What this category's data looks like
Data for this category primarily comes from Dalian Commodity Exchange public market APIs, domestic major port spot trading platform APIs, and industry association public reports. Update cadence follows three schedules: futures market data syncs daily after market close, spot quotes update hourly, and industry analysis reports update weekly. Each structured document includes: iron ore category identifier, origin information, dry basis iron content percentage, trading specifications, daily trading price, daily trading volume, and data release timestamp. Units are as follows: trading price is measured in yuan per wet metric ton, trading volume in metric tons, and dry basis iron content percentage in percent.
What constraints do these characteristics impose on model access and configuration
The multi-source update cadence and structured nature of iron ore data create three core constraints for model access and configuration. First, high-frequency hourly spot quote updates require a short-cycle data sync mechanism to prevent marketing content from lagging behind real-time market conditions due to overly long pull intervals. Second, field naming inconsistencies exist across documents from different sources. For example, some documents use "iron grade" instead of "dry basis iron content percentage". Unified field mapping rules must be configured to ensure correct alignment of multi-source data. Third, iron ore marketing content focuses on core business fields such as trading price and iron content. Higher extraction weights must be assigned to these parameters to avoid the model capturing irrelevant information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DATA_SYNC_INTERVAL | 300 seconds | Iron ore spot quotes update hourly. A 300-second sync interval ensures data timeliness while avoiding excessive API calls that trigger rate limits |
maxContext | 800–1200 characters | Iron ore marketing content centers on real-time market conditions and specification parameters. A range of 800–1200 characters covers core trading data and business descriptions, while avoiding excessive context that causes model inference delays |
SIMILARITY_THRESHOLD | 0.75–0.85 | Matching accuracy requirements for core iron ore fields such as iron content and price are high. This threshold filters low-correlation recall results while retaining sufficient valid matching content |
RECALL_TOP_K | Top 10 entries | Marketing content requires a balance between comprehensiveness and readability. 10 recall results cover market and supply and demand data across different dimensions, while avoiding excessive redundant information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Bulk imported iron ore inventory or trading report files are typically large. A 600-second timeout ensures complete parsing of all structured fields |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Monthly industry report PDFs or CSV files typically do not exceed 1000 MB. This setting allows full import of bulk 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. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Scenario: When deploying with docker-compose, logs show
ERROR 1045 (28000): Access denied for user 'root'@'localhost' (using password: YES), but the MySQL container status is normal. Cause: TheMYSQL_HOSTenvironment variable in the FastGPT configuration was not set to the MySQL service container name, andlocalhostwas incorrectly used to reference the container's internal network. - Scenario: When testing a locally deployed model, the interface displays
504 Gateway Timeout, but direct curl calls to the model API respond normally. Cause: The network policy of the FastGPT container does not open communication ports for the model API, or internal network routing rules block inter-container requests. - Scenario: After importing iron ore spot quote documents, core business fields are not correctly extracted in the knowledge base. Cause: No weight ratio for core fields was configured for the
FIELD_WEIGHTparameter, causing the model to prioritize extracting non-core irrelevant information.
How to confirm proper configuration
- Run a manual data sync test, and verify that the number of synced documents matches the number of entries in the source data.
- Initiate a knowledge base recall test based on iron ore market conditions, and check the field completeness and business relevance of recall results.
- Review model call logs to confirm that the
SIMILARITY_THRESHOLDandRECALL_TOP_Kconfigurations are correctly applied. - Simulate a large file import or long context request, and test whether the
PARSE_FILE_TIMEOUT_SECONDSerror is triggered to confirm the timeout configuration is active.
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.