS_URL | Local port 6379 address or dedicated cache instance address | Caches demand priority data from high-frequency queries to speed up response times during scheduling communication |
STORAGE_S3_ENDPOINT | Fill in according to the enterprise cloud storage service endpoint | Stores document files such as demand assessment forms and version planning descriptions |
FILE_TOKEN_KEY | 7–30 days | Configures the validity period of file upload links, adapting to the update cycle of demand documents |
FE_DOMAIN | Public network domain name of the deployed service | Configures the frontend access address to ensure normal page redirection for internal work order push and demand pool updates |
Three common errors
- Issue: After Docker deployment, the page on port 3000 fails to load, while port 3001 is accessible normally. Cause: The 3000 port was not correctly mapped to the host machine during deployment, or the frontend service
FE_DOMAINconfiguration does not point to the correct port address. - Issue: An uncaught exception occurs after deployment, and backend logs report a connection failure. Cause: In private deployment scenarios, most exceptions stem from incorrect configuration file parameters, such as
DB_MAX_LINKset beyond the database’s maximum connection limit. - Issue: After upgrading to a new version, knowledge base queries return results but cannot be synchronized to the demand pool. Cause: The association configuration between the demand pool and knowledge base was not updated during the upgrade, leading to abnormal data flow links.
How to confirm the configuration is complete
- Check that the formats of parameters such as
MONGODB_URIandREDIS_URLin the configuration file match the deployment environment. Test database connectivity via the command line. - Access the configured
FE_DOMAINaddress, verify that the demand pool list and work order push page load normally, and rule out frontend rendering exceptions. - Import a test customer feedback record, confirm that the data is correctly synchronized to the demand pool list, and verify that the data flow link works properly.
- Initiate high-frequency demand merging query operations, monitor database connection count usage, and adjust
DB_MAX_LINKto a range that meets business query requirements.
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.