What the data for this category looks like
Engineering consulting supervision data primarily comes from on-site supervision patrol records, supervision daily logs, sub-item acceptance reports, progress control ledgers, and rectification and closure documents. Data is updated weekly or at acceptance milestones over the course of a single project cycle. Documents are primarily structured tables, with attachments in PDF or scanned image format, including fields such as supervision unit, project number, acceptance date, rectification item list, rectification completion rate, etc. Units include standard engineering units such as calendar days, square meters, and ten thousand yuan.
What constraints these characteristics impose during deployment and upgrade
Engineering consulting supervision data includes structured tables and a large number of scanned attachments. During deployment, configure parameters adapted for large file storage to prevent failures when uploading or reading attachments. Data is updated weekly or at acceptance milestones alongside project nodes. During upgrades, maintain normal access to historical data from existing projects to avoid interrupting ongoing process guidance. Fields include standard engineering units. During deployment, validate field format and unit matching rules to prevent unit confusion during process guidance. Large data volumes arise from parallel multiple projects. During upgrades, reserve sufficient database storage space to prevent data write timeouts or connection limit exceedances.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 | Adapts to the storage needs of scanned attachments in supervision data, supports large file upload and reading |
DB_MAX_LINK | 20–30 | Matches the concurrent read/write requirements of multiple parallel projects, prevents connection limit exceedances |
REDIS_URL | redis://:your_password@127.0.0.1:6379/0 | Caches temporary session data for process guidance, reduces database read pressure |
FILE_TOKEN_KEY | Generate per-project exclusive keys | Secures access permissions for supervision project attachments, prevents unauthorized downloads |
PG_URL | postgresql://db_user:db_pass@db-host:5432/fastgpt | Stores structured supervision project field data, supports fast retrieval by project number |
Three common configuration mistakes
- Symptom: Workflow debugging returns a truncated "workflow error {"message":"Dangerous behavio” error. Cause: Failure to configure
FILE_TOKEN_KEYleads to failed attachment permission verification, orDB_MAX_LINKset too low causes database connection limit exceedances. - Symptom: All deployed models and application configurations are lost after Windows system restart. Cause: Configuration files were not mounted to the system's persistent storage directory, and configurations in the temporary directory are cleared by the system after restart.
- Symptom: The process guidance page fails to load structured data from historical supervision projects. Cause: Incorrect
PG_URLconfiguration leads to database connection failure or data retrieval exceptions.
How to verify successful configuration
- Upload a scanned attachment from a supervision project, verify that the file can be stored normally and downloaded via link, confirm that the
STORAGE_S3_ENDPOINTconfiguration is valid. - Start 3 or more parallel simulated data write tasks, check that the database connection count does not exceed the limit set by
DB_MAX_LINK, confirm that concurrent read/write operations work normally. - Restart the deployment server, verify that configuration files are not lost, and the model and application list can be loaded normally, confirm that persistent configuration is effective.
- Initiate a process guidance request, check that unit matching verification for structured fields works normally, confirm that the
PG_URLconfiguration is valid.
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 2026-09-28.