What the data for this category looks like
The data for education service marketing content primarily comes from internal course documents, enrollment campaign plans, annual operation reports, structured student data spreadsheets, and marketing material pages from internal collaboration platforms. Update frequency aligns with marketing milestones. Concentrated updates occur before semester enrollment drives and annual campaigns. Document types include multi-page PDF reports, nested collaboration pages, and structured Excel spreadsheets. Most fields contain numerical values with clear units, such as course duration, tuition fees, and enrollment scale. Some documents have multi-level title nesting structures.
Constraints imposed on document parsing and chunking by these characteristics
The presence of long documents requires the parsing process to support large file handling to avoid timeout interruptions. Nested document structures require parsed output to retain parent-child block associations. Otherwise, the hierarchical logic of course systems and campaign workflows will be lost. Fixed header formats in structured Excel spreadsheets require the parsing process to accurately identify header rows, preventing field scattering or misalignment. Internal collaboration platform use cases require the parsing process to support access to internal network addresses. Otherwise, internal marketing material content cannot be extracted. The concentrated update demand tied to marketing milestones requires parsing tasks to respond quickly, avoiding delays to marketing preparation progress.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Adapts to long paragraphs and nested title structures in education documents, ensuring complete context after chunking |
chunkOverlap | 100–150 characters | Retains overlapping content between adjacent chunks, preventing nested titles and body text from being truncated and separated |
enable_parent_child_chunk | Enabled | Matches the multi-level title structure of education documents, retaining parent-child association logic for course modules and campaign segments |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports upload and parsing of PDF reports with approximately 1000 pages per file |
PARSE_EXCEL_HEADER_ROW | Rows 1–2 | Adapts to the header layout of education-focused Excel spreadsheets, enabling accurate identification of fields and corresponding content |
PARSE_TIMEOUT | 1200 seconds | Meets the time requirements for long document parsing, preventing mid-task interruptions for large files |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Parsing internal Confluence pages returns no valid content. The cause is failure to configure internal network access permissions or add the target domain to the parsing whitelist.
- Parsed chunk count for large-page PDF reports is abnormal. The cause is failure to enable the parent-child chunk function, or setting
maxChunkSizetoo small, which forces truncation of core content. - Uploaded Excel documents return format error prompts. The cause is failure to correctly specify the
PARSE_EXCEL_HEADER_ROWparameter, leading to incorrect header identification and subsequent field matching confusion.
How to Confirm Proper Configuration
- Upload a test 500-page PDF report, check the completion status of the parsing task, confirm it finishes within the time set by
PARSE_TIMEOUT. - Upload a structured Excel enrollment spreadsheet, verify that the extracted text content includes preset headers and corresponding data.
- Review the hierarchical structure of chunk results, confirm that parent-child block associations match the original title hierarchy of the document.
- Test parsing a marketing page from the internal collaboration platform, confirm that the returned text content matches the actual displayed marketing information.
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.