What this category’s data consists of
The data for this category primarily comes from asset management systems, procurement systems, financial fixed asset modules, and terminal management platforms. Organizations generate four types of daily documents: asset ledgers, inventory sheets, loan and return forms, and scrap application forms. Core fields include asset number, name, specification model, user, location, procurement date, and asset status. Data update frequency is monthly, quarterly, or annually. A single update covers changes to ownership, location, or usage status of a single batch of assets.
Constraints for Database and Operations Workflows
Multi-source data access requires the database to support cross-system format adaptation and batch synchronization, to avoid field mapping conflicts. The low-frequency but critical update rhythm requires configuring a historical version retention mechanism to support fault retrospective and compliance audits. Unified storage of multiple document types requires establishing standardized field mapping rules to ensure data consistency. Temporarily generated data for account-physical verification and user confirmation requires configuring reasonable cache lifecycle and cleanup policies to avoid excessive storage resource usage. At the same time, changes to core fields such as asset ownership and location must trigger database index updates to ensure query efficiency and support fast retrieval needs during fault troubleshooting.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://asset_admin:secure_pass@db-host:27017/fastgpt_asset?authSource=admin | Supports flexible storage for multiple types of asset documents, with extensibility for unstructured fields |
PG_URL | postgresql://asset_log_admin:secure_pass@db-host:5432/fastgpt_asset_log?sslmode=require | Stores structured loan and return, scrap application data, and ensures transaction consistency |
DB_MAX_LINK | 20–30 connections | Matches concurrent peak load for monthly/quarterly batch updates, to avoid exhausting database connections |
REDIS_URL | redis://redis_admin:secure_pass@cache-host:6379/0 | Caches frequently queried asset location and user information to reduce database query pressure |
| Backup and recovery strategy | Combine node snapshots and incremental logs | Adapts to low-frequency updated asset data, enabling fast recovery during outages |
This configuration applies to FastGPT v4.17.0.
Three common misconfigurations
- Restoring project backups causes knowledge bases and agents to fail to load: Symptoms include empty knowledge base lists and no corresponding agent entries after logging into the backend. The cause is that the backup only exported the project configuration file and did not synchronize the associated database asset ledger and knowledge base metadata, resulting in broken association relationships.
- An
Invalid array lengtherror occurs when executing question-and-answer splitting: Symptoms include the error popping up on the interface after clicking the question-and-answer split button, with the error occurring consistently on each operation. The cause is that the field array in the asset ledger contains null values or abnormal formatting, which is passed directly to the splitting logic without validation. - Database connection timeout occurs: Symptoms include connection timeout errors when batch importing inventory sheets, interrupting the operation. The cause is that the
DB_MAX_LINKconfiguration is too low to support the concurrent connection requirements of batch imports.
How to confirm the configuration is properly set
- Perform cross-source data synchronization tests to verify that asset data from different systems can be correctly mapped to unified fields.
- Trigger an asset data backup and recovery process to verify that the restored data matches the backup node.
- Adjust the
DB_MAX_LINKconfiguration to simulate batch import scenarios and observe whether the number of connections meets business peak requirements. - Check the cache hit rate of the
REDIS_URLto verify that frequently queried asset location information can be read normally.
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.