What the data for this category looks like
Data sources include public monitoring data from coal industry associations, production ledgers of coal chemical enterprises, public quotes from futures markets, and environmental compliance test reports. Update cadences are as follows: real-time operating data is updated minute-by-minute, industry supply and demand reports are updated monthly, and enterprise annual reports are updated annually. Document structures include structured process parameter tables, industrial chain supply and demand balance ledgers, price trend charts, and original compliance test records. Fields include coal ash content, coke oven operating temperature, product yield, and emission indicators. Their respective units are mass fraction, degrees Celsius, proportional value, and concentration value.
How These Data Characteristics Impact Multi-turn Dialogue and Prompt Engineering
Minute-by-minute updates of real-time operating data require that the context retention duration for multi-turn dialogue not be too long. Otherwise, operating parameter deviations may occur. There are many professional fields in structured ledgers with inconsistent units. Prompts must clearly specify the value range and units of fields to avoid AI confusion between different parameter types. Data with different update frequencies requires classified recall. In multi-turn dialogue, the call priority of historical reports and real-time data must be distinguished to prevent the use of outdated data. The sensitive nature of compliance test records requires prompts to limit the retrieval scope, preventing leakage of unpublished internal enterprise data.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Coal chemical data mostly consists of long documents and multi-field ledgers. Sufficient context must be retained to associate professional terms across multiple rounds of questions |
RECALL_TOP_N | Top 6–8 entries | There are many professional fields in the coal chemical industry. Excessive recall will lead to context overload and affect the accuracy of AI responses |
UPLOAD_FILE_MAX_SIZE | 500 MB | Large ledger and research report files from coal chemical enterprises are generally large in size, adapting to conventional upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large structured documents takes a long time, avoiding parsing failure due to timeout |
PROMPT_TEMPLATE | Fixed professional term restricted format | Standardize the output specifications of coal chemical professional fields to avoid AI confusion between parameters with different units |
SIMILARITY_THRESHOLD | 0.75–0.85 | Distinguish the matching degree between coal chemical professional terms and general vocabulary, filtering low-correlation retrieval results |
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 specific analysis. It is recommended to test on your own samples before finalizing values.
Three Common Misconfigurations
- Phenomenon: After deploying via container image packaging, uploaded coal chemical structured ledger files cannot be parsed, and no error prompt is displayed on the interface. Cause: The exclusive dependency image for file parsing was not configured during deployment, so the coal chemical-specific Excel format ledger parsing module was not loaded.
- Phenomenon: After linking variables in the configuration file, null values are returned when calling retrieval. Cause: The retrieval scope of the corresponding file was not enabled in the knowledge base permission settings, and the variable was not correctly bound to the specified data source.
- Phenomenon: After adding a judge node and AI dialogue node in the advanced orchestration of version 4.6.9, the AI cannot obtain the complete initial user question content. Cause: The output node of the judge was not connected to the context input port of the AI dialogue, so the initial question was not passed to the AI dialogue stage.
How to Verify Successful Configuration
- Upload a coal chemical enterprise production ledger file, check whether the extracted fields after parsing include the preset professional fields, and whether the field units conform to the preset specifications.
- Initiate multi-turn queries: first ask about coal material prices for a specific time period, then ask about coke oven yield for the same time period. Check whether the AI can associate the previous time period to generate a response.
- After linking variables in the configuration file, call the retrieval interface. Check whether the returned results include the bound target document content, with no null values returned.
- Add a judge node and AI dialogue node in advanced orchestration, initiate a test query. Check whether the AI can obtain the complete initial user question content.
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.