What the data for this category looks like
Ledger data for government and enterprise IT services comes from sources including communication network operation and maintenance ledgers, user service tickets, billing and settlement records, and device operation logs. Update cycles cover real-time alert tickets, daily billing reports, and weekly service statistics. Document structures are primarily structured tables, with fields such as service node number, alert level, processing duration, request type, billing amount, and service cycle. Units include hours, yuan, entries, and times, among others. Some ledgers include PDF scanned paper ticket files or structured export files. Fields must strictly align with internal IT specifications for government and enterprise organizations.
What constraints these characteristics impose during deployment and upgrade
Government and enterprise IT service ledger data is mostly structured and contains sensitive business information. Database configuration must support both non-relational storage for logs and tickets, and relational storage for billing and statistical fields. Frequent update requirements require configuring asynchronous task queues and scheduled scheduling components during deployment. This avoids service lag caused by synchronous processing. Field and unit diversity requires presetting data validation rules during deployment. This prevents non-compliant data from entering the system. During upgrades, ledger data consistency must be maintained. Back up structured tables and non-relational collections in advance. This avoids data format incompatibility during version iterations.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host address]:27017/fastgpt?authSource=admin | Adapts to unstructured log and ticket storage needs for government and enterprise ledgers |
PG_URL | postgresql://[username]:[password]@[host address]:5432/fastgpt | Stores structured ledger fields and statistical data |
REDIS_URL | redis://:[password]@[host address]:6379/0 | Caches frequently queried ledger statistics and session information |
DB_MAX_LINK | 20–30 connections | Matches concurrent access peaks for government and enterprise ledger data |
STORAGE_S3_ENDPOINT | Fill in according to the internal government and enterprise object storage service address | Stores ledger attachments and generated report files |
ROOT_KEY | Random 32-bit string generated per security specifications | Controls permission access verification for the platform backend |
Three common misconfigurations
- Symptom: Front-end page fails to load after deployment, returns 502 status code. Cause: The address configured in
FE_DOMAINis not bound to the server public IP or port, preventing external access to the service. - Symptom: No parsing option appears after uploading ledger PDFs, and text extraction cannot be triggered. Cause: The associated whitelist for
FILE_TOKEN_KEYis not configured correctly, preventing the pdf-marker service from communicating with the platform. - Symptom: Some nodes cannot synchronize ledger data after cluster deployment. Cause:
REDIS_URLis not configured for cluster mode, causing cache synchronization breaks and preventing data updates from propagating across nodes.
How to confirm successful configuration
- Access the address configured in
FE_DOMAIN, confirm the front-end management interface loads normally, with no resource loading errors. - Run
docker logs fastgpt-webto view the front-end container logs, confirm there are no timeout or authentication failure errors for database connections. - Upload a properly formatted test ledger PDF, confirm the parsing task triggers normally, and the generated text fragments can be viewed in the knowledge base.
- View the monitoring panel corresponding to
REDIS_URL, confirm the cache connection count stays in a stable range, with no frequent disconnections and reconnections.
The values provided are common starting points and should be validated against actual deployment scenarios.
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.