What the data for this category looks like
Thermal coal investment research data primarily comes from port spot price platforms, public reports from coal transportation and marketing associations, futures exchange delivery data, weekly production and sales reports from upstream and downstream enterprises, and operational data from core transport corridors including the Daqin Railway. Spot price quotes are updated daily, weekly production and sales reports are updated weekly, monthly industry analysis and quarterly supply and demand balance sheets are updated monthly and quarterly respectively. Data documents include standardized price ledgers, transportation timetables, policy documents and forecast reports. Core fields include as-received base lower heating value (unit MJ/kg), total sulfur content (%), port clearance price (yuan/ton), and associated information such as port names and shipping origins.
What constraints these characteristics impose on the deployment and upgrade phase
Multi-source heterogeneous data sources require deploying parsing templates tailored to different formats, to avoid conflicts between parsing rules for spot ledgers, long-cycle reports and policy documents. Differentiated update frequencies require configuring differentiated scheduled synchronization tasks, ensuring high-frequency spot data is updated promptly while low-frequency industry reports do not consume excessive system resources. Fixed formats for professional fields require pre-configuring custom parsing rules, preventing default parsing logic from truncating or confusing specialized terms such as heating value and sulfur content. Correlation analysis of cross-source data also requires configuring association indexes for vector databases during deployment, to guarantee accurate linked queries for production areas, ports and price data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Thermal coal monthly supply and demand reports have a large volume, and parsing time is significantly longer than general documents. Extending the timeout period prevents parsing interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some historical data attachments and large supply and demand balance sheets have a large size. Raising the upload upper limit ensures complete data import |
maxContext | 8000–12000 characters | Full context of professional fields must be retained to avoid truncation of core investment research information such as heating value and sulfur content during splitting |
Recall count | Top 10 entries | Thermal coal investment research requires multi-dimensional data covering spot, transportation, and policy. Increasing the recall volume appropriately covers more analysis dimensions |
Similarity threshold | 0.75–0.85 | High precision is required for professional field matching to avoid mixing irrelevant data and affecting the accuracy of investment research conclusions |
PARSE_DOC_SPLIT_LENGTH | 1500 characters | Professional tables and fields in long reports must be fully retained to avoid disrupting data association during splitting |
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 local samples before finalizing settings.
Three common errors
- Phenomenon: A 403 Forbidden error is prompted when accessing the custom domain name after deployment. Cause: FastGPT’s reverse proxy domain binding is not configured correctly, and external access permissions for the corresponding port are not granted.
- Phenomenon: Professional fields including as-received base lower heating value and total sulfur content return empty values when parsing thermal coal price data. Cause: Custom parsing templates for thermal coal-specific terms are not configured, and default parsing rules cannot recognize industry-specific field formats.
- Phenomenon: After upgrading to v4.9.1, locally deployed models do not appear in the model channel list. Cause: Local model support is not enabled in the
MODEL_LOCAL_ENABLEconfiguration item, or the API address and port of the local model are not filled in correctly.
How to confirm the configuration is correct
- Upload a standard thermal coal monthly supply and demand report, check whether the parsed data fields include industry-specific terms, and whether the field format meets investment research requirements.
- Trigger the preset scheduled synchronization task, verify that data sources with different update frequencies complete synchronization as planned, with no timeout or failure logs.
- Enter core investment research keywords in the knowledge base retrieval interface, check whether the number and relevance of recall results match the preset configuration.
- Access the bound custom domain name, verify that the FastGPT interface loads normally, with no access permission-related errors.
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.