What the data for this category looks like
Public transit bus ledger data comes from on-board intelligent terminals, dispatch management systems, passenger flow collection devices, and maintenance systems. Update rhythm follows real-time synchronization or batch upload every 15 minutes. Single ledger document includes structured fields such as route number, stop, on-time duration, passenger trips, daily operating mileage, and energy consumption data. Some documents contain unstructured voice dispatch records and vehicle violation photos. Most field units are kilometers, trips, kilowatt-hours, and minutes. Most documents are structured tables or in CSV format.
What constraints these characteristics impose during deployment and upgrade
Public transit bus ledgers combine structured tables with a small amount of unstructured voice and image records, and have a high update frequency. Deployment requires adapting to multi-source data parsing logic. High-concurrency real-time synchronization requests increase database load. Connection pool parameters need to be configured appropriately. Long-text operation ledgers and unstructured attachments put capacity demands on knowledge base chunking and vector storage. During upgrades, adjust vector database storage thresholds and indexing strategies simultaneously to avoid chunk loss or index failure.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
REDIS_URL | redis://:custom password@server IP:6379/0 | Adapts to the high-concurrency real-time caching needs of bus ledgers, stores temporary dispatch data and user sessions |
MONGODB_URI | mongodb://username:password@server IP:27017/fastgpt?authSource=admin | Stores unstructured voice dispatch records and historical ledger backups |
DB_MAX_LINK | 20–30 | Handles batch uploaded ledger data concurrent requests every 15 minutes, prevents connection exhaustion |
| Knowledge base chunk length | 800–1200 characters | Adapts to the number of fields and text length of single bus ledger records, avoids chunk loss |
| Single recall limit | Top 10 entries | Matches the structured field association logic of bus ledgers, reduces invalid recalls |
STORAGE_S3_ENDPOINT | Fill in according to the access address of the local object storage service provider | Stores batch-uploaded ledger CSV files and vehicle attachment files |
Three Common Misconfigurations
- Symptom: When the knowledge base chunk length is set to 3000 characters, some long ledger records experience chunk loss, and the interface shows insufficient recalled result entries. Cause: The chunk threshold exceeds the system's default maximum processing length without adapting to the long-text structure of bus ledgers, leading to chunking failure.
- Symptom: When deploying on a domestic cloud server, pulling images via
REDIS_URLfails, and the container startup reports a connection timeout error. Cause: The server outbound rule for port 6379 is not configured, or the image source does not adapt to the domestic network environment. - Symptom: When introducing a local image recognition model, vehicle violation photos in the ledgers cannot be parsed normally, and logs show model call failure. Cause: The model access port and authentication parameters are not correctly mapped in the FastGPT configuration, and the input format of the image model is not adapted.
How to Confirm Configuration Is Complete
- Access the FastGPT backend, check the connection status of the cache service corresponding to
REDIS_URL, and confirm normal online status. - Upload a complete bus operation ledger CSV file, check the chunking results, and verify no loss or truncation.
- Initiate a ledger summary request, wait for the specified duration to generate the corresponding report, and check that report fields match the source data.
- Check the database connection pool monitoring corresponding to
DB_MAX_LINK, confirm the number of connections does not exceed the configured threshold, and no error logs are present.
The values provided here are common starting points, and should be validated against actual deployment 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.