What the data for this category looks like
This category of data originates from the unified budget management system, departmental staffing system, prior-year final account data, project repository, and the Ministry of Finance’s annual budget preparation notice. Data updates follow an annual cycle. Corresponding daily documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approval documents. Document fields include staffing headcount, project funding amount, performance target score, and others. Common units are ten thousand yuan, persons, and score units. The structure is split into two types based on budget hierarchy: headquarters-level and subordinate unit summaries.
What constraints these characteristics impose during deployment and upgrade
The annual update rhythm requires configuring scheduled data synchronization tasks during deployment, to avoid using expired historical data. Multi-source data access requires adapting to the interface formats of different systems. Deployment must reserve expansion space for data conversion modules. The hierarchical document structure requires matching permission scopes corresponding to the budget hierarchy during vector retrieval. Upgrade must synchronously adjust permission verification logic. Document field units are tied to business specifications. Deployment must fix field mapping rules to avoid unit confusion in subsequent use, which could affect the accuracy of document drafting.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
REDIS_URL | redis://<Alibaba Cloud Redis mirror address>:6379 | Adapts to pull requirements in domestic network environments, resolves issues with failed default image pulls, default port is 6379 |
MONGODB_URI | mongodb://<username>:<password>@<database address>:27017/fastgpt?authSource=admin | Stores annual budget documents and retrieval vectors, adapts to storage and retrieval requirements for hierarchical data |
PG_URL | postgresql://<username>:<password>@<database address>:5432/fastgpt | Stores budget document approval workflows, permission configurations, and historical operation records |
DB_MAX_LINK | 20–30 connections | Adapts to batch reading requirements for annual budget data, avoids database blocking caused by excessive connection counts |
STORAGE_S3_ENDPOINT | Fill in according to local storage or cloud service provider object storage address | Stores budget-related document attachments, complies with data storage compliance requirements |
Three Common Mistakes
- Executing
docker-compose up -dresults in a Redis pull failure error, with a prompt of connection timeout or image not found. The cause is not using a domestic mirror address to pull Redis dependencies; the default official image cannot be pulled successfully in domestic network environments. - Unable to select a created Deepseek AI channel model in the application's AI model settings. The cause is failing to correctly configure
ROOT_KEYpermissions, or the channel model's interface address and key failing verification. - Deploying on an 8-core 32GB machine results in slow workflow response. The cause is not adjusting the
DB_MAX_LINKconnection count configuration, leading to exhausted database connection resources and inability to process budget data retrieval requests in parallel.
How to Confirm Configurations Are Correct
- Execute the
docker-compose pscommand, check that all container statuses are Up, with no abnormally exited containers. - Enter the system settings page in the FastGPT backend, confirm that database and Redis connection statuses show normal.
- Upload a departmental budget draft document, trigger a retrieval test, confirm that matching historical data and policy notices from corresponding sources can be retrieved.
- After adjusting the
DB_MAX_LINKconfiguration, check the database monitoring panel, confirm that connection counts stay within the set range.
The values given 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.