Data Profile for This Category
Data for image annotation projects originates primarily from image samples uploaded to annotation platforms, target bounding boxes and category labels generated by annotators, preset category dictionaries, and sampling inspection results and rework lists produced by quality inspectors. Daily deliverables include annotation specifications, bounding box examples, quality inspection reports, and rework lists. Data updates follow a daily batch schedule or per individual annotation task. Common fields include unique image identifiers, bounding box pixel coordinates, category labels, annotation statuses, and quality inspection results.
Constraints Imposed on Database and Operations Workflows
The multi-source data of image annotation projects has significant structural variation. It includes structured annotation coordinates and category labels, plus unstructured image metadata and quality inspection documents. This places requirements on a database’s hybrid storage capabilities. High-frequency daily or batch updates create sustained read and write pressure, so reasonable connection pool configuration is needed to avoid resource exhaustion. Annotation data and quality inspection results are strongly correlated. Database join query efficiency directly impacts the progress speed of annotation tasks. Indexes must be optimized to cover common query scenarios, avoiding performance loss from full table scans.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/label_db?authSource=admin | Adapts to storage of unstructured image metadata and bounding box JSON data for image annotations, specifies authentication source to secure access |
PG_URL | postgresql://user:pass@host:5432/label_metadata?sslmode=require | Used to store structured category dictionaries and quality inspection report metadata, supports complex join queries |
DB_MAX_LINK | 200–300 connections | Matches concurrent read/write demands from high-frequency daily or batch data updates, prevents task interruptions caused by connection exhaustion |
REDIS_URL | redis://:pass@host:6379/0 | Caches temporary annotation task statuses and sampling inspection results, reduces pressure from high-frequency database queries |
Three Common Misconfigurations
- Symptom: SQL statements generated on the data resource configuration page fail to execute. Accessing the SQL debugging interface results in unresolvable fields. Cause: Correct indexes are not configured for structured coordinate fields used in image annotations, so the parser cannot recognize non-standard field formats.
- Symptom: Generated query replies include unnecessary punctuation. Pasting directly into the SQL execution interface fails to parse. Cause: No preprocessing is performed for category label text of annotation tasks. Special punctuation damages SQL syntax structure.
- Symptom: Database connection timeout errors occur when submitting annotation tasks in batches. Cause: The
DB_MAX_LINKparameter is not set reasonably. Concurrent connection count exceeds the configured upper limit, causing the connection pool to be exhausted.
How to Verify Proper Configuration
- Run the database connectivity test script included with FastGPT v4.17.0 to validate connectivity for
MONGODB_URIandPG_URL, and confirm read and write permissions are functional. - Submit a batch of test annotation data, observe the response speed of database writes and queries, and adjust
DB_MAX_LINKto a value that matches current task concurrency levels. - Check the cache service monitoring dashboard to confirm the cache service configured via
REDIS_URLcan properly store temporary task statuses, with no abnormal timeouts. - Generate a test SQL statement that includes bounding box coordinates, verify that parsing and query results align with expectations, and confirm field formats are compatible with database syntax.
The values provided are common starting points, and should be evaluated 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.