What Data for This Category Looks Like
Special steel yield rate-related data comes from three main sources: domestic special steel spot trading platforms, futures exchange special steel product contracts, and steel mill ex-factory price ledgers. Data update schedules fall into three categories:
- Spot quotes update same-day data before 10:00 daily
- Futures settlement prices update same-day contract data after 21:00 daily
- Industry statistics update last week's aggregated data every Monday
Single data entries use a structured format, including the following fields: special_steel_variety (special steel variety), production_place (origin), specification (specification model), daily_avg_price (daily average price, unit: yuan/ton), transaction_volume (daily transaction volume, unit: tons), report_date (report date). No redundant nested content is included.
Constraints Imposed on Database and Operations
The large number of special steel varieties and dispersed data sources require the database to support multi-source data aggregation and unified field mapping. Failure to do so will result in inconsistent or disorganized imported fields. Multiple update time nodes require scheduled task scheduling to support multiple cron expressions. This prevents missed or duplicate data updates. Long specification model fields require proper configuration of database character sets and field lengths to avoid data truncation. The requirement for date-partitioned storage optimizes time range query performance. Additional partition rule configuration is needed for this setup. Differences in field naming across multiple data sources require configurable field conversion rules. Without these, some fields will be missing during data import.
Configuration Settings
The following table lists recommended configuration values and their rationales:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MULTI_SOURCE_SYNC_CRON | 0 16 10 * * ?, 0 30 21 * * ? | Matches update timelines for special steel spot quotes (updated before 10:00 daily) and futures settlement prices (updated after 21:00 daily) |
DB_PARTITION_POLICY | RANGE (report_date) | Stores data partitioned by report date to optimize time range query performance |
FIELD_MAPPING_CONFIG | {"spot_price": "daily_avg_price", "trade_vol": "transaction_volume"} | Adapts to field naming differences across data sources and unifies data import formats |
DB_CONNECTION_POOL_SIZE | 20~30 | Matches concurrent request volume for special steel data synchronization, prevents database connection exhaustion |
DATA_VALIDATION_RULE | price > 0 AND transaction_volume >= 0 | Filters abnormal values for special steel prices and transaction volumes to ensure compliant imported data |
VECTOR_DB_TTL_DAYS | 7 | Sets retention period aligned with special steel daily report data, cleans up expired unnecessary historical data |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Each scenario requires individual analysis, and testing against local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Issue: Service fails to start, with
database connection refusederror in logs. Cause: Database connection address is not configured correctly, orDB_CONNECTION_POOL_SIZEexceeds the maximum connection limit of the target database. For version v4.8.10-alpha2, additionally check the format compatibility of connection parameters. - Issue: After modifying vector database data via a native MongoDB client, FastGPT retrieval results do not update synchronously. Cause: FastGPT vector database retrieval relies on built-in index caching. Directly modifying underlying data does not trigger index rebuilding, leading to inconsistent retrieval results and actual stored data.
- Issue:
production_placeorspecificationfields are empty in retrieval results. Cause:FIELD_MAPPING_CONFIGis not configured to complete cross-data source field mapping, so original fields from some data sources are not correctly converted to the unified import format.
How to Verify Successful Configuration
- Execute a database query targeting a specific
report_date, confirm that data storage partitions comply with the configuredDB_PARTITION_POLICYrules, and that query response times meet business requirements. - View execution logs for multi-source synchronization tasks, confirm that tasks complete at preset time nodes, with no data pull failures or import exceptions.
- Enter special steel product keywords in the FastGPT retrieval interface, verify that returned results include the configured unified fields, with no missing fields or abnormal values.
- Connect to the vector database via a native MongoDB client, perform create and delete operations, confirm that FastGPT retrieval results match the changed content synchronously, to verify that vector database configuration is effective.
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.