What data for this category looks like
Data for this category comes from hot metal and scrap charging data, L2 process control data, laboratory composition reports, MES production scheduling systems, energy medium monitoring systems, and equipment status sensors. Data update frequencies fall into three categories: per heat, real-time, and per shift. Daily documents include heat plans, batching lists, converter smelting records, composition test reports, continuous casting process records, and slab quality judgment sheets. Most documents take the form of structured tables and time-series records, with fixed fields including heat number, cast sequence number, composition content (units: %, ppm), smelting temperature (unit: ℃), and equipment operating parameters.
What constraints these characteristics impose on deployment and upgrade
Since data sources cover multiple industrial systems with varying update frequencies, deployment requires synchronization and caching configuration for multi-source data to avoid data inconsistency. Since documentation centers on heats and cast sequences, with large volumes of time-series structured records, deployment needs special configuration for structured data vector retrieval. Since iron and steel smelting and continuous casting processes are high-temperature continuous operations with no arbitrary downtime allowed, upgrades must use rolling update strategies to ensure production processes are not interrupted. Since document errors can cause serious consequences such as heat downgrades or continuous casting interruptions, deployment needs to configure operation logs and failure rollback mechanisms to reduce change risks.
How to set the configuration
This configuration is based on FastGPT v4.17.0, adapted for cross-review of technical documentation for iron and steel smelting and continuous casting scenarios.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt_steel?authSource=admin | Adapts to storage requirements for multi-source structured documents for iron and steel smelting, supports high-concurrency heat data writes |
REDIS_URL | redis://:pass@host:6379/0 | Caches real-time updated heat plans and continuous casting process data, reduces query latency for multi-system data |
FE_DOMAIN | https://steel-technical-doc.your-domain.com | Matches internal domain specifications of iron and steel enterprises, ensures normal access to the OneApi page |
STORAGE_S3_ENDPOINT | https://s3-internal.steel-enterprise.com | Adapts to internal enterprise object storage, stores large volumes of heat records and composition report attachments |
DB_MAX_LINK | 20–30 connections | Adapts to high-concurrency data write requirements per heat and real-time scenarios, prevents database connection pool exhaustion |
Three common mistakes
- Phenomenon: The OneApi page returns an inaccessible error or 404 status code. Cause: The
FE_DOMAINconfiguration does not match the actual internal access domain of the enterprise, or the front-end deployment path does not match the configuration parameters. - Phenomenon: Multiple accounts cannot be created, or account data within the same team cannot be shared. Cause:
REDIS_URLis not configured correctly, or database connections do not have multi-session support enabled, leading to invalid user session and team permission caching. - Phenomenon: Document indexing speed is slow, or inference latency exceeds acceptable thresholds. Cause: The
DB_MAX_LINKconnection pool parameter is not adjusted based on data update frequency, or the server does not reserve sufficient CPU and memory resources to process real-time heat data.
How to confirm the configuration is complete
- Access the configured
FE_DOMAINaddress, confirm that the OneApi page loads normally with no console error prompts. - Log in to the backend using
ROOT_KEYpermissions, create a test team and two test accounts, verify that accounts can join the same team and share uploaded document data. - Upload a converter smelting record document, trigger the indexing process, check if corresponding vector data is generated in the database corresponding to
MONGODB_URI, with no timeout or error logs. - Simulate real-time heat data writing, check if there are data write records on the cache node corresponding to
REDIS_URL, with latency meeting internal enterprise performance requirements.
The values provided are common starting points and should be measured against the reader's own samples.