What the data for this category looks like
Data sources for coal chemical marketing content include investment brochures and product promotional materials produced by corporate marketing departments, industry updates released by industry associations, technical response documents for bidding projects, and internal marketing review documents. Update cycles vary by document type:
- Industrial policy documents are updated irregularly
- Product promotional materials are updated every 6 months to 1 year to reflect changes in capacity and pricing
- Structured marketing ledger data is updated daily
Documents are divided into two categories: structured documents, which are table files with fixed fields, and unstructured documents, which are multi-chapter PDF or Word documents containing process parameters, capacity data, cooperation cases, and similar content. Common fields include coal type, project capacity, gasification efficiency, and marketing coverage area. Corresponding units are none, ten thousand tons/year, kg standard coal/cubic meter, and square kilometer.
What constraints these characteristics impose on document parsing and chunking
Structured CSV files contain mixed field types. Preserve field mappings during parsing to avoid misaligned splits that cause business data errors. Unstructured long documents have nested chapters and technical jargon. Fixed-length chunking risks splitting process parameters from their associated business data. Prioritize splitting by chapter hierarchy instead. Frequently updated marketing ledger data requires incremental parsing to reduce resource consumption from repeated processing. Cross-page customer cases in marketing content need context binding. Ensure case background, solution, and results are included in the same chunk to avoid losing business connections.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Coal chemical documents contain long process parameters and business data. This range preserves the integrity of technical terms and business associations, avoiding splitting cross-disciplinary content |
chunkOverlap | 100–150 characters | Coal chemical documents have many technical terms and cross-chapter business logic. The overlap interval preserves context coherence |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Unstructured coal chemical documents (such as feasibility study reports) have long lengths. Sufficient timeout time is required to complete full parsing |
csv_skip_header | Enabled | The first row of coal chemical marketing CSV files contains fixed business fields. Retaining the header clarifies data mapping |
enable_chunk_by_section | Enabled | Unstructured coal chemical documents have clear chapter titles (such as product solutions, process parameters). Chunking by chapter preserves business module integrity |
UPLOAD_FILE_MAX_SIZE | 500 MB | Single coal chemical marketing documents usually do not exceed this size, preventing exceptions during upload |
The parameter values provided on this page are common starting points for configuration setup. Actual values will vary based on material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test against your own samples before finalizing settings.
Three common errors
- Phenomenon: After upgrading the version, parsing the same coal chemical marketing CSV file returns an error with
400 Bad Requeststatus code. Cause: The default value of thecsv_delimiterparameter was changed to tab in the new version, and the original configuration used comma-separated CSV files that cannot match the format correctly. - Phenomenon: After uploading a coal chemical PDF document, the chunking result lacks complete process parameter paragraphs. Cause: The
enable_chunk_by_sectionconfiguration was not enabled. Fixed-length chunking truncates cross-page technical content. - Phenomenon: When configuring a large model node, the passed document content is empty or only contains partial parsed text. Cause: The system default document parsing process was not disabled. The directly passed original binary file was not recognized by the large model.
How to confirm the configuration is correct
- Upload a typical coal chemical marketing CSV file, check the parsed field display, and confirm all preset business fields are correctly identified.
- Upload an unstructured coal chemical document, check the chunking preview result, and confirm technical terms and associated business data are not split apart.
- Trigger a parsing task, check the task run logs, and confirm no timeout or format error prompts appear.
- Configure a node that directly passes original files, upload the target document, and verify the large model can read the complete original file 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.