What the data for this category looks like
Data sources for education service intelligent due diligence reports include school qualification filing documents, course development documents, operation ledgers, teacher qualification certificates, compliance self-inspection reports, and other materials from educational institutions. Update cycles vary by document type: qualification documents are updated annually, operation ledgers are updated monthly or quarterly, and course documents are updated each semester.
Document types include structured tables, long-form text descriptions, scanned qualification files, and more. Fields cover institution numbers, teacher qualification levels, course hours, revenue details, and others. Units include hours, ten thousand yuan, headcount, and others.
What constraints these characteristics impose on document parsing and chunking
Structured tables account for a high share of content. Retain the association between cells and their corresponding content. This prevents loss of field information when splitting text.
Long-form text such as course descriptions and compliance reports must be chunked by semantic paragraphs. Forced segmentation breaks the logical coherence of content.
Scanned qualification files and teacher certification images are common. Enable high-precision text recognition. Without this, embedded text in images cannot be extracted.
Documents with different update cycles need matching parsing trigger rules. This ensures the latest version content is processed first.
Fields include specific units such as hours and ten thousand yuan. Retain the binding relationship between fields and units. This avoids information loss during vectorization.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_OCR_ENABLE | Enabled | Due diligence reports contain many scanned qualification files and teacher certification images. This setting enables recognition of embedded text in images |
PARSE_TABLE_PRESERVE_STRUCT | Enabled | Structured tables account for a high share of content. This setting retains the association between cells and their corresponding content to avoid field information loss |
CHUNK_SIZE | 800–1200 characters | Long-form text such as course descriptions and compliance reports uses semantic chunking. This range fits most education service document logical paragraph lengths |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large operation ledgers and multi-page course syllabus documents take longer to parse. This setting prevents parsing from timing out |
CHUNK_OVERLAP_RATE | 10% | This ensures semantic coherence for long-text chunks. It prevents key information from being split at adjacent chunk boundaries |
UPLOAD_FILE_ALLOWED_EXT | pdf,docx,xlsx,image/png,image/jpeg | This covers common education service due diligence document types: qualification filings, office documents, scanned files, and more |
Three common misconfigurations
- After uploading an Excel-format operation ledger, field and content correspondence in the vectorization index becomes chaotic. The root cause is that the
PARSE_TABLE_PRESERVE_STRUCTconfiguration is not enabled. This splits table cell content into unconnected scattered text. - After uploading a scanned school qualification PDF, the parsing result has no valid text content. The root cause is that the
PARSE_OCR_ENABLEconfiguration is not enabled. This skips the image text recognition process. - After uploading a document via API, the generated chunk length does not match expectations. The root cause is that
CHUNK_SIZEis not specified in the API request parameters. The global default configuration is used, which does not match the document length.
How to confirm configurations are set correctly
- Upload a scanned qualification file. Check the parsed text content to confirm embedded image text is successfully recognized.
- Upload an Excel-format teacher roster. Check the chunked content to confirm the association between cells and their corresponding content is intact.
- When uploading a document via API, check the request parameters for custom chunking and parsing configuration items. Confirm the parameters are submitted correctly.
- View the chunk list in the knowledge base. Verify that chunk lengths match the preset configuration.
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, and it is recommended to test on your own samples before finalizing settings.
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.