What this category’s data looks like
The main entities of this category are manufacturers of power battery packs, energy storage cabinets, and BMS systems. Daily generated documents include grouping rules, BOM lists, welding and torque records, BMS flashing records, insulation and voltage withstand reports, EOL test reports, and system configuration lists. Data sources cover cell capacity grading equipment, MES systems, welding equipment, torque tools, BMS software version repositories, and EOL test benches. Data update frequency is once per produced pack or cabinet, with synchronous updates each time the BMS software version changes.
What constraints these characteristics impose on deployment and upgrade workflows
Data sources for this category are scattered and linked across the entire production process. During deployment, interface with multiple types of equipment and system interfaces, and adapt to different data formats and synchronization protocols. Frequently updated production data requires the deployment workflow to support incremental synchronization mechanisms to avoid excessive resource usage from full data pulls. Multiple types of documents need to be classified and indexed according to business scenarios. During upgrades, retain the classification structure and permission configuration of the original knowledge base. Abnormal production data directly affects system functions, so deploy high-availability mechanisms to avoid downtime risks.
The following configurations apply to FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Configure as a MongoDB connection string with a replica set | Support data synchronization and high availability for multi-node clusters |
REDIS_URL | Configure a Redis instance with memory no less than 8000 MB | Cache session information and embedding results to support high-frequency queries |
ROOT_KEY | Use a strong random string of 32 characters or more | Secure access permissions for the management backend |
STORAGE_S3_ENDPOINT | Configure the endpoint address of a local object storage service | Store large-volume EOL test reports and document parsing caches |
DB_MAX_LINK | Set to 200-300 | Adapt to concurrent connection requirements for multi-device data synchronization |
Three common mistakes
- Symptom: The FastGPT management interface cannot be accessed after Docker deployment, even after port 3000 is opened. Cause:
FE_DOMAINis not correctly configured to the server public IP or record-filed domain name, which restricts front-end resource loading. - Symptom: No parsing option appears when uploading PDF files to the knowledge base. Cause: The pdf-marker service is not deployed correctly, or the access address of this service is not associated in the
FILE_TOKEN_KEYconfiguration. - Symptom: Data synchronization fails between nodes after cluster deployment. Cause:
MONGODB_URIis only configured with a single node address, and no replica set member addresses are added, resulting in connection restrictions.
How to verify a successful configuration
- Access the public network address corresponding to
FE_DOMAIN, confirm the FastGPT management interface loads normally, with no resource loading failure prompts. - Upload an insulation and voltage withstand report document, wait 1-2 minutes, then confirm the document has been indexed and its content can be retrieved via keywords.
- Initiate a query related to grouping rules, confirm the returned results match the grouping rule documents in the knowledge base.
- Check the MongoDB connection count monitoring panel, confirm the current connection count does not exceed the
DB_MAX_LINKconfiguration value.
The values provided are common starting points and should be measured against the reader's own samples.