What the data for this category looks like
Data sources linked to iron ore marketing include domestic port spot databases, import trader ledgers, futures exchange market data APIs, and steel mill procurement reports. Update rhythms fall into three categories: spot prices are updated multiple times per trading day, port inventory data is updated daily, futures market data is pushed in real time, and procurement ledgers are updated monthly.
The document structure follows a single marketing content linked to multi-dimensional datasets, including origin identifier, total iron content metric, port stock volume, same-day spot quotation, and 7-day price fluctuation curve data. Field and unit correspondences are as follows: origin is a text identifier, total iron content is measured in mass fraction, port stock volume uses ten thousand tons as the unit, spot quotation uses yuan per wet ton as the unit, and fluctuation curve data is time-series numerical values.
What constraints these characteristics impose on deployment and upgrade
Iron ore marketing data includes three categories: real-time market data, static ledgers, and time-series fluctuation data. During deployment, configure low-latency vector indexes for real-time market data to avoid recall delays that impact marketing content timeliness. Pre-configure field mapping rules to address unit differences across multiple data sources; otherwise, parsed data cannot be used for precise matching. Adjust the file parsing timeout threshold when importing monthly bulk procurement ledgers to accommodate large ledger files.
During upgrades, ensure compatibility with newly added port data APIs to prevent marketing content association failures from data source changes. Additionally, configure a reasonable synchronization interval for high-frequency real-time market data updates to avoid excessive system resource consumption.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Iron ore monthly procurement ledger files have large volume, require adaptation for long-duration parsing processes |
VECTOR_SEARCH_TOP_K | 10–15 results | Marketing content requires matching precise origin and grade data; excessive recall increases re-ranking burden |
EMBEDDING_MODEL_MAX_LENGTH | 8192 characters | Time-series text for iron ore price fluctuation curves has long length, requires adaptation for long-text embedding |
DATA_SYNC_CRON | 0 0 */1 * * * | Spot prices require hourly updates to match marketing content timeliness |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Monthly port inventory bulk report files have large volume, require relaxed upload limits |
RERANKER_TOP_N | 5 results | Marketing content focuses on core 3-5 iron ore category parameters, avoids redundant results |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Sustained high disk read/write activity occurs after Docker deployment. Monthly read/write volume exceeds 400 TB, leading to premature storage medium failure. Cause: No vector database caching strategy configured, causing each recall to directly read disk-based vector indexes and consume excessive disk resources.
- Symptom:
pgvector connection failederror occurs when starting the vector database container, interrupting the deployment process. Cause: Incorrect mixing of object storage database and pgvector configuration items, failing to point vector database metadata storage to the correct database type. - Symptom: Total iron content fields are empty after importing iron ore ledger files. Cause: No pre-configured mapping rules for numeric fields, parsing mass fraction data as plain text and resulting in field recognition failure.
How to confirm correct configuration
- Run a bulk ledger import test, verify that the number of parsed fields matches the original file, confirm that parsing timeout and upload size configurations take effect.
- Submit a vector recall request, verify that the number of returned results falls within the range specified by the recall count configuration, confirm that the vector database index configuration is correct.
- Check the data synchronization logs, confirm that the scheduled synchronization task executes as expected with no synchronization delay errors.
- View the resource monitoring panel after starting the container, confirm that disk read/write rates are within normal ranges, with no sustained high load conditions.
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.