What the Data for This Category Looks Like
The data for text annotation projects primarily comes from corpora provided by clients, annotation results produced by in-house annotation platforms, industry-standard vocabularies, and quality inspection sampling results. Daily generated documents include annotation specifications, sample packages, quality inspection reports, and issue feedback forms. Data update cycles typically follow daily or per-annotation-batch schedules. Core fields include annotated text, entity tags, intent classification results, quality inspection scores, and more. The overall focus is on annotation logic for semantic boundaries, entities, and intents.
Constraints Imposed on Deployment and Upgrade by Data Characteristics
The data characteristics of text annotation projects impose clear constraints on deployment and upgrade workflows. Multi-source, frequently updated data requires that deployments support multi-channel data access configurations, and upgrades must ensure consistent data synchronization. The fixed structure of daily documents requires presetting storage and parsing paths for corresponding documents during deployment. Daily or batch update cycles require post-deployment services to support real-time or bulk data loading, and upgrade processes must not interrupt core annotation and quality inspection workflows. For annotation fields focused on semantics, entities, and intents, tag validation rules must be configured during deployment, old-format compatibility must be maintained during upgrades to avoid annotation interruptions, and logging and rollback mechanisms must be reserved to handle failures.
Configuration Guidelines
This guidance applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:27017/fastgpt_text_label | Stores annotated text, tag results, and quality inspection data; requires binding to a dedicated business database |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed annotation specifications, vocabularies, and temporary annotation task data; uses the default database 0 |
DB_MAX_LINK | 200–300 connections | Meets the database connection requirements for concurrent use by multiple annotation teams, preventing task interruptions caused by exhausted connections |
STORAGE_S3_ENDPOINT | Fill in the intranet address provided by the object storage service provider | Stores large documents such as sample packages and quality inspection reports; intranet addresses reduce transmission latency |
FE_DOMAIN | Public domain name of the deployed service | Used to generate access links for annotation tasks and file preview addresses, and adapts to cross-domain requirements for frontend pages |
Three Common Configuration Issues
- Issue: Third-party API configuration is lost after an upgrade, and the interface displays empty configuration items. Cause: Configuration information for third-party annotation interfaces was not written to the persistent storage directory, and the mount directory was not correctly bound during container upgrades, resulting in configuration loss when the container is destroyed.
- Issue: The
packages/plugins/registerfile cannot be found after Docker deployment, and the system plugin configuration interface fails to load. Cause: The plugin registration directory was not correctly copied to the container image during deployment, or the mounted local directory has insufficient permissions, preventing file access. - Issue: Page text input lag occurs when there are many workflow nodes. Cause: The frontend does not implement debounce handling for real-time validation of annotated text, and does not limit the batch update frequency of node rendering, leading to lag caused by extensive DOM reflows.
How to Confirm Successful Configuration
- Review service startup logs to confirm there are no errors in initialization logs for databases and cache services, and adjust connection count configurations based on actual concurrency requirements.
- Upload a test sample package to verify that the storage service can correctly receive the package and generate access links, confirming the validity of storage configurations.
- Access the system plugin configuration interface, attempt to save third-party API configurations, and confirm that configuration information can be properly persisted without loss.
- Open the annotation workflow page, add multiple test nodes, and input text. Observe page response status, and adjust relevant frontend rendering parameters as needed.
The configuration 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.