What this category of data looks like
Coal chemical investment research data mainly comes from public reports of coal industry associations, environmental impact assessment documents of coal chemical enterprises, production process manuals, futures market data, and environmental emission monitoring data. Update cycles include weekly industry supply and demand weekly reports, monthly production capacity and energy consumption statistics, and irregularly released process upgrade documents. Document structures include structured parameter tables, process flow diagram descriptions, and compliance indicator lists. Most fields carry professional units such as "gasification pressure (MPa)", "coal consumption (t/ton methanol)", and "emission concentration (mg/m³)". Some long documents can reach tens of thousands of characters.
What constraints do these characteristics impose on knowledge base retrieval and recall?
The multi-source heterogeneous nature of coal chemical data requires retrieval systems to adapt to both structured parameters and unstructured text, and avoid splitting critical parameter groups. Frequently updated market data requires incremental synchronization, otherwise data lag may occur. The strong semantic relevance of professional terms requires that context associations be retained during segmentation, to avoid parameter logic breaks caused by excessive segmentation. Data sources with different update frequencies need corresponding differentiated recall weights, to ensure that the latest compliance and market data is displayed first.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Coal chemical documents contain long parameter tables and process descriptions. This segmentation length preserves complete parameter groups and contextual logic |
chunk_overlap | 150–200 characters | Connects adjacent segments of long documents, avoids splitting cross-paragraph process parameter associations |
recall_top_k | Top 10–15 results | Coal chemical investment research requires cross-verification of multi-dimensional parameters. This range covers core relevant data while controlling retrieval load |
rerank_top_k | Top 3–5 results | Focuses on highly relevant precise data, aligns with the information accuracy requirements of investment research decisions |
similarity_threshold | Calibrated via actual testing | Coal chemical professional terms have high semantic similarity. Thresholds need to be adjusted based on business scenarios |
parse_table_mode | structured_table | Preserves the complete structure of structured parameter tables, avoids retrieval failure caused by splitting professional parameter groups |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Duplicate process parameter fragments appear in retrieval results. Background logs show that the segment overlap rate exceeds the preset range. Cause:
chunk_overlapis set to more than 250 characters, causing excessive overlap between adjacent segments and repeated recall of the same segment of data. - Phenomenon: Rerank test returns
false, and the interface returns500 Internal Server Error. Cause: The TEI service deployed on a cloud computing power server has its port not opened to the FastGPT access network segment, or the model version is incompatible with the reranking library depended on by FastGPT. - Phenomenon: Core compliance indicators cannot be retrieved after uploading long documents. Cause:
parse_table_modeis not enabled, and structured parameter tables are split into scattered text, making it impossible to match professional search terms.
How to confirm correct configuration
- Upload a single coal chemical process manual, check the parsed segment list in the background, confirm that the segment length falls within the preset
chunk_sizerange. - Enter professional search terms such as "gasifier operating pressure", check whether the retrieved result segments contain complete parameter groups or scattered numerical fragments.
- Deploy the reranking service, perform a test search, check whether the reranked results are sorted according to professional relevance logic or randomly arranged.
- Check the incremental update logs of the knowledge base, confirm that data sources with different update frequencies are automatically synchronized according to preset rules.
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.