What data for this category looks like
Data sources for this category include requirement specifications, test environments, customer business samples, and defect systems. Update frequency is per phase or per version. Daily deliverable documents include UAT use cases, acceptance issue tickets, acceptance reports, and sign-off confirmation forms. UAT use cases include scenario, precondition, and expected result fields. Acceptance issue tickets include issue description, reproduction steps, and severity fields. Acceptance reports include issue summary and rectification status fields. Sign-off confirmation forms include acceptance conclusion and participant signature fields. Data structure is adjusted based on customer business scenarios, with no fixed unified format. Typical users are implementation consultants, test managers, and customer business representatives. The core purpose is to support full-process confirmation for user acceptance testing.
What constraints these characteristics impose on deployment and upgrade workflows
Data sources for this category are scattered and rely on customer-side environments. Isolated database and storage resources must be configured during deployment to prevent mixing of customer business data and internal test data. Data update frequency is high. Each version upgrade requires synchronization to adapt to the latest acceptance document structure. Deployment configurations must support custom field mapping. Acceptance issue tickets must be linked to defect systems for closed-loop management. Defect system connection parameters must be configured. Sign-off confirmation forms require compliant storage. Storage paths that meet permission requirements must be configured to ensure traceability of the acceptance process.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapts to database concurrent access demands during batch UAT use case execution, matches test concurrency levels during the acceptance phase |
MONGODB_URI | Customer acceptance environment exclusive database address | Isolates customer business data and internal test data, meets storage compliance requirements for acceptance data |
REDIS_URL | Set based on actual measurements | Adapts to cache query demands for acceptance issue tickets, balances response speed and memory usage |
FILE_TOKEN_KEY | 32-character random string | Secures upload and download permissions for files including UAT use cases and acceptance reports, prevents unauthorized access |
STORAGE_S3_ENDPOINT | Customer storage service node address | Adapts to storage demands for acceptance sign-off confirmation forms, matches the customer's existing storage environment |
ROOT_KEY | Set based on actual measurements | Controls administrator modification permissions for acceptance configurations, prevents accidental operations from impacting the acceptance process |
Three common mistakes
- Symptom: Custom environment variables configured during Docker deployment in workflows return empty values. Cause: Environment variable passing is not configured in container startup parameters. The FastGPT process cannot obtain externally set environment variables.
- Symptom: After upgrading to FastGPT v4.17.0, m3e model calls return abnormal results. Cause: Adaptation configuration for model connections is not updated synchronously. Old version model call rules are incompatible with the new version.
- Symptom: The file parsing module reports split-related errors, and uploaded acceptance documents cannot be parsed. Cause: The dependency storage path for file parsing is not correctly mounted during Docker deployment. The split tool cannot normally read parsing resources.
How to confirm configurations are correct
- Execute a batch UAT use case execution task. Check database connection count monitoring to confirm that connection counts fall within the parameter range configured during deployment.
- Upload acceptance-related documents. Verify that documents can be uploaded, stored, and downloaded normally to confirm that storage configurations are effective.
- Call custom environment variables in a workflow node. Verify that returned values match the variable content set during deployment.
- Start a model call test. Confirm that model returned results comply with configured adaptation rules.
The values provided are common starting points and should be measured against local 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 2026-10-19.