What the data for this category looks like
Thermal coal marketing-related data comes primarily from quality inspection reports of domestic major coal-producing enterprises, spot transaction ledgers at coastal ports, and supply and demand briefings from industry self-regulatory organizations. This data supports the creation of marketing quotes and industry analysis content for downstream coal-consuming enterprises to acquire customers. Update cadences vary by document type: spot ledgers are updated daily, quality inspection reports are issued with each production batch, and industry briefings are released on a weekly or monthly cycle. Common marketing documents fall into two categories: structured tables and plain text paragraphs. Structured tables typically include fields such as origin identifier, calorific value index, moisture content, ash content, and price tier. Unified unit standards apply: calorific value is measured in kilocalories per kilogram, price is measured in yuan per metric ton, and moisture and ash content are measured in wet basis weight fraction.
What constraints do these characteristics impose on the document parsing and chunking workflow?
Documents from multiple sources have significant format differences, ranging from editable electronic ledgers to scanned paper quality inspection reports. The parsing process must adapt to these different input formats to support rapid generation of marketing content. Frequently updated spot documents require fast parsing to support real-time marketing quote creation. Fields in structured tables are closely linked. When splitting documents, complete information for the same business unit must be retained. Splitting origin and corresponding price into different chunks will reduce the readability of marketing content. Industry briefings of different cycles have wide variations in content length. Chunking must balance content integrity and retrieval granularity. Chunks that are too short or too long will harm the accurate recall of subsequent marketing content. Batch marketing content often includes multiple thermal coal data documents in different formats. A unified parsing logic must be used to ensure consistent output formats and improve the efficiency of marketing material creation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Adapts to the parsing duration of conventional thermal coal documents, avoiding timeout interruptions during batch processing of marketing materials |
PDF_PARSER_MODE | marker high-precision mode | Addresses professional OCR requirements for scanned quality inspection reports, improving recognition accuracy for technical terms and table structures |
CHUNK_SIZE | 800–1000 characters | Matches the length of typical business units in thermal coal documents, avoiding splitting complete marketing information across fields |
CHUNK_OVERLAP_RATE | 10%–15% | Retains field association information across chunks, reducing the probability of losing context during marketing content retrieval |
EXCEL_PARSE_HEADER_ROW | Row 1 | Adapts to the general format of thermal coal ledger documents, ensuring correct binding between field names and corresponding data |
OCR_ENABLED | Enabled | Covers text extraction requirements for scanned quality inspection reports, avoiding parsing failures due to lack of native text content |
The parameter values provided on this page are general recommendations used as a starting point 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 test on your own samples before finalizing settings.
Three Common Configuration Errors
- When uploading a scanned thermal coal quality inspection report PDF, the interface returns an
OCR Errormessage, and logs show OCR recognition failure. The cause is thatmarker high-precision modeis not enabled. Using only the basic OCR mode cannot recognize technical terms and table structures in the document. - In simple mode of version 4.8.9, after uploading a short thermal coal ledger Excel file, the large language model does not trigger file parsing, and the returned result contains no document content. The cause is that the system's default document parsing trigger threshold is set too high, and the short marketing ledger is not identified as a valid parsing target.
- In a private deployment environment, after uploading multiple thermal coal briefing PDFs, FastGPT returns a
504 Gateway Timeouterror, but the marker parsing service logs show that parsing was successful. The cause is that the value set forPARSE_FILE_TIMEOUT_SECONDSis less than the actual time required for the marker service to complete parsing, causing the gateway to terminate the request early.
How to Verify Correct Configuration
- Upload a standard thermal coal spot ledger Excel file, check whether the parsed data fields match the original document, with no misalignment or missing content.
- Upload a scanned thermal coal quality inspection report PDF, verify that the parsed result contains complete fields such as calorific value, moisture content, and price, with no garbled characters or missing recognition.
- Adjust the
CHUNK_SIZEparameter, then batch upload multiple thermal coal documents of different lengths, check whether the chunking results retain complete business unit content. - View the system operation logs, confirm that the timeout setting for parsing requests matches the actual parsing duration, with no timeout-related errors.
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.