Data overview
The data for this category primarily comes from the 12345 citizen hotline platform, contractor business systems, follow-up and evaluation data, and local hotline management-related documents. Daily generated documents include request registration forms, dispatch records, handling responses, follow-up records, extension applications, return explanations, and knowledge base entries. Data is updated daily. Individual ticket data includes fields such as request ID, request type, responsible entity, handling stage, response content, and evaluation results. There are no unified fixed numerical units. Content is only associated with corresponding IDs and text based on business scenarios.
Constraints for database and operations workflows
Daily high-frequency ticket updates require the database to support stable incremental synchronization and batch writing. This avoids service interruptions caused by single-batch write timeouts. Data from multiple sources has varying formats. The operations link must configure unified data cleaning rules to adapt to field formats exported by different business systems. This ensures data can be correctly mapped to database table structures. Ledger aggregation and report generation require association of multiple document types. The database must support multi-table join queries, and sufficient index resources must be reserved to maintain query efficiency. Ticket data involves public requests, so data storage and transmission compliance must be ensured to prevent sensitive information leaks.
Configuration guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/dbname?authSource=admin | Adapts to unstructured storage requirements for multi-source ticket data, supports fast writing and querying |
PG_URL | postgresql://username:password@host:port/dbname | Used to store structured ledger fields and report metadata, ensuring efficient join queries |
DB_MAX_LINK | 20–50 | Matches concurrent write requirements for daily incremental synchronization, prevents service interruptions caused by connection pool overload |
REDIS_URL | redis://:password@host:port/0 | Used to cache frequently queried ticket statistics data, reducing direct database query pressure |
Common configuration errors
Three frequent misconfigurations are listed below:
- Symptom: A
MongoDB connection failedlog error appears during startup, and initialization cannot be completed. Cause: TheMONGODB_URIparameter is not configured correctly, or the database service has not opened the corresponding port, preventing FastGPT from establishing a database connection. - Symptom: The ledger aggregation task times out and returns an empty result. Cause: The
DB_MAX_LINKconfiguration value is too low, and concurrent write requests accumulate, causing the database connection pool to be exhausted and unable to respond to query requests. - Symptom: Fields are missing after multi-source ticket data synchronization. Cause: Data cleaning rules are not configured for the export formats of different business systems, causing some fields to fail to map correctly to the database table structures.
How to verify successful configuration
- Execute a database connection test command to confirm that the addresses, account names, and passwords configured for
MONGODB_URIandPG_URLcan successfully connect to the corresponding database services. - Submit a simulated ticket data write request to confirm that data can be properly stored in the corresponding database table without field loss or format errors.
- Check the Redis cache monitoring panel to confirm that frequently queried ticket statistics data can be cached normally and updated on time, with no cache expiration or write failures.
- Run a ledger aggregation task to confirm that multiple types of ticket documents can be associated to generate summary results that meet business requirements, with no timeouts or errors.
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.