What the data for this category looks like
Education service investment research data primarily comes from official education policy documents, subject teaching and research reports, institutional enrollment statistics, curriculum system documents, teacher qualification standards, and similar materials. Update frequency varies by data type. Policy documents have longer update cycles. Teaching and research reports and real-time course data update more frequently. Document structures include long text interpretations, structured statistical tables, semi-structured module data, and more. Fields include educational stage, subject, class hours, enrollment numbers, credit hours, and similar items. Units include basic measurement units such as hours, people, minutes, and others.
What constraints do these characteristics impose on database and operations
Education service investment research data has scattered sources and diverse types. This requires databases to support access to and unified storage of multi-source heterogeneous data. The large difference in update frequencies across data types requires an operations mechanism that combines incremental and full updates. The diversity of document structures requires databases to support mixed storage of unstructured text, structured tables, and semi-structured data. The diversity of fields and units requires the establishment of unified field mapping rules to avoid data confusion. Additionally, education service scenarios may involve batch imports of large documents or multiple files. This places additional constraints on database concurrent processing capabilities and large file parsing capabilities.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Education service investment research documents are mostly long-text policy interpretations and full textbook analyses, with longer parsing times than general scenarios |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading batch teaching research materials and complete curriculum system documents, adapting to large file import needs for education services |
MONGO_CONNECTION_TIMEOUT_MS | 30000 milliseconds | Education data storage nodes may cover multi-region data sources, requiring extended connection wait times to avoid timeout errors |
CSV_IMPORT_MAX_COLUMNS | 50 columns | Education investment research enrollment statistics and curriculum module data include multi-dimensional fields, requiring adaptation to wide table imports |
RECALL_TOP_K | 8-12 entries | Education service investment research needs to cover multi-dimensional content such as subject knowledge points, policies, enrollment data, etc. Reasonable recall quantity ensures information completeness |
MARKER_PARSE_MAX_SIZE | 500 MB | Adapts to parsing needs for large PDF textbooks in education services, avoiding single-file parsing timeouts |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. 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
- Phenomenon: A connection timeout error occurs when starting a MongoDB connection. Cause: The
MONGO_CONNECTION_TIMEOUT_MSparameter is not adjusted for cross-region nodes storing education data. The default value is too short, causing the connection to time out before it is fully established. - Phenomenon: Importing Excel format files fails, and CSV files can only recognize two columns of data. Cause: The
CSV_IMPORT_MAX_COLUMNSparameter is not adjusted to a value suitable for education wide tables. The default column limit prevents multi-dimensional education data from being imported completely. - Phenomenon: A timeout error "timeout of 360000ms exceeded" is prompted when parsing PDF files larger than 10 MB using marker. Cause: The
PARSE_FILE_TIMEOUT_SECONDSandMARKER_PARSE_MAX_SIZEparameters are not adjusted. The default timeout and file size limits cannot adapt to large document parsing needs for education services.
How to confirm the configuration is complete
- Upload a single education document that does not exceed the preset maximum size. Check the upload progress and parsing logs to confirm there are no file size limit exceeded errors.
- Import CSV format education statistics data that includes multiple columns of fields. Check the field integrity of the import results to confirm there is no field loss caused by column limit restrictions.
- Start the MongoDB service and connect to the configured address. Check the connection logs to confirm there are no connection timeout errors.
- Trigger the database query step of the workflow. View the citation sources of the AI response to confirm that the matched database content has been associated.
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.