What Data for This Category Looks Like
Energy metals-related data primarily comes from public materials of domestic nonferrous metal industry associations, commodity exchange market trends, quarterly reports from mining and smelting enterprises, and public information from third-party supply chain research institutions. Data update frequency falls into two categories: spot trading data is updated daily, with some core varieties supporting real-time market quote pushes; industry supply and demand forecast reports are updated weekly or monthly. Most documents are structured tables or documents with field annotations, including fields such as grade proportion, spot price, total inventory, downstream application proportion, and similar fields. Units are mostly yuan/ton, ten thousand tons, %, tons/year, and similar units.
Constraints Imposed on Deployment and Upgrade by These Characteristics
The high-frequency update and multiple structured field characteristics of energy metals data impose multiple constraints on the deployment and upgrade process. Real-time market data requires configuring high-frequency vector synchronization tasks to ensure knowledge base content stays synchronized with industry dynamics. Structured documents with multiple fields and units require presetting standardized field mapping rules during deployment to avoid mismatches between parsed units and business requirements. Large monthly supply and demand reports require adjusting timeout thresholds for file upload and parsing. For intranet deployment scenarios, industry data source images must be cached in advance to prevent failed data synchronization in offline environments. During version upgrades, complete backups of vector database indexes and MongoDB configurations are required; otherwise, field mapping rules may be lost.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Monthly supply and demand reports for energy metals typically have large file sizes and require long parsing times; default parameters may cause timeout failures |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some large industry research documents may exceed default limits, requiring adaptation to large-volume file upload requirements |
maxContext | 1000–1200 characters | Energy metals data has many fields and rich details; sufficient context must be retained to ensure retrieved content fully covers core indicators |
Recall count | Top 8 results | Energy metals marketing content needs to cover multi-dimensional data; excessive retrievals will cause content redundancy, while insufficient retrievals will fail to cover all business scenarios |
Similarity threshold | 0.75–0.85 | Energy metals industry has many specialized terms with high similarity; adjust the threshold to filter irrelevant content while retaining valid data for different scenarios of the same category |
AUTO_SYNC_INTERVAL | 3600 seconds | Real-time market data requires synchronization once per hour to ensure timely updates of knowledge base content |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: After running the version upgrade command in an intranet environment, a prompt appears indicating that the image cannot be pulled. Cause: The intranet image repository was not configured in advance, or official image packages were not cached, resulting in the inability to obtain upgrade-required images in an offline environment.
- Issue: After uploading an Excel-format energy metals inventory table, the knowledge base returns empty fields or incorrect unit parsing. Cause: No dedicated field mapping rules for energy metals were preset; the default parsing logic cannot recognize specialized units and field names such as grade and tonnage.
- Issue: After upgrading from v4.6.7 to v4.6.8, restarting the service prevents login, and viewing logs returns a database connection failure. Cause: The MongoDB configuration file was modified during the upgrade process, but vector database connection parameters were not updated synchronously, resulting in the service being unable to connect to the database normally.
How to Confirm Proper Configuration
- Upload a test Excel table containing energy metals data, and verify whether the parsed fields match the preset mapping rules.
- Run a manual synchronization task, check the vector database update logs, and confirm that the data synchronization frequency meets preset requirements.
- Simulate the version upgrade process in an intranet environment, back up images and data before performing the upgrade, and verify that the service can restart and log in normally.
- Trigger a large-volume document upload, and verify whether the upload progress and parsing results meet expectations.
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.