What the data for this category looks like
The data for the 12345 citizen hotline workorder handling scenario comes primarily from the 12345 citizen hotline platform, contractor business systems, internal knowledge bases, return visit evaluation data, and local hotline management regulations. Daily generated documents include request registration forms, dispatch records, handling responses, return visit records, extension applications, return explanations, and knowledge base entries. Data updates daily. Core fields include request number, contracting entity, request content, handling node, response content, and return visit result. Data flows only according to business processes, with no fixed statutory time limit fields.
Constraints Imposed by These Characteristics During Deployment and Upgrade
Daily updated workorder data requires an incremental synchronization mechanism during deployment. This avoids excessive server resource usage from full data pulls. Multi-data source access must adapt to the interface specifications of different business systems. This ensures accurate mapping of core fields such as request data and dispatch records. The workorder closed-loop process relies on stable storage and caching services. Any configuration error may lead to workorder overdue, response return, or repeated public complaints. During upgrades, compatibility with original workorder data must be maintained. This prevents historical data loss or flow abnormalities caused by field mapping changes.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
REDIS_URL | Format: redis://:authentication password@local host IP:6379/0, use a locally Docker-deployed Redis instance | Session caching and task queues for hotline workorders depend on Redis; local deployment reduces cross-node latency |
MONGODB_URI | Format: mongodb://username:password@host IP:27017/12345_workorder, includes authentication information and target database name | Workorder data, request records, and historical responses are stored in MongoDB; connection stability must be ensured |
DB_MAX_LINK | 100–150 connections | Daily updated workorder data volume is large; this value balances connection usage and concurrent processing capacity |
ROOT_KEY | Custom high-strength random string, or retain the initially generated key and persist the storage directory | Used for root user permission verification to prevent unauthorized access to the backend configuration interface |
STORAGE_S3_ENDPOINT | Endpoint address adapted to internal storage, such as local MinIO or compliant cloud storage services | Request attachments and knowledge base files require persistent storage and must match internal data security specifications |
Three Common Configuration Mistakes
- After upgrading FastGPT v4.17.0, the root user password automatically resets to the initial value the next day. Cause: The storage directory for the
ROOT_KEYparameter is not persisted, or the key configuration is not written to environment variables during deployment. The key becomes invalid after service restart. - After accessing qwen3-embedding-8b, the workorder-associated file status continuously displays "Indexing". Cause: The
DB_MAX_LINKconfiguration is too low. This causes the indexing task queue to block, or the dimension parameter of the embedding model is not correctly configured. - Unable to connect to the Alibaba Cloud Redis mirror instance. Cause: The
REDIS_URLparameter does not include the authentication password and correct port of Alibaba Cloud Redis, or the server network policy does not open port 6379.
How to Confirm Configuration Is Complete
- Run the
docker logs fastgptcommand. Check that the logs contain no error messages about database connection failures or caching service timeouts. - Log in to the FastGPT backend management interface. Modify the root user password, restart the service, and log in again after 24 hours. Confirm that the password does not automatically revert to the initial value.
- Upload a test request registration form. Check that a complete workorder record is generated in the corresponding MongoDB database, and that field mapping is correct.
- Call the embedding model interface. Verify that the returned vector dimensions match the official parameters of qwen3-embedding-8b. Confirm that the indexing task completes 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 2026-09-28.