What this category of data looks like
Data for this category primarily comes from crowdsourcing platform backends, annotation quality inspection records, annotator training exam results, and financial settlement systems. Daily maintenance documents include personnel access rules, post-training materials, monthly performance reports, and quarterly settlement lists. Data update frequencies are split into daily and monthly updates. Daily data includes daily annotation completion volume and quality inspection exception records. Monthly data includes monthly performance rankings and settlement details. Data fields include unique personnel identifiers, access review status, training assessment scores, daily annotation task volume, settlement amounts, and other related items.
Constraints during deployment and upgrade
Data sources for this category are scattered. During deployment, connect to multiple data sources including crowdsourcing platforms, quality inspection systems, and financial systems. Use parameters such as MONGODB_URI and REDIS_URL to configure a stable data synchronization link. Daily documents are diverse. They include rule-based, training, and settlement files. Before deployment, configure parameters such as FILE_TOKEN_KEY to ensure security of file upload and parsing. Data update frequencies differ between daily and monthly updates. During deployment, plan reasonable scheduled synchronization tasks to avoid excessive resource usage. During upgrades, ensure existing data synchronization logic is not disrupted. Prevent omission of settlement data or abnormal quality inspection records.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapts to concurrent requests from multiple data source synchronizations, avoids database connection overflow |
MONGODB_URI | mongodb://username:password@host:port/database?authSource=admin | Connects to the annotation quality data storage repository of crowdsourcing platforms, ensures stable data reading and writing |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed personnel performance data, reduces database query pressure |
FE_DOMAIN | Public IP of the local deployment server or bound domain name | Ensures normal access and callback of the internal ticket Q&A interface |
FILE_TOKEN_KEY | Randomly generated 32-character string | Encrypts upload requests for files such as annotator training materials and settlement lists, prevents unauthorized access |
ROOT_KEY | Complex random string | Configures system administrator permissions, ensures security of internal operations and maintenance |
Three common mistakes
- Phenomenon: After upgrading to FastGPT v4.17.0, rule display symbols appear at the end of model conversation response results. Cause: The rule traceability switch in debug mode is not turned off in system configuration, causing configured personnel access rules to be appended directly to response content.
- Phenomenon: Daily annotation data from crowdsourcing platforms cannot be synchronized during deployment. Cause: The
MONGODB_URIparameter is not configured correctly, resulting in connection timeout or insufficient permissions with the data source. - Phenomenon: No response after submitting an internal ticket. Cause: The
REDIS_URLparameter is not configured, resulting in failure to generate ticket cache and interruption of the request link.
How to confirm configurations are set correctly
- Run the data synchronization script, check if annotation quality data from crowdsourcing platforms can be imported into the system normally, verify field integrity of imported data.
- Access the address configured via
FE_DOMAIN, open the internal ticket Q&A interface, submit a test ticket and confirm normal receipt of feedback. - Upload a copy of personnel training materials, check if file upload and parsing work normally, confirm no additional authorization is required for file access.
- View system logs, confirm no errors in database connection and cache service running status, confirm concurrent connection count of
DB_MAX_LINKdoes not reach the upper limit.
The values listed are common starting points. They should be evaluated against the specific samples of the deployed environment.
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.