What the Data for This Category Looks Like
The data sources for this category include the food safety sampling and monitoring system, the State Administration for Market Regulation sampling results query system, the laboratory information management system (LIMS) of inspection institutions, and the account books of food producers and operators. Data updates occur monthly. Daily documents include sampling plans, sampling slips, inspection reports, non-compliant food verification and disposal reports, and risk control notices. Data fields include core information such as sampling identification, inspection items, non-compliance determination results, and disposal progress. Core data consists of inspection reports issued by laboratories and full-process disposal records, which support hierarchical viewing for multiple roles including supervision departments and grassroots offices.
Constraints Imposed on Deployment and Upgrade by These Characteristics
The monthly update frequency requires configuring timed data source synchronization tasks during deployment, with pull rules adapted for multiple data source types. Core documents are long-text inspection reports and disposal process records. Parameters for large-file storage and indexing must be configured to avoid content truncation. The need to connect multiple data sources requires configuring differentiated database connection parameters during deployment, separating storage paths for structured and unstructured data. The multi-subject usage scenario requires configuring hierarchical access permissions during deployment, matching the operation scope of different roles. The timeliness requirements of the non-compliance disposal link require retaining audit logs for key operations during upgrades, ensuring process traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:password@mongodb-host:27017/fastgpt_sampling?authSource=admin | Stores unstructured documents such as inspection reports and sampling slips, supports high-concurrency read and write operations |
PG_URL | postgresql://user:pass@pg-host:5432/fastgpt_core?sslmode=require | Stores structured data such as sampling plans and disposal processes, supports complex queries and statistics |
REDIS_URL | redis://:redis-pass@redis-host:6379/0 | Caches frequently accessed sampling data and session information, reduces database load |
DB_MAX_LINK | 20–30 concurrent connections | Adapts to the concurrency requirements of monthly batch data synchronization, avoids connection pool exhaustion |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Generates file access tokens, ensures access security for sensitive documents such as inspection reports |
STORAGE_S3_ENDPOINT | Fill in according to the endpoint provided by self-built object storage or cloud service provider | Stores large-volume inspection report scans and sampling images, adapts to long-text storage requirements |
Three Common Mistakes
- Symptom: A dependency error occurs when running the
docker run --gpus all -itd -p 7231:7231command to start the marker_images:v0.1 image. Cause: GPU driver and container runtime adaptation parameters are not configured in advance, causing the image to fail to call GPU resources for document parsing. - Symptom: After upgrading FastGPT v4.17.0, an error of
cannot fetch internal urlis prompted when importing a food safety sampling system data source via URL. Cause: The access domain name of the internal system is not correctly filled in theFE_DOMAINconfiguration item, causing internal interface calls to be blocked. - Symptom: A newly deployed system cannot initiate AI conversations and cannot call model services normally. Cause: The
ROOT_KEYor the corresponding model's key parameters are not correctly configured, causing the AI service to fail identity verification.
How to Confirm Successful Configuration
- Run the
docker pscommand, confirm that the marker image container and the FastGPT main container are both running, and the port mapping configuration matches the preset values. - Log in to the FastGPT backend, enter the data source management page, test the connection to configured data sources such as the sampling monitoring system and LIMS, confirm that the connection status shows normal.
- Upload a sampling plan document, test the system's text parsing and indexing functions, confirm that the document content can be normally retrieved and displayed.
- Configure a test internal URL, initiate a knowledge base creation request, confirm that no fetch failure error occurs, verify that the
FE_DOMAINconfiguration takes effect.
The values provided 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 2026-09-28.