What Data for This Category Looks Like
Data for this category is typically produced by product designers, interaction designers, and front-end product teams. Daily documents include interactive prototypes, page flowcharts, component descriptions, and review records. Data sources primarily consist of Figma or Axure files, team design systems, user feedback, and usability test records. Update frequency is weekly or per version iteration cycle. The data structure includes nested page hierarchies, component attributes, version numbers, and review statuses. Common fields include page paths, component IDs, review counts, and similar items. There are no uniform fixed numerical units. Some fields are identified using version numbers or counts.
What Constraints These Characteristics Impose on Database and Operations Workflows
The above data characteristics create multiple constraints for the database and operations link. First, data sources include local files and content exported from online tools. This requires database connections to support multi-source authentication and cross-environment access. Second, the weekly or version-level update rhythm requires configuring incremental synchronization mechanisms to avoid excessive system resource usage from full synchronization. Third, nested document structures require databases to support complex nested queries and storage, to adapt to the hierarchical relationships of prototypes and review records. Finally, data anomalies directly lead to deviations in development team understanding. This requires configuring regular backups and read-write separation strategies to ensure data consistency.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:SecurePass123@192.168.1.100:27017/fastgpt_design?authSource=admin | Unstructured data such as prototype files and page flowcharts is stored in MongoDB. This setting specifies the authentication username, password, port, and corresponding business database |
PG_URL | postgresql://fastgpt:SecurePass123@192.168.1.100:5432/fastgpt_report | Structured data such as review records and operational statistics is stored in PostgreSQL, to adapt to complex query requirements |
DB_MAX_LINK | 15–25 connections | Controls the database connection pool size, to avoid resource exhaustion from too many single-node connections, and adapt to weekly/version-level synchronization tasks |
REDIS_URL | redis://:fastgpt_redis@192.168.1.100:6379/1 | Caches prototype preview data and review status snapshots, to reduce database query frequency |
Three Common Misconfigurations
- Symptom: After deploying FastGPT locally, configuring database connections, and running the release note summary, no output is generated. Cause:
MONGODB_URIis not correctly configured in the system environment variables, or the database firewall restricts IP access permissions from the FastGPT deployment node. - Symptom: An error occurs when using variables to pass SQL statements for database operations, but manual input works normally. Cause: Variables are not properly escaped. Special characters such as single quotes and backslashes cause SQL syntax parsing failures.
- Symptom: In Docker-deployed FastGPT, workflows cannot call configured database connections. Cause: The container network mode is not set to bridge or host mode, so the FastGPT container cannot access the database service deployed on the host machine.
How to Verify Correct Configuration
- Access the system settings page of FastGPT v4.17.0, open the database configuration module, and check whether the formats of parameters such as
MONGODB_URIandPG_URLcomply with standard connection string specifications. - On the FastGPT tool test page, enter a query statement for the list of interactive prototypes, and check whether the corresponding number of page entries are returned after execution.
- Access the system log module, filter database-related logs, and confirm that there are no error messages such as connection timeouts, authentication failures, or syntax errors.
- Trigger a prototype data synchronization task. After the task completes, check whether the corresponding version of interactive prototype data is generated in the database.
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 2026-10-19.