What This Category’s Data Looks Like
Coal chemical industry investment research data sources include public industry research reports, coal chemical process standard documents, upstream and downstream supply and demand ledgers, coal quality test reports, and regional production capacity statistics. Update cadence varies by document type. Public research reports are released quarterly. Process parameter documents are updated irregularly alongside standard revisions. Real-time price data updates every hour. Document structures include long-text feasibility study analyses, structured process parameter tables, and quantitative supply and demand data sheets. Fields cover coal grade, ash content percentage, reaction temperature, and unit production capacity. Units include tons, degrees Celsius, megapascals, cubic meters, and more.
Constraints Imposed on Conversation Logging and Auditing
The mixed structure of long text and structured documents requires logs to record details of both unstructured fragment retrieval and structured field matching. Real-time updated price data requires logs to include data version numbers and call timestamps, to prevent mixing data across versions. The precision of coal chemical process parameters requires audit trails to bind specific called fields and corresponding document revision versions. Industry regulatory requirements mandate retaining complete retrieval-retrieval-generation full-chain logs, while supporting filtering log entries by access permission identifiers to ensure compliant data usage.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
LOG_RECORD_SCOPE | full_retrieval+generation | Coal chemical investment research scenarios require coverage of the full retrieval, document parsing, and reply generation chain. This configuration retains complete audit evidence |
EMBEDDING_BATCH_SIZE | 8-12 | Coal chemical documents include long-text research reports and dense structured tables. Excessive batch sizes trigger embedding rate limits. This range balances processing efficiency and rate limits |
AUDIT_LOG_VERSION_BIND | enabled | Logs must be linked to corresponding document revision versions to prevent audit traceability failure from data updates |
PERMISSION_TAG_FIELD | enterprise_id | Supports filtering log entries by enterprise identifier, matching permission isolation requirements in coal chemical scenarios |
WORKFLOW_LOG_TIMEOUT | 600 seconds | Workflows for long-text parsing and multi-document retrieval have long execution cycles. This configuration prevents premature log truncation |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Vectorization task fails, with logs showing embedding rate limit exceeded. Cause: The
EMBEDDING_BATCH_SIZEandEMBEDDING_WORKER_THREADSparameters are not adjusted. Excessive batch size or thread count triggers rate limits. - Phenomenon: In FastGPT version v4.9.0, workflow execution pops gpt-4o-mini call error logs, but no actual API call is triggered. Cause: Model call parameters configured in workflow nodes are not correctly bound to context. Pre-validation failure generates invalid error logs.
- Phenomenon: Knowledge base question-answer pair extraction task gets stuck in training, with no related records in call logs. Cause: The
PARSE_FILE_LOGGINGconfiguration is not enabled. Task execution logs are not collected, and long-text parsing times out without completing the task.
How to Verify Proper Configuration
- Retrieval type logs in the log management module can be filtered, and records can be confirmed to include both structured parameter matching and unstructured document fragment retrieval entries.
- A retrieval request with a preset enterprise identifier can be sent, and audit logs can be checked for the corresponding permission tag field.
- A single long-text coal chemical research report can be submitted for parsing, and logs can be checked to confirm complete parsing duration and version information are recorded.
- A workflow task can be triggered, and logs can be confirmed to retain complete retrieval-retrieval-generation chain records.
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.