What this category of data looks like
Coal chemical due diligence data mainly comes from public statistics of coal industry associations, production ledgers of coal chemical enterprises, emission monitoring data from ecological environment departments, and market trends from commodity trading platforms. Update frequencies vary across data types: raw material purchase ledgers are updated every ten days, unit operation parameters are synchronized in real time, and product inventory and industrial chain quotes are updated after daily market close. A single due diligence document includes five core modules: raw material supply ledger, unit operation parameters, product production capacity and inventory, environmental emission indicators, and upstream and downstream industrial chain market trends. Fields include purchase volume (unit: ton), operation duration (unit: hour), product output (unit: ton), emission concentration (unit: milligrams per cubic meter), and others. There is no unified fixed format template, and the field order and supplementary notes vary across submissions from different enterprises.
Constraints imposed by these characteristics on model access and configuration
The multi-source heterogeneous nature of coal chemical due diligence data requires configuring multi-data source adaptation rules to adapt to original data files in different formats. Differences in update frequencies across modules require setting independent synchronization trigger periods for each module, to avoid resource occupation from high-frequency polling or data lag from low-frequency synchronization. Non-uniform document structures and field differences require configuring custom field mapping rules to allow manual matching of homonymous but different-meaning fields from different sources. Long paragraphs of working condition descriptions and statistical tables combining multiple fields require adjusting context window adaptation parameters to avoid exceeding the model's processing limit due to overly long single paragraphs. In addition, inconsistent field units require configuring preset unit conversion rules to unify the benchmark units for data statistics.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Adapts to long paragraphs of working condition descriptions and multi-field statistical content in coal chemical due diligence reports, avoiding context overflow |
chunkSize | 800–1200 characters | Avoids cross-module truncation when splitting professional documents, while controlling the model processing load for single-segment text |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Coal chemical due diligence documents contain multi-dimensional tabular data, which takes longer to parse; prevents mid-parsing timeout interruptions |
similarityThreshold | 0.75–0.85 | There are many professional terms in the coal chemical industry, requiring a high matching threshold to filter irrelevant recall results and ensure data relevance |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single due diligence report may contain multiple attached ledgers and monitoring reports, adapting to overall upload volume requirements |
rerankTopN | Top 8 entries | Coal chemical data has multi-dimensional associations, retaining enough recall results for reranking and filtering to avoid missing key information |
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, and it is recommended to conduct tests on your own samples before finalizing settings.
Three common mistakes
- Inconsistent model return results between the conversation interface and independent workspace. The phenomenon is deviations in analysis logic for the same coal chemical due diligence data between the two interfaces. The cause is failure to synchronize the
modelNameandtemperatureparameters in global model configuration and single-application configuration. - Unable to access the model via a third-party API link after local deployment. The phenomenon is a
502 Bad Gatewayerror returned during calls. The cause is incorrect configuration of theAPI_BASE_URLparameter, or no network connectivity established between the deployment network and the third-party API service network. - Parsing failure after uploading a due diligence document. The phenomenon is "file parsing timeout" displayed on the interface. The cause is failure to adjust
PARSE_FILE_TIMEOUT_SECONDSto a value adapted to coal chemical multi-table documents; the default configuration duration is insufficient to complete full data parsing.
How to confirm successful configuration
- Upload a standard coal chemical due diligence document, check if the segmented text generated after parsing covers the core modules with no obvious cross-module truncation, and adjust the
chunkSizevalue based on the segmentation effect. - Trigger a full data source synchronization, check the update time and data volume of each module in the synchronization log, and confirm that the synchronization periods of different modules match the preset configuration.
- Initiate a query targeting coal chemical due diligence data, check if the recalled fields and units in the returned results are unified, and adjust the
similarityThresholdvalue based on the matching results. - Test model calls in different application scenarios, confirm that the analysis logic returned by the conversation interface and workspace is consistent, and verify the synchronization of global and application-level model configurations.
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.