What data for this category looks like
Typical data for this category comes from the full production workflow of forging plants, aerospace and automotive forging part suppliers. Daily documents include forging process specifications, cutting cards, furnace heating records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data is collected from raw material quality certificates, furnace heating systems, press collection data, MES systems, non-destructive testing instruments, and customer forging part drawings. Update frequency is per part, per batch, or per furnace run. Data fields include forging part model, deformation step parameters, heating zones, furnace batch numbers, testing results, and more. Units include millimeters, degrees Celsius, tons and other standard units related to metal forming.
What constraints do these characteristics impose on deployment and upgrades
This category's data is multi-source, heterogeneous, and updated frequently. During deployment, support importing multi-format documents and automatically synchronizing data from systems such as MES and furnace heating systems. Fields with dense professional terms require selecting an embedding model adapted to the industrial manufacturing field during deployment to avoid loss of specialized vocabulary semantics. The strong association between forging part batches and furnace numbers requires configuring knowledge base retrieval filtering rules by batch dimension to improve retrieval accuracy. The high risk of error consequences requires enabling full-link logging during deployment to quickly troubleshoot retrieval anomalies.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securePass@mongodb:27017/forging_kb?authSource=admin | Stores structured data such as forging part batches and process specifications, supports creating indexes by furnace batch number |
DB_MAX_LINK | 20–30 connections | Adapts to the concurrent demand for batch data synchronization per furnace run, avoids exhausting database connections |
STORAGE_S3_ENDPOINT | http://minio:9000 | Stores unstructured documents such as ultrasonic testing reports and heat treatment work orders |
REDIS_URL | redis://:redisPass@redis:6379/0 | Caches frequently accessed process specification content to reduce database query latency |
FILE_TOKEN_KEY | 32-character random string | Ensures access security for the document upload interface, prevents unauthorized file uploads |
Three common errors
- Symptom: Docker image pull fails, logs show connection timeout. Cause: Correct image source acceleration is not configured, or the enterprise intranet environment does not open access permissions for the image repository.
- Symptom: The system cannot connect to MongoDB after startup, with an error prompt indicating authentication failure. Cause: The username or password configured in
MONGODB_URIis incorrect, or the database instance does not have authentication mode enabled. - Symptom: The sandbox service does not start normally, and document parsing tasks fail. Cause: The sandbox container is not added to the deployment configuration, or the port mapping configuration is incorrect, making it impossible to call document parsing capabilities.
How to confirm configuration is complete
- Run the
docker pscommand to confirm that the MongoDB, vector database, and FastGPT core containers are all running. - Upload a forging part ultrasonic testing report to verify that the document parsing function can normally extract testing result fields.
- Initiate a query related to process specifications to confirm that the retrieval results include matching process parameter content.
- Check the system logs to confirm that there are no error messages such as database connection timeouts or embedding model loading failures.
The values provided here are common starting points and should be evaluated against the reader's own samples.