What this category of data looks like
Public transit service inquiry data mainly comes from the public transit group’s operation scheduling system, offline station kiosk inquiry logs, and online user-submitted inquiry tickets. Data is divided into real-time and archived categories: Real-time data includes current trips, station stop status, and temporary route adjustment information, with update frequency varying based on actual scheduling conditions. Archived data includes historical inquiry records, response script templates, and route change announcement documents. Each entry contains fields such as inquiry time, problem type (route lookup, trip delay, station adjustment, etc.), user identifier, and processing result. Core identifier fields are route number and station name, and delay duration is measured in minutes.
What constraints these characteristics impose on deployment and upgrade
The low latency requirement of real-time data means a dedicated cache link must be configured during deployment to avoid delays caused by frequent relational database queries. Structured inquiry logs require stable relational database storage to support subsequent ticket statistics and auditing. Massive archived historical data needs cold storage solutions to reduce long-term storage costs. Rolling upgrade strategies must be used during upgrades to ensure real-time inquiry links remain uninterrupted and avoid disrupting normal user inquiries. Multi-dimensional inquiry classification requires pre-configuring knowledge base sharding rules to prevent chaotic recall results. Reference only; verify against the target FastGPT release and deployment documentation.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
REDIS_URL | redis://:bus_redis_pwd@192.168.1.100:6379/0 | Used to cache real-time trip data and user sessions, reduce database query latency, and adapt to concurrent fluctuations during morning and evening peak hours |
PG_URL | postgresql://bus_admin:bus_pg_pwd@192.168.1.101:5432/bus_consult | Stores structured inquiry logs, user feedback, and other auditable data, supporting complex ticket statistics and queries |
STORAGE_S3_ENDPOINT | http://192.168.1.102:9000 | Used to archive historical inquiry records and PDF documents of bus route changes, reducing hardware costs for hot storage |
DB_MAX_LINK | 20–30 | Adapts to peak concurrent transit inquiry traffic, preventing service freezes caused by exhausted database connection pools |
FE_DOMAIN | https://consult.busgroup.com | Configures cross-domain whitelists for the inquiry page, ensuring the front-end page can normally load knowledge base content and inquiry interfaces |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Used to encrypt uploaded inquiry attachments, ensuring transmission security for documents such as bus route adjustment notices |
Three Common Mistakes
- Phenomenon: The workflow canvas lags for more than 5 seconds when dragged, and saving configurations is unresponsive. Cause:
DB_MAX_LINKis not configured properly, leading to exhausted database connection pools and inability to read knowledge base route data in a timely manner. - Phenomenon: Tested route inquiries return information that does not match real-time scheduling data, with insufficient response accuracy. Cause: Cache expiration rules for
REDIS_URLare not configured, using expired static route data without synchronizing latest trip adjustment information. - Phenomenon: The inquiry page cannot be accessed normally after deployment, returning a 403 Forbidden status code. Cause:
FE_DOMAINis not configured correctly, and the front-end domain of the transit inquiry service is not added to the cross-domain whitelist, resulting in blocked front-end requests.
How to Confirm Configuration Completion
- Access the FastGPT administrative backend, navigate to the database monitoring page, and confirm the active database connection count does not exceed the configured
DB_MAX_LINKvalue. - Initiate a test route inquiry, enter a specific bus station and route number, and confirm the returned trip information matches the real-time scheduling data of the public transit group.
- Upload a PDF document of bus station adjustments, confirm the document can be properly stored to the storage medium corresponding to
STORAGE_S3_ENDPOINT, and confirm archived files are viewable in the administrative backend. - Access the inquiry page corresponding to
FE_DOMAIN, confirm the page loads normally, and confirm no cross-domain error prompts appear after submitting a test inquiry.
The values provided here are common starting points, and should be evaluated against the reader's own specific 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.