This page configuration is based on FastGPT v4.17.0.
Data characteristics for this use case
Core data for forging and die forging scenarios comes from raw material quality certificates, heating furnace system data, press collection data, MES systems, non-destructive testing equipment, and customer forging drawings. Daily documents include forging process specifications, cutting cards, heating furnace logs, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data updates follow a cadence of per forging part, per material batch, or per furnace heat. Core fields include forging ratio, fiber direction, furnace batch number, heating interval parameters, and defect detection results.
Constraints for deployment and upgrade
Deployments must support interface integration with different systems to accommodate multi-source heterogeneous data sources. Near-real-time data synchronization mechanisms must be configured to handle high-frequency batch or furnace-based updates. Indexing must match exclusive parameters such as forging ratio and fiber direction to account for the professional nature of core fields, avoiding mismatches from generic field matching. Intranet deployment scenarios require storage and connection configurations adapted to environments without public network access. Data consistency must be enforced to prevent serious consequences such as forging scrap and batch isolation.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:pass123@192.168.1.20:27017/fastgpt?authSource=admin | Stores documents such as ultrasonic testing reports, forging process specifications, and structured forging batch data |
REDIS_URL | redis://:redisPass@192.168.1.21:6379/1 | Caches frequently queried fields including furnace batch number and forging ratio, to support quick quotation verification |
STORAGE_S3_ENDPOINT | http://192.168.1.22:9000 | Stores large-volume testing reports and process documents, adapted to intranet deployment scenarios without public network access |
DB_MAX_LINK | 20–30 | Supports concurrent queries for multiple forging batch data, avoiding connection exhaustion |
ROOT_KEY | Custom high-complexity string, such as aB3!kL9@mN7 | Manages FastGPT backend permissions for intranet deployments, preventing unauthorized access |
FE_DOMAIN | http://fastgpt-fe:8080 | Configures the frontend access domain, adapted to intranet access paths |
Three common configuration mistakes
- Symptom: Deploying the rerank model via docker-compose may result in the FastGPT backend failing to display the model. Cause: No dedicated database index is allocated in the
REDIS_URLconfiguration to cache model parameters, or the model container mount path is not shared with the FastGPT container. - Symptom: Configuring a model as version v2 and selecting it by name in the backend may still result in the v1 version being used during invocation. Cause: The model configuration collection corresponding to
MONGODB_URIis not updated synchronously, or environment variables do not override the default model configuration. - Symptom: Intranet offline deployment may cause the OneAPI container to restart continuously, with logs indicating startup failure. Cause: The offline exported image is not correctly loaded into the intranet Docker environment, or the
PG_URLconfiguration points to a public network database address, preventing connection establishment.
How to verify configuration is complete
- Access the FastGPT backend system settings page, review database and storage connection status, and confirm that the object storage linked to
STORAGE_S3_ENDPOINTcan successfully upload and download ultrasonic testing report files. - Initiate a simulated forging part quotation query, verify that returned fields such as forging ratio and furnace batch number match the data in the intranet MES system.
- Restart the FastGPT container, then check logs for error messages such as database connection timeouts or model loading failures.
- Attempt to access the backend management interface using a key other than
ROOT_KEY, confirm that access to the system is blocked.
The values provided are common starting points and should be measured against the reader's own samples.