What this category of data looks like
Data for this category comes from ticket systems, chat logs, product logs, and customer contract SLAs. Update frequency is daily or real-time. Daily processing relies on four types of documents: Customer tickets contain problem descriptions, submission times, and customer identifiers. Problem classification tables divide levels by impact scope and urgency. Processing records retain response durations and assignment results. Service reports summarize service compliance status during the cycle. Data fields include customer ID, problem classification code, response timeout threshold, and more. There is no unified standard format, so multi-source data access adaptation is required.
What constraints do these characteristics impose during deployment and upgrade
Multi-source data access requires configuring connection parameters for different data sources during deployment, to avoid data synchronization failures caused by format mismatches. Unlike defect management that starts from code change entries, this category accesses data from customer-submitted ticket entries. Deployments must configure API connection permissions for the ticket system. The real-time update feature requires reserving a data synchronization window during upgrades, to prevent interruptions that affect ticket flow. Problem classification and assignment logic relies on existing configurations. After an upgrade, verify field mapping relationships to ensure ticket fields correctly match classification rules. The accuracy of SLA-related data directly relates to service compliance. Deployments must configure monitoring indicators to detect data anomalies promptly. The most time-consuming problem reproduction step relies on complete access to logs and chat records. The deployment phase must verify log collection completeness.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Cluster connection address with read-write permissions | Ticket systems and processing records are mostly stored in MongoDB; ensures connection stability and permission compliance |
REDIS_URL | Cache node address with access password, set connection timeout to 30 seconds | Used to cache real-time ticket data and classification rules; avoids delays from frequent data source queries |
STORAGE_S3_ENDPOINT | Endpoint address adapted to enterprise object storage | Used to store ticket attachments, log backups, and other files; ensures data persistence |
DB_MAX_LINK | 100–200 | Limits database connection count; prevents resource exhaustion in high-concurrency ticket scenarios |
| Ticket classification rule configuration | Divided by two dimensions: urgency and impact scope | Matches the problem classification table structure in business documents; ensures accuracy of automatic ticket classification |
Three common mistakes
- Phenomenon: Garbled characters appear in the online deployed ticket knowledge base after import, while local deployment operates normally. Cause: The storage endpoint configured via
STORAGE_S3_ENDPOINTduring online deployment does not enable Chinese encoding adaptation, leading to abnormal parsing of file content or metadata. - Phenomenon: After upgrading to FastGPT v4.17.0, workflow nodes cannot obtain code run output as AI conversation variables. Cause: Variable mapping configuration items were not synchronized during the upgrade, causing output fields to not be correctly bound to subsequent workflow nodes.
- Phenomenon: When submitting a short text query, the system first returns the full result at once, then switches to streaming output mode. Cause: The cache timeout set in
REDIS_URLis too short, causing context cache invalidation; the system falls back to full data query mode.
How to confirm configurations are correct
- Run a single ticket data synchronization test, verify that multi-source imported ticket content displays normally, with no garbled characters or missing fields.
- Submit test tickets with different urgency and impact scopes, confirm that the system’s automatic classification results match the preset ticket classification rules.
- Check the database connection monitoring panel, confirm that the connection count set via
DB_MAX_LINKdoes not trigger over-limit alerts. - Trigger the workflow code run node, verify that output content is correctly passed to subsequent AI conversation links, with no empty variables.
The values given 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.