What this category of data looks like
Coal chemical industry data comes from public reports released by the Coal Chemical Branch of the China Coal Industry Association, regular financial reports of publicly traded coal chemical enterprises, project environmental impact assessment documents, production operation logs, and commodity futures and spot trading data. Update rhythms vary widely: production operation data updates daily, industry research reports update weekly or monthly, and annual capacity planning reports update quarterly or annually. Document types include feasibility study reports spanning dozens of pages, structured capacity and energy consumption tables, and short industry news texts. Fields include professional units such as ten thousand tons per year of production capacity, kilogram standard coal per ton of product coal consumption, and yuan per ton of spot price. Some documents contain cross-disciplinary term combinations.
What constraints these characteristics impose on model access and configuration
The multi-type, multi-update-frequency and professional field characteristics of coal chemical data create clear constraints for model access and configuration. A high share of long documents requires avoiding overly fragmented segmentation that breaks professional semantics, while adapting to the model's context window limits. A high share of structured tables requires enabling dedicated table parsing functions to ensure accurate extraction of fields and units. Wide variation in data source update frequencies requires supporting configuration of different synchronization cycles per data source type. Many high-similarity professional terms require adjusting recall thresholds to balance precision and coverage.
How to Set Configuration Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single-segment professional content in coal chemical feasibility study reports is lengthy. This avoids semantic fragmentation while adapting to mainstream model context windows |
PARSE_TABLE_ENABLE | Enabled | Coal chemical data contains large volumes of structured capacity and energy consumption tables, requiring accurate extraction of fields and unit information |
RECALL_TOP_N | Top 8–12 results | Investment research decisions require multi-dimensional data support, balancing recall coverage and inference efficiency |
SIMILARITY_THRESHOLD | 0.75–0.85 | Coal chemical professional terms have high similarity. A reasonable threshold must be set to avoid false recalls or missed key information |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single large feasibility study reports can reach hundreds of pages, requiring support for large-file batch uploads |
AUTO_SYNC_INTERVAL | Configured by data source type | Production data is synchronized daily, industry research reports weekly, and annual reports quarterly |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Configuration Errors
- Issue: Knowledge base capacity calculation results have significant deviations, making storage and call costs impossible to estimate accurately. Cause: The
DOCUMENT_ESTIMATE_UNITparameter is not configured, and the unit conversion rule is not set based on the average character count of coal chemical documents. - Issue: After attempting to connect the MemoRAG component, the system prompts adaptation errors or fails to load. Cause: The compatibility requirements between the current FastGPT version and MemoRAG are not confirmed, and the access path parameters for
CUSTOM_RETRIEVERare not correctly configured. - Issue: Streaming output is not triggered for short-text queries, and full results are returned directly. Cause: The
STREAMING_ENABLEparameter is not enabled, or themaxContextvalue exceeds the model's streaming inference threshold.
How to Confirm Correct Configuration
- Upload a typical coal chemical feasibility study report, review the parsed text segmentation and table extraction results, and confirm the segmentation logic matches the preset configuration.
- Submit a professional investment research query, verify that the number of recalled documents matches the value set for
RECALL_TOP_N, and that the similarity scores meet the expected threshold. - Submit a short-text query, observe whether the output is returned in streaming format, and confirm the streaming output function is working properly.
- View the automatic synchronization logs to confirm that different data sources perform update operations according to the preset cycle.
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.