What this category of data looks like
Data for image annotation projects typically comes from image samples uploaded via annotation platforms, bounding box examples produced by annotators, preset category dictionaries, and quality inspection reports and rework lists generated during random inspection. Data updates follow a daily sync schedule or update per annotation batch. Each data entry includes fields such as target bounding box coordinates, category labels, annotation confidence, and quality inspection judgment results. There are no unified fixed units, and values adjust based on the specific requirements of annotation categories and projects. Daily associated documents include annotation specifications, bounding box example instructions, batch quality inspection reports, and rework rectification lists.
Constraints on deployment and upgrade
Multi-batch data update requirements for image annotation projects require reserving sufficient database read/write buffers during deployment to avoid connection blocking during batch annotation. Image samples and quality inspection reports have large storage volumes, so configure storage parameters adapted to large files to prevent storage overflow. Category dictionaries and field rules differ across projects, so avoid overwriting custom configurations during upgrades to prevent resetting project-specific settings. Rework lists generated during annotation require real-time synchronization, so deployment must ensure task queue stability to avoid ticket response delays.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 | Adapt to daily batch data read/write requests of annotation projects, avoid database connection exhaustion |
MONGODB_URI | Configure a dedicated connection string based on project storage requirements | Store unstructured data such as annotation samples and quality inspection reports, isolate business data from other systems |
REDIS_URL | Configure an independent cache instance, set cache duration to 300 seconds | Cache real-time annotation progress and rework lists, enable quick queries during operations duty |
STORAGE_S3_ENDPOINT | Dedicated node address adapted to large file storage | Store large-volume image samples and bounding box examples, meet storage requirements for batch annotation |
FE_DOMAIN | https://internal annotation platform dedicated domain | Configure the frontend access address for jumping to the corresponding annotation task page in internal tickets |
Three common misconfigurations
- A missing dependency error occurs when executing the
docker build -f ./projects/command. This is caused by failing to pre-install the Docker dependency packages required by FastGPT v4.17.0, and failing to clear local residual image cache. - The database cannot be created normally after local deployment. This is caused by incorrect configuration of
MONGODB_URIorPG_URLparameters, leading to incorrect connection string format or insufficient database access permissions. - Annotation data queries cannot be triggered in internal tickets. This is caused by failing to configure cache permissions for
REDIS_URL, resulting in failure to normally cache and call real-time data.
How to confirm configuration validity
- Execute the database connection test command to verify that the configurations of
MONGODB_URIandPG_URLcan establish normal connections. - Upload a test image sample to check if the storage path matches the configuration node of
STORAGE_S3_ENDPOINT. - Submit an internal ticket to query annotation progress, confirm that the frontend jump address matches the configuration of
FE_DOMAIN. - View the Redis cache log to confirm that cache tasks for
REDIS_URLhave been written normally.
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.