What data for this category looks like
Ledger data for non-ferrous metal smelting originates from Manufacturing Execution System (MES), Enterprise Resource Planning system (ERP), laboratory testing system, and energy metering system. Data updates follow three schedules: production process ledgers update per shift, batch test data updates per heat or per batch, and energy consumption data updates hourly. Each ledger document includes fixed fields such as heat number, metal grade value, unit energy consumption, output volume, test time, and operator identification number. Supported units include tons and tons of standard coal per ton of metal. Documents typically take the form of structured tables or formatted text, with each document ranging from hundreds to thousands of characters in length.
Constraints imposed on deployment and upgrade
Multi-source heterogeneous data sources require configuring multiple compliant storage connections during deployment to interface with databases from MES, ERP, and testing systems respectively. High-frequency data updates require adjusting incremental synchronization trigger rules during upgrades to match shift, per-batch, and hourly data refresh cycles. Fixed fields and units require aligning with the enterprise’s internal database table structure during deployment to ensure correct mapping and extraction of core data. Longer document lengths require adjusting concurrent connection count configurations to avoid loading timeouts during multi-source data synchronization.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:pass123@192.168.1.100:27017/smelting_db | Connects to storage repositories for production process ledgers and test logs, matches the account, address, and database name of the enterprise's existing MongoDB deployment |
PG_URL | postgresql://erp_user:erp_pass@192.168.1.101:5432/erp_db | Connects to ERP system production plan and material consumption data, uses standard PostgreSQL connection format |
REDIS_URL | redis://default:redis_pass@192.168.1.102:6379/0 | Caches real-time test data and system sessions, supports fast reading of high-frequency data |
DB_MAX_LINK | 15–25 connections | Limits concurrent database connection count to avoid connection overload during multi-source data synchronization |
STORAGE_S3_ENDPOINT | http://192.168.1.103:9000 | Connects to the enterprise's internal object storage for storing batch-uploaded ledger scan files and report attachments |
FE_DOMAIN | https://smelting-docs.example.com | Binds the access domain name for the enterprise's internal knowledge base, ensures correct internal user permissions and access paths |
Three Common Configuration Errors
- Symptom: Containers restart continuously after running docker-compose up. Database authentication failure is displayed in logs. Cause: Account, password, and database name in
MONGODB_URIorPG_URLare not filled correctly, and do not match the actual permission configuration of the ledger data storage repository. - Symptom: Insufficient number of ledger documents retrieved by the knowledge base, making it impossible to generate complete reports. Cause:
DB_MAX_LINKis set too low, which limits the concurrent query count for multi-source data, resulting in partial data sources not being read. - Symptom: Uploaded ledger documents fail to correctly parse core fields, resulting in missing key information in generated reports. Cause: The ERP database table structure corresponding to
PG_URLis not aligned, and fixed fields for production plans and material consumption are not mapped during configuration.
How to Verify Correct Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Run the docker-compose ps command to confirm all service containers are running with no abnormal exit prompts.
- Enter the data source management page in the FastGPT backend, test the storage repository connections corresponding to
MONGODB_URIandPG_URLrespectively, and confirm successful connections with no permission errors. - Upload a standard non-ferrous metal smelting ledger document, run a parsing test, and confirm that core fields such as heat number and metal grade value are correctly extracted.
- Trigger a ledger summary and report generation task, check the system backend logs, and confirm no database connection errors or data synchronization exceptions.
The values provided 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.