What this category’s data looks like
Thermal coal financial report data originates from domestic coal industry associations, futures exchange spot price platforms, and public annual reports of listed coal enterprises. Data updates follow three schedules: daily spot prices, weekly supply and demand inventory data, and quarterly and annual reports updated per enterprise disclosure cycles. Document structures include core price indicators, regional supply and demand balance sheets, and transportation and logistics data. Most fields use units of yuan/ton, ten thousand tons, or days. Some reports include special statistical items such as long-term contract fulfillment rate and import volume proportion.
Constraints Imposed on Deployment and Upgrade
The multi-source, high-frequency nature of thermal coal financial report data requires distinguishing incremental and full synchronization logic when configuring scheduled pull tasks during deployment. This prevents excessive server resource usage from full pull operations. Large format differences across multiple data sources require presetting parsing templates adapted to each platform during deployment. Retain old templates during upgrades to maintain compatibility with historical data. Quarterly annual report data has large volume, so vector database sharding thresholds must align with single document lengths. Upgrades must avoid index reconstruction timeouts. Inconsistent field units require configuring unified conversion rules during deployment. During upgrades, verify that conversion logic is not overwritten.
Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single thermal coal quarterly financial report document can reach tens of thousands of characters. Timeout values must cover full parsing and format conversion processes |
UPLOAD_FILE_MAX_SIZE | 50 MB | Single annual thermal coal financial report PDF or Excel file max size is approximately 40-50 MB. This setting covers full document upload requirements |
CRON_SYNC_INTERVAL | 0 1 * * * | Daily thermal coal spot data updates completed before dawn each day. Synchronization tasks must run after updates |
VECTOR_STORE_SHARD_SIZE | 2000 items/shard | Thermal coal financial reports involve multi-dimensional data sharding. Single shard data volume matches single-batch vector computing resources to avoid index reconstruction timeouts during upgrades |
RECALL_TOP_K | Top 10 items | Financial report analysis requires recalling enough associated documents to support multi-dimensional analysis, avoiding omission of critical data |
SIMILARITY_THRESHOLD | 0.75-0.85 | Filter low-correlation non-thermal coal category data, ensuring recall results focus on target category financial report information |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- After running
docker pull fastgpt/fastgpt:v4.8.10and starting the container, the displayed version is 4.1.16. Cause: Failed to correctly specify the version tag when pulling the image, and pulled an old image from another branch by mistake. - After configuring DingTalk access, the application release stage prompts "Receiving message address verification failed". Cause: The deployed service does not have a publicly accessible callback address configured, or a firewall blocks external requests.
- When attempting to update the version after offline deployment, an image pull failure error occurs. Cause: Did not pre-download the corresponding version of the image and dependency packages, so the offline environment cannot access the official image repository.
How to Verify Proper Configuration
- Manually upload a test thermal coal daily financial report document, and check if the parsed data fields include core statistical indicators for the target category.
- Run a scheduled synchronization task once, and check if the number of newly added documents in the vector database matches the data volume of the synchronization source.
- Submit a financial report analysis request, and check if the correlation of recall results meets the preset range requirements.
- View container runtime logs, confirm that parsing tasks do not trigger timeout errors, and that synchronization tasks execute as scheduled.
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.