Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Railway operation business handling data sources include station ticket terminals, 12306 online systems, train scheduling ledgers, passenger service records, and more. Update frequencies are divided into real-time (ticket sales, rescheduling, refund operations), daily (daily passenger flow statistics, station-train handover records), and monthly (annual operation reports, business compliance archives). A single data entry includes fields such as handling number, handling type, handling time, associated train/station information, handling status, and anonymized passenger identifier. Corresponding types for each field are string, enumerated value, timestamp, string, enumerated value, and anonymized string.
What constraints do these characteristics impose during deployment and upgrade
Real-time updated business handling data requires configuring a low-latency vector index update mechanism during deployment. This prevents time gaps between the index and actual business data from affecting guidance accuracy. Multi-dimensional business fields require precise field mapping in the vector database. This avoids retrieval failures caused by missing fields or format errors. High-frequency concurrent business requests require database connection count configurations matched to actual load. This prevents service interruptions from exhausted connections. Railway business data includes sensitive information. Strict compliance with data storage regulatory requirements must be followed during deployment, with dedicated storage and access permissions configured. Incremental synchronization of business handling data must be ensured during upgrades. This prevents service interruptions caused by full data updates.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Matches the high-frequency concurrent request volume of railway business handling, prevents database connection exhaustion |
MONGODB_URI | Use dedicated replica set address, configure read-write separation parameters | Separates read and write requests for business data, improves overall retrieval and storage performance |
REDIS_URL | Configure as local cache instance, set session timeout to 300 seconds | Caches context data for multi-turn dialogues, ensures continuity of business handling process guidance |
STORAGE_S3_ENDPOINT | Use domestic cloud storage endpoints that meet compliance requirements | Ensures storage and access of railway business data comply with industry compliance standards |
ROOT_KEY | Use a strong random string of at least 32 characters | Ensures access security of system backend management interfaces |
FE_DOMAIN | Configure as the dedicated internal access domain for railway operations | Restricts external unauthorized access, improves security of internal services |
Three common mistakes
- All vector index query results are identical after containerized deployment: The locally loaded railway-specific vector model file was not correctly mounted to the container image. The container loaded the generic default model on startup, resulting in identical matching scores for all retrieval results.
- First call succeeds but subsequent multi-turn dialogue requests throw errors: The
REDIS_URLparameter was not configured correctly. Session cache storage failed, so context data for multi-turn dialogues was lost, and subsequent requests could not retrieve historical conversation information. - Local knowledge document library fails to load: The local file path was mistakenly used as the storage endpoint when configuring the
STORAGE_S3_ENDPOINTparameter. The system cannot recognize local file paths, causing document reading failures.
How to confirm the configuration is correct
- Log in to the system monitoring dashboard, check the database connection count monitoring, and confirm that the current connection count does not reach the upper limit set by the
DB_MAX_LINKconfiguration. - Initiate a business handling process guidance test that includes multi-turn dialogues. Input different business scenario questions, and confirm that the system can correctly associate historical dialogue context and provide consistent guidance.
- Upload local documents related to railway business handling, wait for the system to complete vector index generation, and check the document management interface to confirm the index status is completed.
- Access the internal domain configured for
FE_DOMAIN, and confirm that the frontend page loads normally and calls the backend business handling guidance service.
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.