What this category of data looks like
Typical entities are smelting, electrolysis, laboratory, and scheduling departments of copper, aluminum, lead-zinc, and other smelting enterprises. Daily generated documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis results, DCS system data, cell voltage and current monitoring data, laboratory test data, energy and environmental protection monitoring data. Data update frequency is per batch of raw materials, per furnace, or per cell. Some key parameters are updated in real time. Documents contain fields such as raw material composition ratios, cell operating parameters, and laboratory indicators. There is no unified fixed format, and details vary by production link.
What constraints these characteristics impose on deployment and upgrade
The high-frequency real-time data and multi-source heterogeneous document characteristics of this category create multiple constraints for FastGPT deployment and upgrade. First, concurrent write demands from multiple data sources require sufficient database connection resources during deployment. This prevents data loss caused by connection exhaustion. Second, real-time updated cell condition and laboratory data need a stable caching mechanism to reduce pressure on the core database. Third, unstructured multi-type documents require flexible storage and index configurations to ensure efficient subsequent audit and retrieval. The upgrade process must maintain an uninterrupted data link. This avoids disruption to normal access of inspection records in production links due to configuration changes.
How to set configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Matches the concurrent write requirements of multiple data sources for this category, and adapts to real-time updated inspection and process record data |
MONGODB_URI | mongodb://[username]:[password]@[host]:27017/fastgpt?authSource=admin | Stores multi-source heterogeneous inspection documents and process records, supports high-throughput unstructured data read and write |
REDIS_URL | redis://:[password]@[host]:6379/0 | Caches frequently queried electrolytic cell condition and laboratory data to reduce access pressure on the core database |
FE_DOMAIN | https://[internal enterprise production domain] | Adapts to the access scenario of intranet deployment, avoids access exceptions caused by cross-domain configuration issues |
FILE_TOKEN_KEY | randomly generated 32-bit secure string | Ensures access permissions for inspection documents and production ledgers, prevents unauthorized content disclosure |
STORAGE_S3_ENDPOINT | http://[local storage host]:9000 | Adapts to the document storage needs of local private enterprise deployments, matches storage paths for multiple types of production documents |
Three common misconfigurations
- Issue: After upgrading, calls to third-party large language models return a 401 Unauthorized error. Cause:
ROOT_KEYor the storage format of the model API key is not configured correctly, leading to authentication failure. - Issue: After upgrading a local private deployment, some historical inspection documents fail to load normally. Cause:
STORAGE_S3_ENDPOINTor the local storage path is not configured correctly, causing deviation in document storage path mapping. - Issue: After deploying the bge-rerank-base image, test requests receive no response. Cause:
DB_MAX_LINKis not adjusted to adapt to concurrent requests from the reranking model, leading to database connection pool exhaustion and inability to process subsequent requests.
How to confirm the configuration is correct
- Log in to the system settings page of the FastGPT backend, and verify that the values of core configuration items such as
DB_MAX_LINKandREDIS_URLmatch the preset configuration. - Import a standard electrolytic cell condition record document, test that document parsing and indexing functions work normally, and that keyword fields within the document can be retrieved.
- Initiate a simulated inspection record audit query, confirm that returned results include document content corresponding to this category, such as batching lists and laboratory reports.
- Check container runtime logs, confirm that there are no error messages related to database connection timeouts, authentication failures, or storage path errors.
The values given are common starting points and should be measured against the reader's own samples.