What Data for This Category Looks Like
Coal chemical investment research data primarily comes from public industry journals, feasibility study reports released by coal deep processing enterprises, environmental impact assessment documents published by ecological environment departments, coal chemical product trading data listed on futures exchanges, and industry operation briefings released by the National Energy Administration. Update frequency varies by data type. Industry briefings update weekly or monthly. Feasibility study reports update as projects progress. Environmental impact assessment documents update at project approval nodes. Trading data updates per trading day. Documents include long-text process descriptions, structured parameter tables, scattered policy clauses. Some documents are plain text transcribed from scanned copies, with messy line breaks and redundant characters. Fields include coal type, gasifier model, processing capacity, raw material consumption, product output, policy document number, release date, and more. There is no unified fixed format.
What Constraints Do These Characteristics Impose on the Context and Token Processing Stage
The characteristics of coal chemical investment research data, including long-text process descriptions, structured parameter tables, and frequently updated trading data, impose multiple constraints on the context and token processing stage. Single feasibility study report documents can reach tens of thousands of characters. Batch recall quickly fills the context window, leading to a sharp increase in token consumption. Structured parameter tables contain multiple sets of related values. Without precise filtering, redundant parameters will occupy additional tokens. Text transcribed from scanned copies contains garbled characters and redundant line breaks. The model recognizes these as valid tokens, further increasing invalid token consumption. Frequently updated real-time trading data requires context recall data sources to have timeliness. Improper cache cycle settings introduce outdated data, while increasing the token calculation load for each recall.
How to Set the Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContextToken | 8000–16000 | Matches the token proportion of single long documents in the coal chemical industry, adapts to the context window upper limit of mainstream quantitative models, and avoids context overflow |
recallChunkSize | 800–1200 characters | Adapts to the typical paragraph length of coal chemical process descriptions and parameter tables, balances semantic completeness and token occupancy |
recallTopK | Top 3–5 entries | Filters redundant non-core parameters in coal chemical data, avoids excessive recall leading to context token overload |
rerankReturnTopN | Top 2–4 entries | Retains coal chemical data most relevant to investment research questions, reduces token consumption of reranking results |
contextCacheTTL | 1–4 hours | Adapts to the update rhythm of coal chemical industry data, balances context timeliness and token calculation cost |
parseChunkOverlap | 50–100 characters | Retains paragraph connection semantics for long coal chemical documents, avoids loss of key related information after chunking |
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
- Phenomenon: The knowledge base returns truncated content, only partial process parameters or policy clauses. Cause: The
maxContextTokenconfiguration is not adjusted. The context window is too small to hold all recalled coal chemical document content. The model cannot output complete results. - Phenomenon: Single-round question answering takes too long, and background logs show token usage far exceeds the preset threshold. Cause: Reasonable
recallTopKandrecallChunkSizeare not set. Excessive redundant non-core coal chemical data is recalled, leading to context token overload. - Phenomenon: After starting the reranker container, the console returns a
401 Unauthorizederror, or the chat interface pops up atoken validation failederror after submitting voice input. Cause: The access token for the reranking model is not correctly obtained and configured, or the token permissions for voice transcription are not correctly configured, leading to authentication failure.
How to Confirm Correct Configuration
- Upload the longest single coal chemical feasibility study report, trigger a question answering session, check the token usage in background logs, confirm it does not exceed the preset
maxContextTokenupper limit. - For typical coal chemical investment research questions, verify the number of recall results, confirm it matches the configured
recallTopKandrerankReturnTopNparameters. - Submit voice input content, confirm the interface shows no
token validation failederror, and the transcribed content fully matches the input voice. - Wait longer than the configured
contextCacheTTLduration, then initiate the same question answering session again, confirm the recalled industry data is the latest version.
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.