What the data for this category looks like
Data sources for education service intelligent due diligence reports include school operation qualification documents submitted by education institutions, teacher filing materials, course syllabus documents, fee disclosure ledgers, regulatory agency public information, and user feedback records. Update rhythms vary across data types: school operation qualifications are updated through annual inspections, course syllabi are adjusted each semester, and user feedback records are added in real time. Document structures contain both structured fields (such as unified social credit code, school operation permit number, number of teachers) and unstructured text (such as course details, complaint descriptions). Some fields have clear units: for example, single-course fee standards use yuan/class hour as the unit, and school operation permit validity periods are marked in date format.
What constraints these characteristics impose on workflow orchestration
First, the mix of structured and unstructured data requires workflows to configure multi-format parsing nodes, and distinguish between field extraction and full-text retrieval steps. Second, differences in data update rhythms require workflows to embed scheduled synchronization nodes. These nodes separately handle annual qualification updates, semester course updates, and real-time feedback pulling. Third, fields with clear units require workflows to add unit verification nodes. This prevents extraction errors where fee standards do not match their associated units. Fourth, format differences across multiple source documents require configuring a whitelist to restrict parsable file types. This stops invalid format imports from interfering with workflow execution.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 10-15 entries | Education due diligence reports need to cover multi-dimensional data. Excessive recall will cause total token count to exceed limits |
similarity threshold | 0.75-0.85 | Text similarity requirements for education qualification materials are high. A low threshold will introduce irrelevant regulatory public information |
segment length | 800-1200 characters | Education due diligence documents are mostly long texts. This length balances context integrity and token usage |
workflow timeout | 300-600 seconds | Multiple qualification documents and ledgers need parsing. Long document parsing requires sufficient execution time |
allowed file types | ["pdf", "xlsx", "doc", "html"] | Covers commonly used document formats for education due diligence. Prevents invalid files from triggering parsing failures |
export format | json | Facilitates cross-environment reuse of workflow configurations, and adapts to subsequent batch deployment needs |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After importing an external workflow JSON file, the text processing module displays normally, but the module option does not appear when creating a local workflow. Cause: The advanced plugin market is not enabled locally, or the corresponding text processing plugin is not installed.
- Symptom: Cannot find the export entry for workflow to JSON format, or the exported file format does not match expectations. Cause: Did not switch to the dedicated export menu in the workflow editing page, or mistakenly selected a non-JSON export format.
- Symptom: After setting
search reference limitto 2000, short text answers still exceed 3000 tokens. Cause: Segment length and recall count are not restricted at the same time. Total token count is calculated by multiplying the number of segments and the length of a single segment. Single reference length is not included in this calculation.
How to confirm configurations are complete
- Upload a PDF of an education institution's school operation qualification document. Check if the workflow automatically triggers parsing and extracts the specified structured fields.
- View workflow execution logs. Confirm that the
recall countandsimilarity thresholdconfigurations have taken effect, and the returned knowledge base results fall within the expected range. - Export the workflow in JSON format. Verify that the exported file contains all configured nodes and parameter information.
- Simulate a batch upload scenario with multiple documents. Confirm that the workflow completes execution within the configured timeout period, with no abnormal interruptions.
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.