What This Category’s Data Looks Like
Data sources for coal chemical due diligence reports include public statistics from coal industry associations, public environmental impact assessment announcements for coal chemical projects, annual reports of listed companies, and public supply chain quotation prices. Update cadences vary: core indicators such as production capacity and energy consumption are updated monthly. Industry policies and project progress are updated quarterly. Annual industry white papers are released each calendar year. Document structures typically include modules such as basic project information, capacity parameters, raw material consumption, pollution discharge indicators, and financial summaries. Most field units use professional metering standards, including 10,000 tons per year, cubic meters per ton of product, and tons of CO₂ per 10,000 tons of production capacity.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Coal chemical due diligence data is scattered across sources and highly specialized. Multi-turn dialogue must verify information consistency across multiple data sources, and prompts must clearly specify the priority of different data sources. Individual report texts are lengthy, so multi-turn dialogue context windows must retain sufficient content to avoid truncating critical professional parameters. Field units are highly specialized, so prompts must mandate use of corresponding units to prevent confusion over metering standards. Data update cycles vary widely, so multi-turn dialogue historical context must automatically filter expired data to ensure referenced information is timely.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Individual coal chemical due diligence report texts are lengthy, so sufficient context must be retained to support multi-turn information verification and avoid truncating critical professional parameters |
recallTopK | Top 6–8 entries | Coal chemical due diligence data has many specialized fields, so enough relevant fragments must be recalled to cover core dimensions such as production capacity, energy consumption, and finance |
similarityThreshold | 0.72–0.78 | Coal chemical professional terms have high distinctiveness. A threshold that is too low will introduce irrelevant industry data, while a threshold that is too high will miss some relevant information |
historyMaxLength | 10–15 turns | Multi-turn due diligence dialogue requires retaining context for key data comparisons; too many turns will increase model inference load |
promptTemplate | "Please base on the provided coal chemical due diligence data, output in the format [field name] (unit: [corresponding unit]), and prioritize using the most recently updated data sources" | Must clearly specify unit requirements for professional fields, unify output formats, and specify data source priority |
historyAutoSave | Enabled | Ensures that response content from multi-turn dialogues is fully retained, avoiding empty responses when reloading conversations |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Specified response content is not retained in multi-turn dialogue history, and automatic responses are empty when reloading conversations. Cause: The
historyAutoSaveparameter is not enabled, or the configuredhistoryExcludeListincludes core keywords of the response content. - Phenomenon: The signature time in workflow prompts is not correctly output as the current system date, with fixed values or empty values appearing. Cause: The platform’s built-in time variable is not correctly bound, or the variable’s effective scope is not declared in the prompt template.
- Phenomenon: Response content from the previous AI dialogue in a workflow is included in the final output of the second dialogue. Cause: The output of the previous node is not excluded in the input configuration of the second dialogue, or the
inputFilterPreviousNodeparameter is not enabled.
How to Verify Correct Configuration
- Upload a standard coal chemical due diligence report, initiate a multi-turn dialogue, and confirm that each round of response content is retained in the historical record.
- Insert the built-in time variable into the workflow prompt, trigger a run, and check that the signature time matches the current system date.
- Configure two connected AI dialogue nodes, trigger a run, and check the final output to confirm only the response content of the second dialogue is included.
- Adjust the
similarityThresholdto 0.7, initiate a query, and confirm all recalled fragments are related to coal chemical professional data.
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.