Database and Operations for Special Steel Investment Research Knowledge Base Construction

What data looks like for this category

Special steel investment research data comes from internal production ledgers of steel mills, quality inspection center test reports, public supply and demand data for product categories from industry associations, and custom requirement documents from downstream equipment manufacturing enterprises.

Data updates follow two cadences:

1. Production-related quality inspection and scheduling data updates per furnace batch
2. Industry supply and demand data updates monthly

Each structured document includes furnace batch number, steel grade identifier, chemical composition parameters, mechanical performance parameters, and production process parameters. Field units include megapascals, millimeters, and degrees Celsius.

Unstructured documents are mostly PDF reports with test tables, or Word documents marked with process details.

What constraints these characteristics impose on database and operations workflows

Mixed high-frequency, small-batch production data updated per furnace batch and low-frequency, large-batch industry data updated monthly creates periodic peaks in database read-write load. Configure load balancing and dynamic scaling strategies to address this.

Heterogeneous multi-source data has inconsistent field units. Add a standardized validation step before data ingestion to avoid vector recall bias caused by unit differences.

Unstructured test reports contain complex tables. This requires more granular configuration for document parsing timeouts and segmentation rules.

Downstream custom requirement documents have inconsistent formats. Reserve flexible field mapping configurations. Establish a validation mechanism for multi-data source synchronization to prevent dirty data from entering the knowledge base.

How to set configurations

The parameter values provided on this page are common recommended starting points for configuration. Actual values vary based on material form, data volume, and business rules. Individual analysis is required for specific cases. Test on samples before finalizing settings.

Three common configuration errors

• Symptom: Database connection failure. Logs display connection refused or authentication failed. Cause: Incorrect configuration of username and password in the database connection string, or unexposed database port during container deployment.
• Symptom: The number of recall results does not match the configured RECALL_TOP_N value. Actual returned entries are fewer than expected. Cause: Unadjusted database read concurrency. High-frequency queries exhaust connections, so some requests fail to retrieve vector data.
• Symptom: Chemical composition parameter fields are empty after parsing uploaded quality inspection PDF reports. Cause: Complex table parsing configuration is disabled, or segmentation length is set too short, leading to truncated cross-page table content.

How to verify correct configuration

• Run an upload and parsing task for a single large quality inspection PDF. Check parsing logs to confirm elapsed time stays below the configured PARSE_FILE_TIMEOUT_SECONDS. Verify that parsed fields including chemical composition and mechanical performance are complete.
• Initiate multiple batches of identical document queries. Monitor database connection count and load on the monitoring panel. Confirm no connection exhaustion or excessive load peaks occur.
• Adjust the RECALL_TOP_N configuration, then run a precise steel grade query. Confirm the number of returned results matches the configured value.
• Connect to the configured vector database using an external database management tool. Confirm permission settings allow non-root users to perform basic create, delete, and modify operations.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.