What the data for this category looks like
Data for thermal coal financing daily reports comes from public spot data of domestic coal trading centers, financing ledger synchronization from partner financial institutions, and port cargo right registration systems. The update schedule follows: full financing business data from the previous day is updated every early morning, and one-off adjustment information for unusual transactions is added at midday. The data uses structured JSON format, with each line corresponding to a single day's financing business. It includes fixed fields such as report date, supply origin, transaction benchmark price, single financing amount, financing period, and fund provider type. The unit for benchmark price is yuan/ton, and the unit for financing amount is ten thousand yuan.
What constraints these characteristics impose on deployment and upgrade
The above data characteristics impose multiple constraints on the deployment and upgrade process:
- The daily full + midday incremental update schedule requires configuring two scheduled sync tasks during deployment, to avoid occupying system resources during peak daytime business hours.
- The category-specific field structure requires enabling custom field mapping during knowledge base parsing, to prevent core business dimensions from being lost via generic parsing rules.
- Dependence on third-party system data sources requires configuring stable API retries and whitelist mechanisms to ensure sync success rates.
- During upgrades, retain existing sync and parsing configurations to avoid breaking already adapted business logic due to version updates.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SYNC_CRON | 0 1 * * * | Matches the schedule for updating full previous day data in early morning, avoids occupying peak daytime business resources |
KNOWLEDGE_BASE_SYNC_MODE | incremental | Adapts to midday added real-time unusual transaction data, reduces storage and computing overhead of full sync |
MAX_RETRY_TIMES | 3 | Addresses temporary fluctuations in third-party data interfaces, prevents sync interruption from a single failed pull |
PARSE_CUSTOM_FIELDS | ["report_date", "origin", "base_price", "finance_amount", "finance_period"] | Matches the dedicated fields of thermal coal financing daily reports, ensures parsed data structure meets business requirements |
MODEL_LOAD_BALANCE_STRATEGY | round_robin | Implements load balancing across multiple model channels, adapts to peak pressure of large model calls |
DOCKER_CONTAINER_RESTART_POLICY | unless-stopped | Prevents dependent services such as PG containers from exiting automatically due to occasional errors, ensures stable operation of sync tasks |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Database connection failure error appears several hours after local Docker deployment, and recovery requires deleting the PG container and redeploying. Cause: PG data volume persistence was not configured; version 4.9.9 does not enable storage mounting by default. Sync data accumulated during operation fills the container's local storage, triggering a storage overflow error.
- Symptom: After upgrading the version, the load balancing configuration for existing model channels becomes invalid, and traffic concentration errors occur during large model calls. Cause: The old version configuration file was overwritten directly, the custom settings for
MODEL_LOAD_BALANCE_STRATEGYwere not retained, and the configuration structure was not upgraded step-by-step according to the version instructions. Some old version parameters are not compatible with the new version logic. - Symptom: Some core fields are missing from synced financing daily report data, making it unavailable for subsequent business analysis. Cause: Custom field mapping for
PARSE_CUSTOM_FIELDSwas not configured, and generic parsing rules filtered out dedicated business fields for thermal coal financing, leading to loss of critical data.
How to confirm the configuration is correct
- View FastGPT sync task logs, confirm that both daily scheduled sync and midday incremental sync tasks execute according to the configured
SYNC_CRON, with no failed records. - Enter the model management interface, confirm that the
MODEL_LOAD_BALANCE_STRATEGYconfiguration is enabled, and check multi-model channel call logs to confirm traffic allocation complies with configuration rules. - Manually trigger an incremental sync task, check that the parsed data structure includes the configured custom fields, with no field loss or incorrect mapping.
- Check Docker dependent containers' storage mounting and restart policies, confirm there are no storage overflow or unexpected exit issues during service operation.
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.