What data looks like for this category
Organizations in this category include manufacturers of disposable medical consumables, implantable and interventional devices, diagnostic equipment, and active medical devices. Daily processed documents include production process specifications, batch production records, clean zone records, sterilization batch records, inspection reports, software version records, adverse event materials, and traceability materials. Data sources cover design history files, device and material batch information, MES systems, clean environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Data updates occur per batch or per product, plus regular synchronization per environmental monitoring plans. Document structures include fields such as controlled numbers, batch information, operation parameters, and inspection results. Some records require links to unique device identifiers and biocompatibility test data.
Constraints on deployment and upgrade
Data sources for this category are scattered and use diverse formats, including structured records exported from systems and unstructured documents scanned from paper. Deployments must support multi-source data access and format compatibility. Data updates happen frequently and tie to the full product lifecycle. Deployments must configure incremental synchronization mechanisms to avoid resource consumption from full re-scanning. Documents involve product traceability and aseptic control requirements, so deployments must strengthen data verification and permission control. Upgrades must ensure historical data consistency and traceability, and avoid access exceptions for controlled documents caused by configuration changes.
Configuration settings
| Config Item | Recommended Value | Basis for this value |
|---|---|---|
MONGODB_URI | mongodb://<username>:<password>@<host>:<port>/fastgpt?authSource=admin | This category stores multi-batch controlled documents and traceability data, so a dedicated database instance and authentication information are required, based on FastGPT v4.17.0 configuration specifications |
REDIS_URL | redis://:<password>@<host>:<port>/0 | Used to cache retrieval results and session data, configure a separate instance to avoid conflicts with other business caches |
STORAGE_S3_ENDPOINT | s3.<region>.aliyuncs.com | Sterilization records and clean environment monitoring data for this category require long-term archiving, so adapt the object storage service access endpoint |
DB_MAX_LINK | 20–30 connections | This category has frequent data updates, so limit database connections to avoid resource exhaustion |
| Custom reranker model request address | http://<docker container internal IP>:<port> | Adapt to the reranker model deployed in Docker, ensure container network reachability |
Three common configuration errors
- Phenomenon: Knowledge base retrieval responses are slow, and the interface shows request timeout. Cause: No independent
REDIS_URLinstance is configured, and shared resources between cache and business data cause blocking. - Phenomenon: The reranker model test passes, but every retrieval result reranking returns false, and the interface returns empty reranking results. Cause: The correct internal IP address of the Docker container is not filled in the custom request address, or the container port mapping is not opened.
- Phenomenon: After upgrading
fastgpt-mcp-server, the FastGPT service fails to start, and the log shows a configuration file format error. Cause: The originalMONGODB_URIandREDIS_URLconfigurations were not retained during the upgrade, causing the service to fail to connect to dependent services.
How to confirm correct configuration
- Log in to the system configuration page in the FastGPT v4.17.0 backend, and check that configuration information for dependent services such as
MONGODB_URIandREDIS_URLmatches actual deployed service addresses. - Upload a typical document of this category, such as a batch production record, run a document parsing test, and confirm that format recognition and field extraction work normally.
- Enter the reranker model configuration page, fill in the address of the reranker model deployed in Docker, run a connectivity test, and confirm that returned results are normal.
- View the system monitoring panel, confirm that database connection count does not exceed the configured
DB_MAX_LINKvalue, and that retrieval response time meets configured threshold requirements.
The values provided are common starting points and should be evaluated against samples relevant to the specific deployment.