What Data in This Category Looks Like
The data for this category primarily comes from the regulatory rule repository, higher-level law database, departmental policy document repository, and administrative regulatory document filing system. It covers five core document types: regulatory document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Data updates do not follow a fixed schedule; new or adjusted content is added as review work progresses. Each individual document includes fields such as document name, document number, drafting unit, review node, opinion solicitation status, and filing status. The total data volume fluctuates based on review batches.
Constraints on Database and Operations Phases
The data characteristics of this category impose multiple constraints on database and operations phases. Dispersed multi-source data requires compatibility with multiple data source access methods to avoid limiting business expansion. Non-fixed update cycles require support for incremental synchronization mechanisms to reduce resource consumption from full data pulls. Diverse document types and significantly varying field structures require flexible storage solutions to adapt to changing document formats. Fluctuating data volumes based on review batches require reserved elastic expansion space, as well as configured regular backup strategies to ensure data can be recovered in abnormal scenarios.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:xxxx@192.168.1.100:27017/fastgpt_legality?authSource=admin | Most documents in this category are unstructured or semi-structured. MongoDB adapts to flexible schema requirements and supports multi-source document storage. |
PG_URL | postgresql://admin:xxxx@192.168.1.101:5432/fastgpt_ledger?sslmode=require | Used to store structured fields for review ledgers. PostgreSQL supports complex queries and transaction guarantees, meeting ledger aggregation needs. |
DB_MAX_LINK | 10–15 connections | Data updates for this category follow no fixed schedule. Avoid excessive connection counts that exhaust database resources; set based on actual testing. |
REDIS_URL | redis://:xxxx@192.168.1.102:6379/0 | Used to cache frequently accessed higher-level law comparison results, reducing database query pressure and improving review efficiency. |
| Database backup cycle | Daily 00:00 full backup + hourly incremental backup | Errors in this category’s data can lead to filing corrections. This ensures data can be quickly recovered and meets compliance requirements. |
Three Common Misconfigurations
- Symptom: A
Failed to connect to jyfkk:1433 - 38BBDDC3AF7F0000error is returned when connecting to a SQL Server database, while the MongoDB visualization tool can connect normally. Cause: Correct SQL Server data source adaptation parameters are not specified in the FastGPT configuration, or network policies restrict communication between the FastGPT container and the SQL Server instance. - Symptom: After a Docker deployment is restarted following a power outage, PostgreSQL and MongoDB services fail to start. Cause: Container volume persistent storage is not configured. Power outage causes data loss in the container or abnormal service status, and the automatic service recovery mechanism is not enabled.
- Symptom: Fields in the review ledger appear missing or formatted incorrectly. Cause: A flexible storage solution is not used to adapt to the changing document field structure of this category, or data format conversion exceptions are not handled during incremental synchronization.
How to Verify Successful Configuration
- Run a data source connectivity test to confirm that the database services corresponding to each configuration item can be accessed normally, and verify that the results meet the reading requirements for business data.
- Trigger an incremental synchronization task, check that the number of synchronized data entries matches the source system’s data batches, with no obvious loss or duplication.
- View the database backup logs to confirm that full and incremental backup tasks execute normally according to the set schedule, and that the backup file storage path is accessible.
- Simulate a power outage restart scenario, check the automatic recovery status of database services, and confirm that services can start normally and load data.
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.