What the data for this category looks like
Daily documentation for forging and die forging includes forging process specifications, cutting lists, heating furnace logs, forging process cards, mold life records, heat treatment work orders, ultrasonic testing reports, and more. Data sources include raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Data update frequency is per forging part, per batch of materials, or per furnace production cycle. Document fields include furnace batch number, forging ratio, mold usage count, testing results, and others. Forging ratio uses a numerical value plus a proportion symbol as its unit. Furnace batch numbers are 12 to 18 character sequences.
What Constraints These Characteristics Bring to the Deployment and Upgrade Phase
High-frequency, high-volume data updates require configuring connection pool parameters optimized for batch retrieval during deployment, to prevent concurrent access from exhausting database resources. Multi-source heterogeneous data formats require unified preprocessing rules to ensure consistent field mapping for documents exported from different systems. Precise matching requirements for key business fields such as furnace batch number and forging ratio require retrieval configurations to prioritize association with core fields. Additionally, the severe consequences of forging part errors require configuring complete failure retry and alert mechanisms during deployment, to ensure review processes are not interrupted. The FastGPT v4.17.0 deployment template must be adapted to on-premises private network environments, to avoid security risks from public network access.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 concurrent connections | Adapts to the multi-batch parallel document retrieval needs of forging workshops, prevents database connection exhaustion |
MONGODB_URI | Local private deployment address, bound to a dedicated storage volume of 100 GB or larger | Stores batch-generated forging document metadata, ensures persistence and access speed for multi-furnace data |
REDIS_URL | Local Redis instance, configured with a 10-minute expiration policy | Caches frequently retrieved heating furnace logs and process specification data, reduces database access pressure |
STORAGE_S3_ENDPOINT | On-premises private object storage endpoint address | Stores large-volume documents such as ultrasonic testing reports and heat treatment work orders, unifies management of unstructured data |
FE_DOMAIN | On-premises private domain name or intranet IP | Limits front-end access scope, ensures access security for classified forging part documents |
Three Common Misconfigurations
- Phenomenon: After deploying the Docker image, vector retrieval results have identical scores, and different forging part documents cannot be distinguished. Cause: The local vector database configuration file was not correctly mounted during image packaging, causing all retrieval calls to use fixed embedding vectors from the same pre-trained model by default.
- Phenomenon: Retrieval works normally on the first call, but subsequent requests with multi-turn context trigger errors. Cause: The session caching mechanism for
REDIS_URLwas not configured, and overflow of multi-turn conversation context data causes service interruption. - Phenomenon: Hybrid retrieval response time exceeds 15 seconds, failing to meet on-site review requirements. Cause: The number of documents retrieved per single request was not limited, and full historical forging part documents were loaded for comparison at the same time.
How to Confirm the Configuration Is Correct
- Log in to the FastGPT backend, check the connection status panel for
MONGODB_URI, confirm that a normal connection is displayed. - Upload a typical forging process specification document, initiate a keyword search, confirm that the returned results have differing scores and match core fields such as furnace batch number and forging ratio.
- Deploy the Docker image and mount the corresponding configuration volume, initiate more than 3 retrieval requests with context, confirm that no errors occur and single-round response time is stable within 10 seconds.
- Check the monitoring metrics for
REDIS_URL, confirm that cache hit count increases with retrieval frequency, with no significant backlog of cache misses.
The values listed are common starting points, and should be evaluated against the specific samples in use.