What Data for This Category Looks Like
Data for coal chemical industry intelligent due diligence reports comes primarily from coal industry association monthly capacity bulletins, National Energy Administration coal chemical project approval announcements, public corporate environmental impact assessment documents, and annual operation reports. Data update cadences fall into three categories: monthly (unit operation parameters), irregular (new project approvals), and annual (corporate financial and environmental annual reports). Individual due diligence documents typically include fields such as process parameters, raw coal blend ratios, production capacity scale, and pollutant emission indicators. Units include tons, cubic meters, percentage, tons/year, and others. Document length-related parameters vary widely. Values should be determined based on statistics from internal samples or actual measurements.
How These Characteristics Create Constraints for Deployment and Upgrade
Coal chemical due diligence documents are generally long and have diverse field units, which create constraints on FastGPT parsing and indexing links. Long documents require extended parsing timeout times to avoid mid-process interruptions. Multi-unit fields require custom extraction rules to ensure accurate parameter identification. The update cadences of different data sources vary greatly. During upgrades, the incremental synchronization trigger logic must be adapted to distinguish synchronization cycles for monthly operation data and irregular project data. Additionally, coal chemical data involves compliance information. Sensitive content filtering parameters must be configured during deployment to ensure compliant data usage.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the long-document parsing requirements of coal chemical due diligence documents, avoiding mid-process timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual coal chemical due diligence documents can reach hundreds of MB in size, requiring an increased upload limit |
Recall Count | Top 10 entries | Coal chemical data has numerous and scattered fields. A sufficient number of segments must be recalled to cover key parameters |
Similarity Threshold | 0.75–0.85 | Differentiates between coal chemical industry technical terms and general expressions, preventing low-relevance content from being included |
EMBEDDING_MODEL | qwen3-embedding-8b | Meets the model deployment requirements of FastGPT v4.9.11, and supports semantic understanding of coal chemical professional text |
SYNC_INTERVAL_HOURS | 24 hours | Matches the update cadence of monthly operation data. Adjustments to on-demand triggering can be made as needed |
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. It is recommended to conduct actual measurements on internal samples before finalizing values.
Three Common Mistakes
- A
{"code":500,"message":"URI malformed"}error appears when accessing the frontend after deployment. The cause is an incorrectly configured frontend reverse proxy path for FastGPT v4.9.11, leading to incorrect API request path concatenation. - The frontend page cannot be accessed after container deployment, even though the container status is normal. The cause is failure to open the mapped container port or configure corresponding port rules in the security group.
- The root account password is automatically reset to
123456every 1 day. The cause is failure to disable the default password reset scheduled task built into FastGPT v4.9.11, or failure to correctly configure persistent storage leading to configuration loss.
How to Confirm Configuration is Complete
- Upload a coal chemical due diligence document, and check whether the parsing task status shows completed, with no timeout or parsing failure prompts.
- Call a test interface or enter coal chemical professional keywords in the knowledge base test interface, and check whether the recall results include valid content for corresponding fields.
- View container logs to confirm that configuration items such as
PARSE_FILE_TIMEOUT_SECONDSandEMBEDDING_MODELhave been correctly loaded, with no parameter missing errors. - Upload an updated monthly operation report, test the incremental synchronization function, and check whether index updates are completed automatically.
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.