What the data for this category looks like
Business process data for state-owned industrial funds has three primary sources: internal approval ledgers, project declaration forms, and filing registration documents. Data is pulled from internal business systems of fund management entities, connected to the state-owned assets supervision platform via integration interfaces. Updates follow a set cadence: monthly updates for declaration progress data, and quarterly updates for filing information.
Document structure includes both structured form fields and unstructured approval minutes. Structured fields include project name, application quota, approval node, and others. The unit for application quota is ten thousand yuan. Approval node uses an enumerated string type. Declaration time follows standard date formatting. Unstructured documents are approval explanation materials, with a maximum limit of 5000 characters per single page.
What constraints these characteristics impose during deployment and upgrade
High-frequency read and write operations for structured form data impose requirements on the connection count and stability of relational databases. The large context requirements of unstructured approval minutes demand sufficient memory and CPU resources for deployment nodes.
Monthly and quarterly bulk data updates create temporary database write peaks. A data migration window must be reserved during upgrades to avoid business interruptions.
Data from state-owned industrial funds must comply with storage encryption requirements set by state-owned asset supervision regulations. Secure storage and access permissions must be configured during deployment. The encryption configuration for existing data must be retained during the upgrade process to avoid compliance risks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Supports stable database connection pools for the high volume of concurrent submission and query requests in state-owned industrial fund business processes |
MONGODB_URI | Internal MongoDB cluster address, with TLS encryption authentication parameters attached | Stores unstructured approval minutes and declaration attachments; internal deployment reduces data leakage risks |
PG_URL | Local PostgreSQL instance address, with connection timeout set to 60 seconds | Stores structured approval form data; the timeout setting prevents long connections from occupying resources |
REDIS_URL | Local Redis cluster address, with memory limit configured to 16 GB | Caches frequently queried approval node status and user sessions, ensuring response speed for business processing |
STORAGE_S3_ENDPOINT | Compliant internal object storage endpoint | Stores attached declaration material files, aligning with state-owned asset data storage specifications |
Three common mistakes
Reference only; verify against the target FastGPT release and deployment documentation.
- After deployment, logging into the system prompts an incorrect username or password error. The cause is failure to correctly configure the initial secret key for
ROOT_KEY, or failure to import preset administrator account parameters in the initialization script. - After saving model configuration and initiating a chat, the system switches to another large language model. The cause is failure to rebind the secret key parameters for the model service after upgrading, leading to failure to persist configuration data to the database.
How to confirm configurations are correct
- Run the
docker-compose pscommand to confirm that all FastGPT v4.17.0 related containers are in a running state. - Access the domain configured in
FE_DOMAIN, and use the administrator account to attempt logging into the system. Verify that the login process has no error messages. - Submit a test industrial fund project declaration data, and check whether the structured form record is successfully written to the corresponding database.
- Configure and save a specified large language model, initiate a test conversation, and confirm that the returned response matches the configured model service.
The values provided are common starting points, and should be evaluated against the deployment environment’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.