What the data for this category looks like
The data for this category primarily comes from internal enterprise procurement systems, supplier quotation documents, budget sheets, and contract systems. Daily generated documents include procurement applications, price comparison records, contract texts, and acceptance slips. Data updates are performed per individual procurement process, with updates completed after a single process finishes. Monthly cumulative update volume is stable on a regular basis. Common data fields include procurement order number, supplier entity information, budget amount, contract start and end dates, etc. There is no unified mandatory format, and it adjusts along with internal enterprise processes.
What constraints these characteristics impose on the deployment and upgrade phase
Procurement data sources are scattered. Deployment must support multi-source data access configuration, and integrate exported files from different procurement systems and contract texts in various formats. Update frequencies are uneven. Deployment must configure a flexible incremental synchronization mechanism to avoid excessive server resource usage from full pull operations. Document structures are inconsistent. Upgrades must retain compatibility with old field mappings to prevent historical procurement data from failing to load properly. Procurement processes involve financial and legal compliance links. Deployment must strengthen data permission controls to ensure sensitive fields are only accessible to authorized roles, reducing data leakage risks.
Configuration Settings
This page’s configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 connections | Adapts to the concurrency requirements of single-batch procurement data pulls, avoiding database connection exhaustion |
MONGODB_URI | mongodb://[username]:[password]@[server address]:27017/fastgpt?authSource=admin | Stores unstructured data of historical procurement contracts and acceptance slips, requires specifying the corresponding database name |
REDIS_URL | redis://:[password]@[server address]:6379/0 | Caches real-time tickets and supplier price comparison results, reducing database query pressure |
STORAGE_S3_ENDPOINT | Fill in according to the enterprise's internal object storage address | Stores uploaded attachments of procurement documents, adapts to the enterprise's existing storage architecture |
ROOT_KEY | Custom key string with complexity ≥16 characters | Configures super administrator permissions for managing global configurations of procurement processes, must comply with internal enterprise key specifications |
Three common mistakes
- Symptom: Image pull timeout or failure, console returns connection timeout or 404 error. Cause: Enterprise internal image acceleration address is not configured, and pulling official images directly is blocked by network restrictions.
- Symptom: Historical procurement data fails to load after upgrading to the specified version, the interface displays empty fields. Cause: Custom fields of old version data were not exported and mapped in advance, resulting in incompatible field structures after upgrade.
- Symptom: Ticket Q&A results are unrelated to procurement processes, and the number of recalled entries is insufficient. Cause: No knowledge base data source for this category was configured, using only a general document library leads to insufficient recall accuracy.
How to confirm configurations are correct
- Run the database connection test command to verify the connectivity of
MONGODB_URIandREDIS_URL, confirm there are no authentication or address errors. - Upload a historical procurement application document, check whether the file storage path is correct, and confirm that the
STORAGE_S3_ENDPOINTconfiguration takes effect. - Submit a simulated internal ticket with keywords related to procurement applications, check whether the Q&A recall results include the configured category knowledge base content.
- View system logs, confirm that the connection number configured by
DB_MAX_LINKdoes not trigger an over-limit error, and the database load is within the normal range.
The values provided here 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.