What the Data for This Category Looks Like
The data comes from internal marketing material libraries of financial institutions, marketing plan documents delivered by partners, and scanned paper originals from offline events. Update cadence adjusts based on individual marketing campaign cycles, with concentrated updates of campaign materials during single events and on-demand supplements on a daily basis. Document structures are mixed, including plain-text activity descriptions, product manuals with embedded comparison tables, annotated promotional poster originals, and formats such as docx, pdf, and scanned files. Fields include activity ID, touch channel, effective period, target customer group, etc. Date fields use standard Gregorian calendar formats, and amount fields use Chinese Yuan as the unit.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking?
Mixed document structures require the parsing module to support recognition of plain text, embedded tables, and scanned files simultaneously, to avoid missing non-text content. Concentrated bulk updates of materials create scenarios with large single upload volumes, requiring resource scheduling adapted for batch parsing. Document structures with multiple associated fields require retaining the binding relationship between fields and their corresponding content during chunking, to avoid splitting cross-field business information. Scanned file originals require an additional OCR processing workflow to ensure handwritten or printed promotional content can be fully extracted. Most embedded tables are product comparison content, requiring retention of the complete table structure during chunking to avoid splitting tables into scattered paragraphs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapt to large document parsing for batch uploads, avoid single-file timeout interruptions |
maxChunkSize | 800–1200 characters | Match the average length of paragraphs and tables in marketing documents, retain business information integrity |
ENABLE_TABLE_PARSE | Enabled | Retain embedded table structures, avoid splitting product comparison content |
OCR_ENABLED | Auto-trigger based on document type | Adapt to scanned marketing originals, enable OCR only for non-editable documents |
UPLOAD_BATCH_MAX_COUNT | 50 | Match batch upload scenarios for concentrated material updates, avoid overload from single submissions |
CHUNK_KEEP_FIELD_RELATION | Enabled | Bind business fields and their corresponding content within documents, ensure complete information association after chunking |
The parameter values provided on this page are common starting points for configuration setup. 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
- Symptom: After uploading a docx-format marketing document, the parsing status shows failure, and the log returns the
PARSE_FAILEDerror code. Cause: Some docx documents use non-standard embedded styles, and the parsing module cannot recognize the text blocks bound to these styles. - Symptom: After uploading a product comparison table with merged cells in version 4.9.6, the chunking result splits the table into scattered paragraphs, with no complete table structure. Cause: The table parsing logic in this version has poor adaptation for merged cells, and fails to retain the row and column associations of the table.
- Symptom: When enabling GPU-accelerated OCR parsing, the container reports a
CUDA_OUT_OF_MEMORYerror, and cannot detect the installed GPU driver. Cause: GPU devices and driver directories were not mounted during docker deployment, and the CUDA version does not match the dependency version built into the image.
How to Confirm Proper Configuration
- Upload one typical marketing document that includes scanned files and embedded tables, check the text and structural integrity of the parsing result, and confirm that OCR functionality automatically triggers for non-editable document scenarios.
- Batch upload multiple identical types of marketing documents, observe the execution status of the task queue, and confirm that the batch upload upper limit configuration does not cause submission failures.
- Export the chunked fragment list, verify whether each fragment retains the associated business fields and complete table structure, and confirm that the field association configuration takes effect.
- Check the container runtime logs, confirm that GPU acceleration-related error messages have been eliminated, and device detection and video memory allocation are functioning normally.
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.