What the data for this category looks like
Data sources for education service intelligent due diligence reports include school qualification filing documents, teacher resume files, student service ledgers, teaching research plan documents, annual financial statements, and other materials from educational service institutions. Update frequencies vary: qualification documents are updated annually, teacher position changes are updated in real time with adjustments, and student service ledgers are updated monthly. Document structures include structured paper qualification scans, semi-structured business ledger tables, and unstructured teaching research and service records. Fields include school operation permit number, teacher years of teaching, teaching research cycle, student service cycle, and others, with each field corresponding to different units of measurement.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data formats require the parsing module to support mixed format processing. OCR parameters for scans and structured extraction parameters for tables must be configured. Differences in data update frequencies require configuring differentiated sync trigger rules, distinguishing between annually updated qualification documents and frequently updated business ledgers to avoid invalid syncs. Multi-unit field types require configuring unit standardization rules to ensure model-extracted field results use unified formats and avoid unit confusion. Some documents involve student privacy information, so sensitive information filtering parameters must be configured to ensure extracted content complies with compliance requirements.
How to determine configuration values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapt to the parsing duration of education service qualification scans and ledger tables, avoid timeout interruptions for large files |
chunkSize | 800–1200 characters | Cover the paragraph granularity of education service documents, balance context coherence and extraction accuracy |
OCR_SWITCH | Enabled | Process paper school qualification and teacher certification scans from education service institutions, extract editable text content |
FIELD_UNIT_MAPPING | Map by preset units per field | Standardize extraction results for multi-unit fields such as teacher years of teaching and teaching research cycle, avoid unit confusion |
RETRIEVAL_MODEL_TYPE | Select a suitable open-source index model based on education service data volume | Adapt to the multi-source heterogeneous data indexing needs of education service due diligence reports, balance retrieval accuracy and resource usage |
INCREMENTAL_SYNC_INTERVAL | 1 year for qualification documents, 30 days for business ledgers | Match the update frequency of education service data, reduce the number of invalid syncs |
FUNCTION_CALL_ENABLE | Enabled | Support structured extraction and compliance verification of due diligence report fields |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and testing on local samples is recommended before finalizing settings.
Three common configuration errors
- Phenomenon: An
invalid function definitionerror is returned when function call is invoked, or the structured extraction result is empty. Cause: Function call support in model access configuration is not enabled, or unit mapping rules for education service fields are not configured, causing the model to fail to recognize the unit formats of fields such as teacher years of teaching and cycle, triggering parameter verification failure. - Phenomenon: A locally deployed large model triggers a
context window overflowerror when parsing education service documents. Cause: ThechunkSizeparameter is not adjusted, and the input document fragment is too long, exceeding the model context window limit, causing parsing interruption. - Phenomenon: Retrieval results have low matching degree with target due diligence fields, or recalled results are redundant. Cause: An index model adapted to the characteristics of education service data is not selected, resulting in insufficient retrieval accuracy.
How to confirm the configuration is complete
- Upload a single education service qualification scan, verify whether the parsing result covers preset key fields, and confirm that the
OCR_SWITCHconfiguration takes effect. - Initiate an incremental sync task, verify whether the sync cycle matches the update frequency of the corresponding data type, and confirm that the
INCREMENTAL_SYNC_INTERVALconfiguration is reasonable. - Initiate a field extraction test, input the structured extraction requirements for education service due diligence, and confirm that the returned result contains standardized unit information without format errors.
- Check the model access logs, confirm that there are no parsing timeout related errors, and verify that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration adapts to the actual document size.
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.