What the data for this category looks like
Steel smelting ledger data comes from process control systems, manufacturing execution systems (MES), and quality inspection laboratory systems across blast furnace, converter, continuous caster, and steel rolling processes. Data update rhythms fall into two categories: real-time trigger via process nodes and scheduled batch synchronization. A single ledger document includes fields such as process number, material batch number, process parameters, quality inspection results, and energy consumption data. Some fields use industry-specific units: molten iron temperature uses ℃, furnace age uses days, and steel tonnage energy consumption uses kgce/t. Documents are stored as a combination of structured tables and text annotations, and the text length of a complete single ledger varies widely.
What constraints these characteristics impose on deployment and upgrade
Multi-source heterogeneous data sources require docking rules for multiple database types to be configured during deployment, to avoid format incompatibility issues during synchronization. Different update rhythms require flexible synchronization trigger rules. These rules must support both incremental synchronization of real-time process parameters and full archive import of historical ledgers. The dedicated units and structured format of ledger fields require strict field mapping matching during knowledge base import. Field index rules cannot be adjusted arbitrarily during upgrades, otherwise existing generated reports will fail to read corresponding data normally. In addition, the archive data volume of steel smelting ledgers is large. Sufficient storage and computing resources must be reserved during deployment to avoid data loading timeouts during upgrades.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
DB_MAX_LINK | 10–20 connections | The concurrent volume of steel smelting ledger synchronization tasks is high. Limiting the number of database connections prevents database resource exhaustion in production environments |
MONGODB_URI | Enter the standard connection address of the enterprise’s internal MES archive database | Historical ledger data is stored long-term in MongoDB. Direct docking with the archive source is required to import complete historical data |
PG_URL | Enter the PostgreSQL connection address for real-time process data | Real-time process parameters and quality inspection data are stored in PostgreSQL. Synchronous updates to the knowledge base are required to ensure the timeliness of report data |
REDIS_URL | Configure the intranet connection address of the local Redis cluster | Used to cache intermediate results of ledger queries, reducing the pressure of repeated requests on production databases |
STORAGE_S3_ENDPOINT | Enter the access endpoint of the enterprise’s internal object storage | Used to store PDF reports generated from ledgers and raw data attachments, ensuring reports can be downloaded and traced normally |
Three Common Misconfigurations
- Issue: After upgrading FastGPT v4.17.0, a 500 status code appears when accessing the knowledge base page, and the console shows a database connection timeout. Cause: The
DB_MAX_LINKparameter was not adjusted to adapt to the high-concurrency synchronization requests of steel smelting ledgers, resulting in database connection pool exhaustion. - Issue: When calling the model to generate a ledger report, the model stream response is empty with no valid output. Cause: The deployed large language model was not correctly connected during the model configuration phase, resulting in failure to properly parse ledger data and generate reports.
- Issue: After restarting the Docker container on an Ubuntu system, the ledger synchronization task reports a permission denied error and cannot read the local data directory. Cause: The correct permissions for the container mount directory were not configured during deployment. The default permission configuration of the container overwrote the initial settings after restart.
How to Verify a Complete Configuration
- Run the built-in database connection test tool to verify the connection status of
MONGODB_URIandPG_URL, and confirm that basic ledger fields and their corresponding units can be read. - Trigger a manual synchronization task to check whether the knowledge base entries after synchronization include dedicated fields for steel smelting ledgers, such as process number and furnace age.
- After restarting the FastGPT service, check the background logs to confirm that there are no failed connection prompts for
REDIS_URLand that the caching function operates normally. - Generate a simulated ledger report to check whether the storage path of the report attachments matches the configuration of
STORAGE_S3_ENDPOINTand that they can be accessed and downloaded normally.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.