What the Data for This Category Looks Like
Education service investment research data sources include public disclosure documents from education administrative departments, quarterly research reports released by industry associations, publicly available operational annual reports from colleges and educational institutions, course listing and delisting records from online education platforms, and interview minutes from special surveys. Document formats primarily consist of long-text industry research reports, structured policy clauses, course detail page content, and qualification certification documents. Fields include policy document numbers, institutional qualification numbers, course unit prices, enrollment counts, and more. Units involve person-times, ten thousand yuan, academic semesters, and others. Update frequencies vary: policy files update in real time upon release, industry research reports update quarterly, and operational data syncs daily.
Constraints Imposed on Knowledge Base Retrieval and Recall
Education service investment research data contains both structured fields and long-text content, with notable differences in update frequencies. Structured fields such as policy document numbers and qualification numbers require retrieval to balance semantic matching and precise keyword recall, to prevent matching irrelevant policy clauses. Long-text research reports are mostly coherent industry analyses. Contextual connections must be retained during segmentation to avoid losing core logic after splitting. Data sources with different update frequencies require differentiated indexing strategies to ensure the timeliness of retrieved content. Automatic removal rules must be configured for delisted courses and revoked policies, to avoid recalling expired or invalid information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall count | Top 8-12 entries | Education service investment research data includes multiple segmented fields, requiring a sufficient candidate set to cover matching results across different dimensions, avoiding missing key policy or research report content |
Similarity threshold | 0.75-0.85 | Structured fields such as policy document numbers require high matching accuracy, while semantic matching for long-text research reports needs to balance recall completeness. This interval balances precision and recall rate |
Chunk size | 1000-1500 characters | Most education service research reports are coherent industry analysis content. This segmentation length preserves complete logic of individual chapters, avoiding contextual breaks after splitting |
Incremental sync interval | Every 6 hours | Education policies update in real time upon release, while industry research reports release quarterly. This interval balances timeliness and server load |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single education research report files have large volume. A longer timeout duration ensures complete parsing, avoiding mid-process interruptions |
Rerank result count | Top 5 entries | Investment research decisions require precise core content. Limiting the number of returned results avoids redundant information interfering with retrieval results |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Duplicate policy documents or research report fragments appear in retrieval results. Cause: The deduplication switch of the knowledge base index merging component is not enabled, causing the same content to be indexed and recalled multiple times.
- Symptom:
PARSE_FILE_TIMEOUTerror occurs when parsing large-volume education research reports. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is too short to complete long-text parsing. - Symptom: The number of returned retrieval results is far lower than expected. Cause: The similarity threshold is set too high, and no keyword matching strategy is combined, resulting in failure to effectively recall structured fields such as policy document numbers.
How to Confirm Correct Configuration
- Submit a retrieval request that includes structured fields, check whether the returned results include matching policy document numbers or qualification numbers, and adjust keyword matching rules to meet expectations.
- Upload a delisted course document, verify that the content does not appear in retrieval results, confirming that the expired content removal rule is effective.
- Check the index synchronization log, verify that the incremental synchronization task executes at the preset interval, confirming that content from updated data sources is indexed in a timely manner.
- Split a long-text research report, check whether the segmented content retains complete chapter logic, confirming that the segmentation configuration matches the document characteristics.
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.