What the Data for This Category Looks Like
Education service investment research data mainly comes from public industry research reports, institutional public filing materials, education enterprise financial reports, official policy documents, course teaching evaluation reports, and similar sources. Update cycles vary by type: policy documents are updated quarterly or annually, enterprise financial reports are released quarterly, industry research reports are updated irregularly, and course materials are iterated monthly. Document formats include PDF mixed text-image reports, Excel structured statistical tables, Word teaching plans, image-based policy announcements, and more. These documents contain specific fields such as enrollment numbers, tuition standards, student-teacher ratios, and policy document numbers, paired with dedicated units.
What Constraints Do These Characteristics Impose on the "Document Parsing and Chunking" Link
Education service investment research data has diverse sources and complex formats, requiring the parsing link to support complete parsing of multiple file types and avoid missing embedded images, tables, and other resources. The update cycles of different documents vary widely, and some single documents have a large number of pages and file sizes. This requires the chunking link to adapt to long document processing and avoid parsing timeouts or memory overflow. Documents contain specific fields and dedicated units, requiring the chunking process to retain data relevance and avoid arbitrary cutting that causes misalignment between fields and units. The presence of large amounts of structured statistical data requires retaining the original table structure after parsing to ensure the accuracy of subsequent retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Education service investment research documents often include multi-page research reports and batch statistical tables, with large individual file sizes, so large file upload requirements must be supported |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Long document parsing includes multi-page content and complex tables, requiring longer processing time to complete full parsing |
chunk_size | 800–1200 characters | Education investment research documents contain a large number of professional terms and long sentences. Excessively large chunks will lose context relevance, while excessively small chunks will destroy logical coherence |
chunk_overlap | 100–150 characters | Contextual connection between chunks must be retained to prevent key information from breaking at chunk boundaries |
enable_image_parse | Enabled | Education investment research documents often include course screenshots, policy charts and other mixed text-image content, so associated image and text information must be extracted |
excel_table_parse_mode | Retain full structure | Educational data is mostly structured statistical tables, so the correspondence between fields and units must be retained to avoid data misalignment after parsing |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues need to be analyzed on a case-by-case basis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Parsing tasks only utilize a single 3090 GPU, and the second GPU resource remains unoccupied. Cause: Multi-GPU parallel scheduling parameters are not configured, and only single-card parsing mode is enabled.
- Phenomenon: Embedded image associated text or image placeholders are not included in PDF parsing results. Cause: The
enable_image_parseconfiguration item is not enabled, and the image-text association parsing logic is not triggered. - Phenomenon: Embedded images in Excel files are not extracted, and statistical description text in tables has field misalignment. Cause:
excel_table_parse_modeis not set to retain embedded resources, and only plain text table content is parsed.
How to Verify Proper Configuration
- Upload a single large-volume education investment research document, check hardware resource monitoring during the parsing process, confirm that multiple GPUs are scheduled for use.
- Upload an Excel file containing embedded images and tables, check the parsed results, confirm that the table structure and embedded images are fully retained.
- Upload a long document containing professional terms, check the chunked text fragments, confirm that key contextual connections are retained in the chunks.
- Check the parsing task run logs, confirm that no timeout errors are triggered.
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.