What this category of data looks like
Data for this category comes from enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems, with an annual update frequency. Daily processed documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data fields include issue numbers, rectification responsible persons, completion deadlines, supporting material association items, and more. The size of individual documents varies widely; some audit working papers include multi-page structured credential scans, so support for large file reading and storage is required.
What constraints these characteristics impose on deployment and upgrade
Annual updated batch data requires deployment nodes to support large-file batch import and incremental synchronization, and stable database connection parameters must be configured. Multi-system data docking requirements require pre-configuring permissions and data formats for each business system. Upgrades must be compatible with field mapping rules of older versions of the system. Associated storage of rectification ledgers and closure certificates requires configuring a reliable object storage service to prevent sensitive audit data from being exfiltrated. Large-file processing for audit working papers requires deployment nodes to reserve sufficient memory and disk space. For offline upgrade scenarios, offline image packages must be prepared in advance to avoid missing dependencies during cross-version upgrades.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapt to concurrent connection requirements for multi-system data synchronization, avoid database connection exhaustion |
MONGODB_URI | Path pointing to an offline intranet-deployed database instance | Adapt to offline deployment scenarios for public sector and state-owned enterprise entities, ensure sensitive audit data does not leave the intranet |
REDIS_URL | redis://:custom_password@intranet_IP:6379/0 | Cache associated data of audit issues and rectification ledgers, improve query efficiency |
STORAGE_S3_ENDPOINT | Intranet object storage service address | Store large files such as rectification closure certificates and audit working papers, comply with data non-exfiltration requirements |
| Maximum Context Window | 12000 token | Adapt to context reading requirements for long-document audit working papers, cover complete business credential content |
Three common mistakes
- Phenomenon: After cross-major version upgrades (such as from v4.6.7 to v4.8.10), audit issue fields in the database are empty. Cause: Upgrade scripts are not executed in version progression, and database structure update steps for intermediate versions are skipped directly, resulting in new fields not being generated.
- Phenomenon: After offline deployment upgrade, rectification closure certificate files cannot be uploaded. Cause:
STORAGE_S3_ENDPOINTis configured with an invalid address, and it does not point to the intranet object storage service, resulting in connection failure. - Phenomenon: When processing long audit working papers, generated reports are truncated. Cause: The maximum context window set in the deployment script does not match the configuration value in the FastGPT backend interface. The backend configuration takes priority, and the parameters set during deployment are not overwritten.
How to confirm the configuration is correct
- Run the database connection test tool included with FastGPT, verify that the concurrent connection number configured for
DB_MAX_LINKcan be established normally. - Upload a rectification closure certificate scan, check whether it can be stored normally and associated with the corresponding rectification ledger entry.
- Import an audit working paper with more than 10,000 characters, verify that the generated report does not have content truncation.
- The above verification steps apply to FastGPT v4.17.0.
Note: The configuration values provided are common starting points. They should be evaluated against the reader's own specific 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.